Dynamic Model Research Articles

Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics.

The complex neural activity of prefrontal cortex (PFC) is a hallmark of cognitive processes. How these rich dynamics emerge and support neural computations is largely unknown. Here, we infer mechanisms underlying the context-dependent integration of sensory inputs by fitting dynamical models to PFC population responses of behaving monkeys. A class of models implementing linear dynamics driven by external inputs accurately captured PFC responses within contexts and revealed equally performing mechanisms. One model implemented context-dependent recurrent dynamics and relied on transient input amplification; the other relied on subtle contextual modulations of the inputs, providing constraints on the attentional effects in sensory areas required to explain flexible PFC responses and behavior. Both models revealed properties of inputs and recurrent dynamics that were not apparent from qualitative descriptions of PFC responses. By revealing mechanisms that are quantitatively consistent with complex cortical dynamics, our modeling approach provides a principled and general framework to link neural population activity and computation.

Measurement and modelling of driver learning of steering control during successive obstacle avoidance manoeuvres

Understanding of driver behaviour can provide invaluable insight for the design of vehicles and driver assistance systems. Most existing human driver models do not incorporate driver learning or the effect of a driver's confidence in their predictions of future states, both of which affect human-generated control actions. In this paper, human driver steering control is assessed based on experimental data, then a driver model is proposed which captures driver learning and is capable of reproducing a wide range of human control styles by the selection of appropriate parameters. The driver model learns an internal model of the vehicle dynamics from experience, using a Gaussian Process, then selects control actions via Model Predictive Control. The results verify the model's capacity to capture the learning drivers achieve over time and to replicate various observed cautious and adventurous behaviours.

Stress Wave Characteristics and Damage Analysis of the Helicopter Tail Drive Shaft with the High-Speed Projectile Impact

The tail drive shaft is easily hit by the ground antiaircraft machine gun in low-altitude helicopter combat, resulting in projectile penetration damage and directly jeopardizing the helicopter’s safety during flight. The projectile penetration damage will cause elastic–plastic deformation and failure damage to the tail drive shaft, and excite high-frequency stress waves suitable for projectile impact monitoring. To examine the effects of different impact parameters on the stress wave characteristics and damage from the tail drive shaft during projectile impact, the LS-DYNA is used to establish a dynamic model of projectile impact on the tail drive shaft. The passive monitoring experiment of projectile impact on the tail drive shaft is carried out based on the PZT piezoelectric sensor. It is concluded that both methods can obtain reliable stress wave signals and damage conditions through the comparison of experimental and simulation results. Then, the wavelet transform and short-time Fourier transform three-dimensional time–frequency are used to select the best frequency band for extracting the stress wave eigenvalues, along with the statistics and analysis of the stress wave characteristic values and damage degree under different impact parameters. The results show that the impact stress waveforms and high-frequency components are significantly different with the change in projectile impact parameters. There are significant differences among different impact parameters on the first trough amplitude, the two peak time intervals, the kurtosis factor, and the bullet hole damage generation laws. The functional relationship between the projectile impact parameters and the characteristic parameters of the stress wave, as well as the damage degree, are established. The research conclusion can provide theoretical reference and data support for projectile impact monitoring and damage assessment of the helicopter tail drive shaft.

Dynamic relations between longitudinal morphological, behavioral, and emotional indicators and cognitive impairment: evidence from the Chinese Longitudinal Healthy Longevity Survey

BackgroundWe aimed to assess the effects of body mass index (BMI), activities of daily living (ADL), and subjective well-being (SWB) on cognitive impairment and propose dynamic risk prediction models for aging cognitive decline.MethodsWe leveraged the Chinese Longitudinal Healthy Longevity Survey from 1998 to 2018. Cognitive status was measured using the Chinese Mini-Mental State Examination. We employed repeated measures correlation to assess associations, linear mixed-effect models to characterize the longitudinal changes, and Cox proportional hazard regression to model survival time. Dynamic predictive models were established based on the Bayesian joint model and deep learning approach named dynamic-DeepHit. Marginal structural Cox models were adopted to control for time-varying confounding factors and assess effect sizes.ResultsADL, SWB, and BMI showed protective effects on cognitive impairment after controlling observed confounding factors, with respective direct hazard ratios of 0.756 (0.741, 0.771), 0.912 (0.902, 0.921), and 0.919 (0.909, 0.929). Dynamic risk predictive models manifested high accuracy (best AUC = 0.89). ADL was endowed with the best predictive capability, although the combination of BMI, ADL, and SWB showed the most remarkable performance.ConclusionsBMI, ADL, and SWB are protective factors for cognitive impairment. A dynamic prediction model using these indicators can efficiently identify vulnerable individuals with high accuracy.

