Time-varying Interactions Research Articles

Executive function, limbic circuit dynamics and repetitive and restricted behaviors in children with autism spectrum disorder

ObjectiveRepetitive and restricted behaviors (RRBs) are a core symptom of autism spectrum disorder (ASD), but effective treatment approaches are still lacking. Executive function (EF) has been identified as a promising target, as research increasingly shows a link between EF deficits and the occurrence of RRBs. However, the neural mechanisms that connect the two remain unclear. Since the orbitofrontal cortex (OFC) plays a role in both EF and RRBs, its functional connectivity dynamics could offer valuable insights into this relationship.MethodsThis study analyzed data from the Autism Brain Imaging Data Exchange (ABIDE) II database to explore brain function in 93 boys with ASD and 110 typically developing (TD) boys. Time-varying functional connectivity was analyzed between eight OFC subregions and other brain areas. By employing linear regression, the study assessed how atypical connectivity dynamics and EF influence RRBs. Additionally, mediation analysis with bootstrapping was used to determine how EF mediates the relationship between atypical connectivity and RRBs.ResultsWe found significant differences in the variance of FC between ASD and TD groups, specifically in the OFC subregion in L-prefrontal and the left amygdala (t = 5.00, FDR q < 0.01). Regression analyses revealed that increased variance of this FC and EF significantly impacted RRBs, with inhibition, emotional control, and monitor showing strong associations (standardized β = 0.60 to 0.62, p < 0.01), which also had significant indirect effects on the relationship between the above dynamic FC and RRBs, which accounted for 59% of the total effect.ConclusionThis study highlights the critical role of EFs as a key mechanism in addressing RRBs in ASD. Specifically, it points out that EFs mediate the influence of atypical time-varying interactions within the OFC-amygdala circuit on RRBs.

Mapping and assessment of harmonic voltage levels for railway traction supply stations in Sweden

Assessing harmonic distortion measurements in the electric railway power systems (ERPS) requires evaluating the time-varying behavior, interactions, and performance in different time scales. This paper aims to map and assess harmonic voltage levels in 13 traction converter stations for the Swedish railway power supply system, with findings that have direct practical implications. For that, measurements from the public and railway grid sides for 69 weeks are analyzed. Statistical values are explored for the harmonic voltage spectra and total harmonic distortion (THD) variation. The public grid side measurements are investigated using 95th percentile weekly values, and performance is evaluated by comparing the recommended planning levels of IEC 61,000–3–6. The intraweek variation complements the information about the time-varying behavior of the THD. The 95th percentile, minimum daily values, and intraday variation are explored to understand the time-based behavior since there are no reference limits from standards for comparison, looking to the railway grid side. Extended analysis is placed on the railway grid side to highlight some aspects of measurement time-aggregation based on 10-min values, and time-series trend analysis is used to confirm traffic planning impact. Discussion and findings regarding railway operation, the technology deployed at the traction converter station, time-varying behavior, traffic planning impact, measurement time-aggregation, and spectra patterns were presented.

Online decoupling of the time-varying longitudinal feedback loops for improved performance in Advanced Virgo Plus*

Abstract Gravitational Wave detectors require low-noise sensors combined with high-performance feedback loops to maximize the detector sensitivity in the low-frequency detection range. Some feedback loops in the detector are strongly coupled and their coupling varies over time, which is inherent to the optical configuration and the optical readout scheme used to obtain error signals for the feedback loops. This paper presents a control-based approach to deal with the time-varying interaction among three of the longitudinal degrees of freedom in the Advanced Virgo Plus detector, using the pre-existing infrastructure of the detector. An intuitive Multiple-Input Multiple-Output stability criterion is presented that illustrates how the time-varying coupling affects the stability of the feedback loops, as well as a method that identifies the coupling online and updates a decoupling matrix accordingly. Experiment results of the proposed method on the Advanced Virgo Plus detector illustrate continuous minimization of the coupling over time. Using the derived stability criterion, it is furthermore shown that the coupling is sufficiently minimized such that the interaction terms can be neglected in the control design, resulting in an improved controller performance.

The time-varying interaction of northbound capital flows and stock market performance in China

This study employs the SV-TVP-SVAR model to investigate the dynamic interactions between northbound capital and stock market performance in China, highlighting the time-varying statistical relationships. The findings reveal that the influence of market returns on northbound capital is predominantly short-term, exhibiting negative feedback, which helps stabilize the market during periods of extreme volatility. However, during market reversals, northbound capital shows positive feedback, correlating with improving stock returns. Regarding predictability, while northbound capital provides some predictive power for stock returns, this influence diminishes quickly. The study further notes that retail investors tend to imitate the high-frequency trading patterns of northbound capital.