Adaptive Position/Force Controller Design Using Fuzzy Neural Network and Stiffness Estimation for Robot Manipulator

AbstractThis paper proposes an adaptive hybrid position/force control approach using fuzzy neural networks (FNNs) for a robot manipulator with joint friction compensation. The dynamics model and system uncertainties are estimated by FNNs. For force tracking control, an adaptive impedance controller is employed with an online stiffness estimator, wherein the stiffness of the contacted environment is estimated using a gradient descent algorithm. The adaptive update laws of the FNNs and the stability of the controller are obtained using the Lyapunov stability theorem. Finally, the proposed adaptive hybrid controller is implemented on the AR605, a 6-axis articulated robot manipulator manufactured by the Industrial Technology Research Institute (ITRI). The effectiveness and robustness of the proposed control strategies are verified by the simulation and experimental results.

How does manufacturing agglomeration affect urban ecological resilience? evidence from the Yangtze river delta region of China

As rapid urbanization challenges urban ecosystem stability, understanding the relationship between manufacturing agglomeration (MA) and urban ecological resilience (UER) has become increasingly critical. This study examines how MA influences UER in China’s Yangtze River Delta region, employing dynamic spatial panel models to analyze prefecture-level panel data from 2003 to 2020. Our findings reveal an inverted U-shaped relationship between MA and UER, with significant spatial spillover effects. Specifically, moderate levels of MA enhance UER through improved resource efficiency and technological innovation, while excessive agglomeration leads to environmental degradation. The study also identifies green technological innovation as a critical mediating mechanism in this relationship. These findings contribute to theoretical understanding and policy formulation for sustainable urban development.

The annual dynamic dataset of high-resolution crop water use in China from 1991 to 2019

Accurately quantifying agricultural water use is essential for protecting agricultural systems from the risk of water scarcity and promoting sustainable water management. While previous studies have innovatively provided spatially explicit analyses or datasets, they tend to have relatively coarse resolution (~8.3 km), and inadequately considered precise localization parameters. Here, we produced annual blue and green water use for 15 main crops with a resolution of 1 km for the years 1991–2019 in China. Firstly, we estimated the yearly crop blue and green water use at the site scale by incorporating more localized input parameters using a dynamic water balance model. Then, the random forest model was combined with site-scale simulation results to generate spatial predictions of blue and green water for each crop from 1991 to 2019. The resulting maps showed a high correlation with locally observed values at field stations (R2 = 0.95), statistics (R2 = 0.77), and exhibited some strengths compared with existing datasets that covered various scales. This dataset can play a key role in devising sustainable water management strategies.

Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory

To solve the problem of photovoltaic power prediction in areas with large climate changes, this article proposes a hybrid Long Short-Term Memory method to improve the prediction accuracy and noise resistance. It combines the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and kernel principal component analysis (KPCA) algorithm. The ICEEMDAN algorithm reduces the instability of the environmental factor sequence. The KPCA algorithm reduces the input dimensions of the model. LSTM performs dynamic time modeling of the multivariate feature sequences to predict the output PV power. The adaptability of the ICEEMDAN-KPCA-LSTM model is assessed with datasets from a PV plant in west China and evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared metrics. Using 70% of the datasets for output PV power estimation, the results show a good performance, with an RMSE of 4.3715, MAPE of 8.9264%, and R-squared value of 89.973%. By comparing with other prediction models, the ICEEMDAN-KPCA-LSTM photovoltaic output power model outperforms other models.

Experimental and Computational Investigation of the Emission and Dispersion of Fine Particulate Matter (PM2.5) During Domestic Cooking

As the wealth of evidence grows as to the negative impact of indoor air quality on human health, it has become increasingly urgent to investigate and characterise sources of air pollution within the home. Fine particulate matter with a diameter of 2.5 µm or less (PM2.5) is a key cause for concern, and cooking is known to be one of the most significant sources of domestic PM2.5. In this study, the aim was to demonstrate the efficacy of combining experimental techniques and cutting-edge High-Performance Computing (HPC) to characterise the dispersion of PM2.5 during stir-frying within a kitchen laboratory. This was carried out using both experimental measurement with low-cost sensors and high-fidelity Computational Fluid Dynamics (CFD) modelling, in which Lagrangian Stochastic Methods were used to model particle dispersion. Experimental results showed considerable spatio-temporal variation across the kitchen, with PM2.5 mass concentrations in some regions elevated over 1000g/m3 above the baseline. This demonstrated both the impact that even a short-term cooking event can have on indoor air quality and the need to factor in such strong spatio-temporal variations when assessing exposure risk in such settings. The computational results were promising, with a reasonable approximation of the experimental data shown at the majority of monitoring points, and future improvements to and applications of the model are suggested.

Frequency-independent damping models for a high-rise building with simultaneous horizontal and vertical seismic motions

Recent seismic analyses indicate that the structural damping ratio should be considered frequency-independent, for safe and accurate estimations. In response, damping models like the Wilson–Penzien (WP) damping model, that is one of the modal damping, provide frequency independence across all modes; however, these models require considerable computational resources, especially for large-scale models. While Rayleigh damping is computationally efficient, it maintains a nearly constant damping ratio only within a limited frequency range. To address these limitations, several alternative damping models have been introduced, such as uniform (UN), causal hysteretic (CH), and extended Rayleigh (ER). We use the factor Wξ to represent the frequency range where the damping ratio remains approximately constant, defined as the ratio of maximum to minimum frequencies (fmax/fmin), within a specified tolerance of the target damping ratio. For Rayleigh damping, Wξ = 3.7, while the CH and ER models achieve Wξ values greater than 20. Although the UN model achieves a high Wξ, it demands large computational resources in the implicit analyses, commonly used for seismic response studies. In this study, we address the simultaneously inputting horizontal and vertical seismic motion into a large-scale dynamic analysis model of a high-rise building. In this analysis, horizontal, vertical, and local beam vibration modes spanning a wide frequency range appeared. Considering that these modes require the same damping ratio, damping models with Wξ values of 50 or higher are desirable. However, this threshold considerably exceeds Wξ values achievable with the existing models, rendering these models unsuitable for the intended application. Therefore, we propose and validate the efficiency of two new damping models (ER-W and CH19) that meet this requirement by improving existing models. Using these damping models, it is possible to analyze the horizontal and vertical modes and local vibration modes of the beam, assuming a simultaneous horizontal and vertical input to a high-rise building.

Identifying dynamic discrete choice models with hyperbolic discounting

Identifying dynamic discrete choice models with hyperbolic discounting

Source optimization method employing dynamic polarization aberration model for High-NA EUVL

Source optimization method employing dynamic polarization aberration model for High-NA EUVL

COVID-19 REINFECTION PROCESSES: MATHEMATICAL MODELING, DATA FITTING WITH TIME-VARYING PARAMETERS, AND OPTIMAL CONTROL STUDIES

In this paper, we construct a COVID-19 dynamic model that included both the initial and reinfected population compartments, and conduct a structural identifiability analysis of the model parameters to ensure the robustness of the parameter fitting results. We use some actual statistical data from North Carolina to fit the model and estimate the values of some important parameters. In order to accurately fit the parameters in the model, we improve the physics-informed neural networks (PINNs) method in this paper, so that the fitting results can be reproduced on Matlab. The results of this study show that the transmission capacity of the virus in the reinfected person is only slightly lower than that of the first infected person, and vaccination is not effective in reducing the transmission rate of the virus. The death rate of the reinfected is much higher than that of the first infected person. Finally, we conduct a cost-effectiveness study using optimal control methods and found that, while it is easier to reduce reinfection by combining multiple strategies, the most effective strategy for reducing reinfection is to increase treatment cure rates and reduce direct or indirect contact among those who have recovered.