Bitcoin: a Ponzi scheme or an emerging inflation-fighting asset?

Under the dual impact of the COVID-19 pandemic and the Russian-Ukrainian conflict, the excessive stimulation of monetary policy continuously pushes up global inflation (INF). Therefore, this article explores whether Bitcoin can serve as a safe haven for INF. We apply the rolling-window Granger causality test to solve the issue of parameter instability in vector autoregression (VAR) systems and investigate the time-varying interaction between INF and Bitcoin price (BP). The negative influence of INF on BP means a high inflation shock causes BP to decline, indicating that Bitcoin cannot be a safe asset against INF. This is because investors have decreased their willingness to hold Bitcoin under the high INF expectations and cause BP to fall. This finding is not supported by the Intertemporal Capital Asset Pricing Model, emphasising that INF positively impacts BP. Conversely, BP has positive and negative impacts on INF. The positive effect highlights the effectiveness of Bitcoin in predicting INF fluctuations, but economic factors could undermine this effectiveness. In the context of economic stagnation and market turmoil, investors can adjust their portfolio investments based on Bitcoin. The government should utilise the trend of BP to regulate the dynamics of INF to reduce uncertainty in the financial system.

Temporal interaction and its role in the evolution of cooperation.

This research investigates the impact of dynamic, time-varying interactions on cooperative behavior in social dilemmas. Traditional research has focused on deterministic rules governing pairwise interactions, yet the impact of interaction frequency and synchronization in groups on cooperation remains underexplored. Addressing this gap, our work introduces two temporal interaction mechanisms to model the stochastic or periodic participation of individuals in public goods games, acknowledging real-life variances due to exogenous temporal factors and geographical time differences. We consider that the interaction state significantly influences both game payoff calculations and the strategy updating process, offering new insights into the emergence and sustainability of cooperation. Our results indicate that maximum game participation frequency is suboptimal under a stochastic interaction mechanism. Instead, an intermediate activation probability maximizes cooperation, suggesting a vital balance between interaction frequency and inactivity security. Furthermore, local synchronization of interactions within specific areas is shown to be beneficial, as time differences hinder the spread of cross-structures but promote the formation of dense cooperative clusters with smoother boundaries. We also note that stronger clustering in networks, larger group sizes, and lower noise increase cooperation. This research contributes to understanding the role of node-based temporality and probabilistic interactions in social dilemmas, offering insights into fostering cooperation.

Thermal Effects on Prestress Loss in Pretensioned Concrete Girders

The fabrication process of pretensioned prestressed concrete (PC) girders involves temperature changes, which affect the effective prestress and mechanical properties of the girders. Currently, there is a lack of a holistic understanding and accurate calculation methods for the prestress variation due to temperature change (PVTC), leading to technical challenges in calculating effective prestress in pretensioned PC girders. This study investigates the PVTC in three stages considering the time-varying interaction between concrete and tendons, proposes a new method to consider the effect of a deviator on the PVTC of a bent tendon, conducts an experimental study to validate the theoretical analysis, and develops measures for reducing the PVTC. The results show that the presented method provides reasonable predictions of PVTC, and the PVTC of the girder with steam curing is up to 80.3 MPa. Based on the presented method, measures for reducing the PVTC are proposed. This study provides new insights into computing the PVTC and improves the design and fabrication of pretensioned PC girders.

Quasi-projective synchronization of non-identical time-varying delayed quaternion-valued neural networks with interaction terms: Direct method

This article investigates the quasi-projective synchronization (QPS) of non-identical quaternion-valued neural networks (QVNNs) with time-varying delays and interaction terms. Due to parameter mismatches between the drive and response systems, complete projective synchronization is not possible. As a result, a novel idea, known as QPS, is explored up to a small error bound. The direct method is used to derive several general criteria to achieve QPS. The Lyapunov stability theory is employed to estimate the error bound. The numerical simulation results are presented graphically for various special cases to demonstrate the synchronization of time-varying delayed QVNNs with mismatched parameters and interaction terms, which confirm the efficiency of the presented research results. The second example showcases the application of QVNNs coupled with associative memory to illustrate its efficacy in accurately restoring the original color image patterns.