Tidally driven remelting around 4.35 billion years ago indicates the Moon is old

The last giant impact on Earth is thought to have formed the Moon1. The timing of this event can be determined by dating the different rocks assumed to have crystallized from the lunar magma ocean (LMO). This has led to a wide range of estimates for the age of the Moon between 4.35 and 4.51 billion years ago (Ga), depending on whether ages for lunar whole-rock samples2, 3–4 or individual zircon grains5, 6–7 are used. Here we argue that the frequent occurrence of approximately 4.35-Ga ages among lunar rocks and a spike in zircon ages at about the same time8 is indicative of a remelting event driven by the Moon’s orbital evolution rather than the original crystallization of the LMO. We show that during passage through the Laplace plane transition9, the Moon experienced sufficient tidal heating and melting to reset the formation ages of most lunar samples, while retaining an earlier frozen-in shape10 and rare, earlier-formed zircons. This paradigm reconciles existing discrepancies in estimates for the crystallization time of the LMO, and permits formation of the Moon within a few tens of million years of Solar System formation, consistent with dynamical models of terrestrial planet formation11. Remelting of the Moon also explains the lower number of lunar impact basins than expected12,13, and allows metal from planetesimals accreted to the Moon after its formation to be removed to the lunar core, explaining the apparent deficit of such materials in the Moon compared with Earth14.

Dynamics analysis of rotor in disc centrifuge for separation of bioengineering

In order to reveal the influence of gyroscope effect and structure parameters on the modal frequency and critical speed of the rotor system in disc centrifuge for separation of bioengineering. Based on the rotor rigid body dynamics, the fixed-point gyroscope motion of the rotating spindle of the disc centrifuge was analyzed. Establishing a finite element dynamic model for the rotor system of a disc centrifuge, taking into account the gyroscopic moment caused by the rotational inertia of the rotor disk and the elastic support of the bearings. We calculated the gyroscopic moment and the analytical expression of critical speed. Analyzing the quantitative relationship between the gyroscope effect, bearing support stiffness, drum material density and the critical speed of the rotor system. The results show that the calculated value of finite element is close to the measured value. The influence of gyroscopic force on the vibration characteristics of rotor system for dish centrifuge cannot be ignored. The critical speeds of the rotor system increase with the increase of elastic support stiffness, while the first critical speed decreases with the increase of drum density, but the second critical speed increases with the increase of drum density. These studies provide a theoretical reference for the dynamic response, structural design and dynamic balance of disc centrifuge.

Model Identification and Transferability Analysis for Vehicle-to-Grid Aggregate Available Capacity Prediction Based on Origin–Destination Mobility Data

Vehicle-to-grid (V2G) technology is emerging as an innovative paradigm for improving the electricity grid in terms of stabilization and demand response, through the integration of electric vehicles (EVs). A cornerstone in this field is the estimation of the aggregated available capacity (AAC) of EVs based on available data such as origin–destination mobility data, traffic and time of day. This paper considers a real case study, consisting of two aggregation points, identified in the city of Padua (Italy). As a result, this study presents a new method to identify potential applications of V2G by analyzing floating car data (FCD), which allows planners to infer the available AAC obtained from private vehicles. Specifically, the proposed method takes advantage of the opportunity provided by FCD to find private car users who may be interested in participating in V2G schemes, as telematics and location-based applications allow vehicles to be continuously tracked in time and space. Linear and nonlinear dynamic models with different input variables were developed to analyze their relevance for the estimation in one-step- and multiple-step-ahead prediction. The best results were obtained by using traffic data as exogenous input and nonlinear dynamic models implemented by multilayer perceptrons and long short-term memory (LSTM) networks. Both structures achieved an R2 of 0.95 and 0.87 for the three-step-ahead AAC prediction in the two hubs considered, compared to the values of 0.88 and 0.72 obtained with the linear autoregressive model. In addition, the transferability of the obtained models from one aggregation point to another was analyzed to address the problem of data scarcity in these applications. In this case, the LSTM showed the best performance when the fine-tuning strategy was considered, achieving an R2 of 0.80 and 0.89 for the two hubs considered.

The role of subsurface models to evaluate geo-containment for safe storage of CO 2 – A case study of the Porthos CCS project in the Netherlands

3D static and dynamic models are the recommended standard to assess uncertainties in the subsurface. When focusing on CO 2 storage in depleted hydrocarbon fields, uncertainties from the potential storage resources are reasonably constrained, but uncertainties can be substantial regarding injectivity and, most importantly for safe operations, the evaluation of storage containment and fault stability. Traditional modelling workflows from the oil and gas industry can be followed to evaluate storage resources and injectivity. However, CO 2 project specific risks require different modelling workflows with additional interfaces between disciplines like geomechanics and flow assurance, which need to be linked logically to capture the subsurface process. It has been demonstrated that the operational objectives for long-term CO 2 storage can be safely achieved. The structural setting in the area of interest is well understood, and sealing integrity of the overburden sections and risk of seismic events has been assessed. Mechanisms and impact of potential CO 2 migration have been studied in detail and safeguards have been assessed. The Porthos case has proven that the applied workflow is a suitable way to get the full picture from multiple disciplines, highlighting the need for a solid initial framing and a risk-based modelling approach.

Research on the impact of traction rods on coupler separation in the middle locomotive of 20,000-tonne heavy-haul trains

Aiming at the coupler separation problem in the middle locomotive of 20,000-tonne heavy-haul trains in China, this study investigates the influence of the locomotive traction rod. A theoretical kinematic model was developed to compare the motion of locomotives equipped with single and double traction rods, and the mechanism of coupler separation was analysed. A dynamic model, considering the three-dimensional interaction of couplers, was also established to simulate the dynamic process of coupler separation during braking and release stages. The simulation results indicate that locomotives equipped with double rods (positioned at both sides of the car body) are more prone to coupler separation than those with single rods (placed at the middle of the car body). The pitch angle of the double-rod model reaches a maximum of 0.20° when the electric braking force is 200 kN, approximately 10 times that of the single-rod model. For double-rod locomotives prone to coupler separation, maintaining a coupler knuckle friction coefficient greater than 0.1 and an initial height difference of less than 50 mm can significantly reduce the risk of coupler separation.

Adolescents’ Self-Regulation of Social Media Use During the Beginning of the COVID-19 Pandemic: An Idiographic Approach

AbstractAdolescent social media serves a broad range of functions, which may be helpful for some and harmful for others. During the COVID-19 lockdown, social media evolved considerably, occupying an even more central role in adolescents’ lives. This study leverages a new approach to measuring social media use behaviors—passive smartphone sensing. Specifically, we aimed to test if and how adolescents self-regulate their social media use in response to how they feel during and after use. This study followed 19 adolescents for 1 month. Participants completed baseline measures, assessing demographic and clinical characteristics. We used passive smartphone sensing to measure objective social media use behaviors (“screen time” and checking) for a 1-month period. Adolescents also completed daily diary questions on their mood. Analyses took an idiographic (n = 1) approach. Dynamic structural equation models tested daily and next-day relationships between social media use behaviors and mood for each adolescent. Most adolescents (n = 13 of 19) did not self-regulate their social media use in relation to their mood. Most importantly, they did not use it less when they felt more negative mood during use. That said, some adolescents (n = 6) did alter their social media use behaviors depending on their mood. Each adolescent’s pattern of social media use and mood was also qualitatively interpreted within their context of demographic (e.g., experience of holding a minoritized identity) and clinical characteristics (e.g., history of suicidal thoughts and behaviors). These results highlight the next steps for possible intervention points to help adolescents adjust their use patterns to maximize mental health benefits while minimizing possible harm. Findings also begin to develop a template for applying social media use recommendations, while centering the experiences of individual adolescents.

Nonstationary Functional Time Series Forecasting

ABSTRACTWe propose a nonstationary functional time series forecasting method with an application to age‐specific mortality rates observed over the years. The method begins by taking the first‐order differencing and estimates its long‐run covariance function. Through eigendecomposition, we obtain a set of estimated functional principal components and their associated scores for the differenced series. These components allow us to reconstruct the original functional data and compute the residuals. To model the temporal patterns in the residuals, we again perform dynamic functional principal component analysis and extract its estimated principal components and the associated scores for the residuals. As a byproduct, we introduce a geometrically decaying weighted approach to assign higher weights to the most recent data than those from the distant past. Using the Swedish age‐specific mortality rates from 1751 to 2022, we demonstrate that the weighted dynamic functional factor model can produce more accurate point and interval forecasts, particularly for male series exhibiting higher volatility.