Running safety control performance of high-speed trains on bridges under seismic excitation utilizing tuned mass damper

The safety of high-speed trains on bridges during seismic conditions is of paramount concern, especially for high-pier bridges characterized by lower lateral stiffness. The risk of train derailment during earthquakes can increase due to heightened low-frequency excitation. While Tuned Mass Dampers (TMDs) have proven effective in mitigating structural responses, their influence on time-varying vehicle-bridge interactions under earthquakes remains an unexplored area of study. In this research, a 1:10 scale model of a high-speed train-track-bridge coupling system, incorporating a nonlinear wheel-rail interface, was subjected to testing using a four-shaking-table system. This system exhibited good control accuracy and synchronization rates. The experimental results were used to validate the accuracy of the numerical model for the train-track-bridge coupled system under various train speeds. Subsequently, the seismic control performance of TMDs on high-speed trains was assessed through a combination of experimental tests and numerical simulations. Both sets of results, from the experimental and numerical approaches, confirmed the effectiveness of TMDs in controlling acceleration and force-related parameters for both moving and stationary trains. Notably, the control performance is most pronounced when vibrations closely align with the TMD's operating frequency. Additionally, this study delved into the mechanism of train vibration control and conducted a comprehensive parametric investigation, considering factors like pier heights (24 m–50 m), train speeds (0–300 km/h), ground motion intensities (0.04 g–0.21 g), and TMD mass ratios (0.03–0.08). The findings revealed that TMDs have a more significant impact on enhancing the safety index of trains for high-pier bridges characterized by lower frequencies. For instance, in the case of a 50 m pier, the running train's derailment coefficient can be reduced by 34.94 %. However, the control action is narrowband, effectively mitigating train vibrations near the bridge's natural frequency but less effective outside the working frequency range and absolute base acceleration.

Temporal local clustering coefficient uncovers the hidden pattern in temporal networks.

Identifying and extracting topological characteristics are essential for understanding associated structures and organizational principles of complex networks. For temporal networks where the network topology varies with time, beyond the classical patterns such as small-worldness and scale-freeness extracted from the perspective of traditional aggregated static networks, the temporality and simultaneity of time-varying interactions should also be included. Here we extend the traditional analysis on the local clustering coefficient C in static networks and study the dynamical local clustering coefficient of temporal networks. We demonstrate that the temporal local clustering coefficient TC conveys the hidden information of nodes' neighboring connectance when interactions occur at various rhythms. By systematically analyzing various empirical datasets, we find that TC uncovers different interaction patterns in different types of temporal networks. Specifically, we show that TC has a strong positive correlation with C in efficiency-related networks, whereas they are uncorrelated in social activity-related networks. Moreover, TC helps to exclude interference from accidental interactions and reflect the actual clustering properties of network nodes. Our results shed light on the importance of digging into dynamical characteristics to fundamentally understand the underlying temporal structures of real complex systems.

Variation in Time-to-Gender-Affirming Hormone Therapy in the US Active Duty Service Members.

Beginning in July 2016, transgender service members in the US military were allowed to receive gender-affirming medical care, if so desired. This study aimed to evaluate variation in time-to-hormone therapy initiation in active duty Service members after the receipt of a diagnosis indicative of gender dysphoria in the Military Health System. This retrospective cohort study included data from those enrolled in TRICARE Prime between July 2016 and December 2021 and extracted from the Military Health System Data Repository. A population-based sample of US Service members who had an encounter with a relevant International Classification of Diseases 9/10 diagnosis code. Time-to-gender-affirming hormone initiation after diagnosis receipt. A total of 2439 Service members were included (Mage 24y; 62% white, 16% Black; 12% Latine; 65% Junior Enlisted; 37% Army, 29% Navy, 25% Air Force, 7% Marine Corps; 46% first recorded administrative assigned gender marker female). Overall, 41% and 52% initiated gender-affirming hormone therapy within 1 and 3 years of diagnosis, respectively. In the generalized additive model, time-to-gender-affirming hormone initiation was longer for Service members with a first administrative assigned gender marker of male relative to female (P<0.001), and Asian and Pacific Islander (P=0.02) and Black (P=0.047) relative to white Service members. In time-varying interactions, junior enlisted members had longer time-to-initiation, relative to senior enlisted members and junior officers, until about 2-years postinitial diagnosis. The significant variation and documented inequities indicate that institutional data-driven policy modifications are needed to ensure timely access for those desiring care.

Stability prediction method of time-varying real-time hybrid testing system on vehicle-bridge coupled system

In recent years, real-time hybrid testing (RTHT) has been applied for the dynamic testing of high-speed trains running on bridges. A guarantee of stability for the RTHT system is essential to achieve a safe and reliable result. However, the inherent time-varying characteristics of the vehicle-bridge coupled system pose challenges to RTHT stability prediction. This study aims to develop a stability prediction method specifically tailored for time-varying RTHT system. Firstly, the vehicle-bridge coupled RTHT was modelled using a discrete state–space representation with a comprehensive consideration of the time-varying vehicle-bridge interaction and the dynamics of the shaking table. Subsequently, a time-varying stability criterion was derived from the periodic time-varying state matrix, forming the basis for a relative stability prediction method. The validity of the proposed method was confirmed through simulations and experiments employing a single-axle interaction within the vehicle-bridge coupled RTHT system. The coupled system consisted of a quarter-car model and simply supported beams were used as an example to evaluate the stability and accuracy of the time-varying RTHT. The results showed that the stability increased with increasing vehicle speed. Reducing the pure time delay of the shaking table improved both stability and accuracy. Increasing the effective frequency of the shaking table improved the accuracy but may reduce the stability of the RTHT.

Coupled quintessence scalar field model in light of observational datasets

We do a detailed analysis of a well-theoretically motivated interacting dark energy scalar field model with a time-varying interaction term. Using current cosmological datasets from CMB, BAO, Type Ia Supernova, H(z) measurements from cosmic chronometers, angular diameter measurements from Megamasers, growth measurements, and local SH0ES measurements, we found that dark energy component may act differently than a cosmological constant at early times. The observational data also does not disfavor a small interaction between dark energy and dark matter at late times.When using all these datasets in combination, our value of H 0 agrees well with SH0ES results but in 2.5σ tension with Planck results. We also did AIC and BIC analysis, and we found that the cosmological data prefer coupled quintessence model over ΛCDM, although the chi-square per number of degrees of freedom test prefers the latter.

Out-of-equilibrium interactions and collective locomotion of colloidal spheres with squirming of nematoelastic multipoles

Many living and artificial systems show similar emergent behavior and collective motions on different scales, starting from swarms of bacteria to synthetic active particles, herds of mammals, and crowds of people. What all these systems often have in common is that new collective properties like flocking emerge from interactions between individual self-propelled or driven units. Such systems are naturally out-of-equilibrium and propel at the expense of consumed energy. Mimicking nature by making self-propelled or externally driven particles and studying their individual and collective motility may allow for deeper understanding of physical underpinnings behind collective motion of large groups of interacting objects or beings. Here, using a soft matter system of colloids immersed into a liquid crystal, we show that resulting so-called nematoelastic multipoles can be set into a bidirectional locomotion by external oscillating electric fields. Out-of-equilibrium elastic interactions between such colloidal objects lead to collective flock-like behaviors emerging from time-varying elasticity-mediated interactions between externally driven propelling particles. Repulsive elastic interactions in the equilibrium state can be turned into attractive interactions in the out-of-equilibrium state under applied external electric fields. We probe this behavior at different number densities of colloidal particles and show that particles in dense dispersions collectively select the same direction of a coherent motion due to elastic interactions between near neighbors. In our experimentally implemented design, their motion is highly ordered and without clustering or jamming often present in other colloidal transport systems, which is promising for technological and fundamental-science applications, like nano-cargo transport, out-of-equilibrium assembly, and microrobotics.

Mixed localized matter wave solitons in Bose–Einstein condensates with time-varying interatomic interaction and a time-varying complex harmonic trapping potential

We study the generation of mixed localized matter wave solitons in the quasi-one-dimensional Gross–Pitaevskii equation with a complex potential (model equation) that describes the dynamics of Bose–Einstein condensates trapped in an harmonic potential when the loss/gain of condensate atoms is taken into account. Performing a modified lens-type transformation, the integrability condition is derived and the model equation is converted to a Kundu-like nonlinear Schrödinger equation. Through the linear stability analysis, the phenomenon of the modulational instability is analyzed and the criterion of the modulational instability is established. Based on the generalized perturbation (n,N−n)−fold Darboux transformation, new mixed localized wave solutions are presented and used for analyzing the generation of mixed matter-wave solitons in Bose–Einstein condensates with two-body interatomic interactions. We show that mixed localized waves generated with these exact solutions can change from a strong interaction to a weak interaction by choosing the parameters such as the similarity parameters, the spectrum parameter, or the seed solution parameter. Our results show that the spectrum parameter is useful for generating interesting structures which are beneficial to understanding the complex physical phenomena in Bose–Einstein condensates with atomic gain/loss.

Case-Base Neural Network: Survival analysis with time-varying, higher-order interactions

In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures. Using a novel sampling scheme and data augmentation to naturally account for censoring, we construct a feed-forward neural network that includes time as an input. CBNNs predict the probability of an event occurring at a given moment to estimate the full hazard function. We compare the performance of CBNNs to regression and neural network-based survival methods in a simulation and three case studies using two time-dependent metrics. First, we examine performance on a simulation involving a complex baseline hazard and time-varying interactions to assess all methods, with CBNN outperforming competitors. Then, we apply all methods to three real data applications, with CBNNs outperforming the competing models in two studies and showing similar performance in the third. Our results highlight the benefit of combining case-base sampling with deep learning to provide a simple and flexible framework for data-driven modeling of single event survival outcomes that estimates time-varying effects and a complex baseline hazard by design. An R package is available at https://github.com/Jesse-Islam/cbnn.

Climate warming, renewable energy consumption and rare earth market: Evidence from the United States

This study investigates the time-varying interactions among climate warming, renewable energy consumption (REC) and rare earth market using TVP-VAR-SV model. We focus on the stock performance in the rare earth financial market. Our empirical results show that the effects of climate warming and REC on rare earth market changes over time and increases significantly during the COVID-19 pandemic. The REC has short-term positive impact on rare earth market, and the positive impact of climate warming on the rare earth market has a time lag effect. In the long term, climate warming has a significant promotion effect on REC, especially during major events. The short-term positive impact of REC on the rare earth market is prominent after the signing of the Paris Agreement (PA), while its impact shows counter-cyclical fluctuations during the COVID-19 pandemic. The effect of disaggregated REC on rare earth market is obviously heterogeneous. Solar and wind energy consumption are the most important reasons for changes in rare earth market.

WASP-69b’s Escaping Envelope Is Confined to a Tail Extending at Least 7 Rp

Studying the escaping atmospheres of highly irradiated exoplanets is critical for understanding the physical mechanisms that shape the demographics of close-in planets. A number of planetary outflows have been observed as excess H/He absorption during/after transit. Such an outflow has been observed for WASP-69b by multiple groups that disagree on the geometry and velocity structure of the outflow. Here, we report the detection of this planet’s outflow using Keck/NIRSPEC for the first time. We observed the outflow 1.28 hr after egress until the target set, demonstrating the outflow extends at least 5.8 × 105 km or 7.5 Rp This detection is significantly longer than previous observations, which report an outflow extending ∼2.2 planet radii just 1 yr prior. The outflow is blueshifted by −23 km s−1 in the planetary rest frame. We estimate a current mass-loss rate of 1 M ⊕ Gyr−1. Our observations are most consistent with an outflow that is strongly sculpted by ram pressure from the stellar wind. However, potential variability in the outflow could be due to time-varying interactions with the stellar wind or differences in instrumental precision.

Impact of time varying interaction: Formation and annihilation of extreme events in dynamical systems.

This study investigates the emergence of extreme events in two different coupled systems: the FitzHugh-Nagumo neuron model and the forced Liénard system, both based on time-varying interactions. The time-varying coupling function between the systems determines the duration and frequency of their interaction. Extreme events in the coupled system arise as a result of the influence of time-varying interactions within various parameter regions. We specifically focus on elucidating how the transition point between extreme events and regular events shifts in response to the duration of interaction time between the systems. By selecting the appropriate interaction time, we can effectively mitigate extreme events, which is highly advantageous for controlling undesired fluctuations in engineering applications. Furthermore, we extend our investigation to networks of oscillators, where the interactions among network elements are also time dependent. The proposed approach for coupled systems holds wide applicability to oscillator networks.

Dynamic-group-aware networks for multi-agent trajectory prediction with relational reasoning

Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly consider static, pairwise interactions with limited relational reasoning. To more comprehensively model interactions and reason relations, we propose DynGroupNet, a dynamic-group-aware network, which (i) models time-varying interactions in highly dynamic scenes; (ii) captures both pairwise and group-wise interactions; and (iii) reasons both interaction strength and category without direct supervision. Based on DynGroupNet, we further design a prediction system to forecast socially plausible trajectories with dynamic relational reasoning. The proposed prediction system leverages the Gaussian mixture model, multiple sampling and prediction refinement to promote prediction diversity for multiple future possibilities capturing, training stability for efficient model learning and trajectory smoothness for more realistic predictions, respectively. The proposed complex interaction modeling of DynGroupNet, future diversity capturing, efficient model training and trajectory smoothing of prediction system together to promote more accurate and plausible future predictions. Extensive experiments show that: (1) DynGroupNet can capture time-varying group behaviors, infer time-varying interaction category and interaction strength during prediction; (2) DynGroupNet significantly outperforms the state-of-the-art trajectory prediction methods by 28.0%, 34.9%, 13.0% in FDE on the NBA, NFL and SDD datasets.