Dynamic Factor Research Articles

Investigation on the propagation of uncertainties of a timber floor under human excitation

Due to the characteristics of high stiffness-weight ratio, timber floors are prone to annoying vibrations under human excitation. Given the natural origin of timber, its mechanical properties exhibit significant variability. The randomness inherent in human excitation cannot be overlooked during structural dynamic analysis. Consequently, the adoption of a stochastic approach is imperative for attaining reliable serviceability evaluation results. However, current research on human-induced vibrations in the timber floor, accounting for this randomness, remains inadequate. In this paper, an experimental investigation is conducted on the dynamic properties and human-induced responses of a timber floor composed of glued laminated timber and oriented strand board. A finite element model is developed and subsequently validated for accuracy in terms of modal properties and dynamic responses. The probability density evolution method is introduced for stochastic analysis, which demonstrates that both the natural frequency and dynamic responses of the floor exhibit considerable variability when uncertainty factors are considered. The Kullback–Leibler divergence indices are used to assess the impact of each uncertain variable quantitatively. The results indicate that the longitudinal elastic modulus has the greatest influence on the natural frequency, while the first dynamic load factor, αz1, exerts the most significant impact on dynamic responses. Notably, both material mechanical properties and load model parameters contribute to the uncertainty of dynamic responses, with the influence of the load model parameters being significantly greater than that of material mechanical properties.

A hybrid model for state of charge estimation of lithium-ion batteries utilizing improved adaptive extended Kalman filter and long short-term memory neural network

With lithium-ion batteries being utilized in all aspects of life, accurately estimating the state of charge (SOC) of a battery has become a key issue in battery management systems. In this paper, an improved hybrid model based on adaptive extended Kalman filter (AEKF) and improved long short-term memory (ILSTM) neural network is proposed. The proposed model is based on a second-order RC equivalent circuit model, and the dynamic forgetting factor recursive least squares (DFFRLS) and AEKF algorithms are utilized to obtain the initial SOC estimates. And the estimation error in the AEKF algorithm due to neglecting the higher order terms of the Taylor expansion equations is compensated by the improvement of the LSTM network. The results under different working conditions indicate that the SOC estimation of the hybrid model has good convergence and high system robustness. The maximum error (MAX) of this algorithm is less than 2.3 %, especially the root mean square error (RMSE) and mean absolute error (MAE) are less than 0.84 % and 0.65 %, respectively.

The Business Cycle and the Financial Cycle: Granger Causality for the Peruvian Economy, 2000 – 2019

It is relevant to know the relationship between the financial and business cycles to prevent the appearance of financial crises and their negative effects on the macroeconomy. In this sense, a better understanding of the links between financial cycles and the business cycle of Peru can provide valuable information for economic and macroprudential policy decisions. This study examines the causal relationship between Peru's financial and business cycles. The dynamic factors methodology is used to measure the financial cycle, taking four representative indicators of the financial sector: Volume of loans to the private sector (credit market), the Lima Stock Exchange General Index (stock market), Embig Peru (bond market), and the Exchange Rate (currency market) (Ramos, 2019). On the one hand, using the Granger causality test in the frequency domain, it was found that the financial cycle causes the business cycle at frequencies greater than 10 quarters (2.5 years). In contrast, the business cycle does not cause the financial cycle.

Gut Microbiota and DTI Microstructural Brain Alterations in Rodents Due to Morphine Self-Administration.

The opioid epidemic is an evolving health crisis in need of interventions that target all domains of maladaptive changes due to chronic use and abuse. Opioids are known for their effects on the opioid and dopaminergic systems, in addition to neurocircuitry changes that mediate changes in behavior; however, new research lines are looking at complementary changes in the brain and gut. The gut-brain axis (GBA) is a bidirectional signaling process that permits feedback between the brain and gut and is altered in subjects with opioid use disorders. In this work, we determine longitudinal, non-invasive, and in-vivo complementary changes in the brain and gut in rodents trained to self-administer morphine for two weeks using MRI and 16S rDNA analysis of fecal matter. We assess the changes occurring during both an acute phase (early in the self-administration process, after two days of self-administration) and a chronic phase (late in the self-administration process, after two weeks of self-administration), with all measurements benchmarked against baseline (naïve, non-drug state). Rats were surgically implanted with an intravenous jugular catheter for self-administration of morphine. Rats were allowed to choose between an active lever, which delivers a single infusion of morphine (0.4 mg/kg/infusion), or an inactive lever, which had no consequence upon pressing. Animals were scanned in a 7T MRI scanner three times (baseline, acute, and chronic), and before scanning, fecal matter was collected from each rat. After the last scan session, a subset of animals was euthanized, and brains were preserved for immunohistochemistry analysis. We found early changes in gut microbiota diversity and specific abundance as early as the acute phase that persisted into the chronic phase. In MRI, we identified alterations in diffusivity indices both within subjects and between groups, showing a main effect in the striatum, thalamus, and somatosensory cortex. Finally, immunohistochemistry analyses revealed increased neuroinflammatory markers in the thalamus of rats exposed to morphine. Overall, we demonstrate that morphine self-administration shapes the brain and gut microbiota. In conclusion, gut changes precede the anatomical effects observed in MRI features, with neuroinflammation emerging as a crucial link mediating communication between the gut and the brain. This highlights neuroinflammation as a potential target in addressing the impacts of opioid use.

Assessing the Role of Emotional Intelligence in Team Performance Case study at Al Mujtama Islamic Boarding School 4 Cianjur

Emotional intelligence (EI) has emerged as a crucial factor in understanding team dynamics and performance within service organizations. This paper presents a conceptual framework that assesses the role of emotional intelligence in enhancing team performance, specifically tailored for service-oriented contexts. The framework integrates key components of emotional intelligence, including self-awareness, self-regulation, social awareness, and relationship management, with team dynamics such as collaboration, communication, and conflict resolution. The paper begins by defining emotional intelligence and its relevance in the service industry, highlighting its impact on individual and team behaviors. It then outlines the conceptual framework, detailing how each aspect of emotional intelligence contributes to team effectiveness and performance. The framework emphasizes the importance of developing emotional intelligence skills among team members and leaders to foster a positive work environment and improve overall team outcomes. Furthermore, the paper discusses the potential challenges and barriers faced in implementing emotional intelligence practices in service organizations, such as resistance to change and the need for training and development programs. Strategies for overcoming these challenges and promoting a culture of emotional intelligence within teams are explored, drawing on examples from successful case studies and empirical research. Through a comprehensive review of existing literature and theoretical perspectives, this paper offers insights into the potential benefits of integrating emotional intelligence into team development strategies within service organizations. Practical implications and recommendations for implementing the framework are also discussed, aiming to guide organizations in enhancing team performance through a deeper understanding and application of emotional intelligence principles

Refined Landslide Susceptibility Mapping Considering Land Use Changes and InSAR Deformation: A Case Study of Yulin City, Guangxi

Landslide susceptibility maps (LSMs) are valuable tools typically used by local authorities for land use management and planning activities, supporting decision-makers in urban and infrastructure planning. To address this, we proposed a refined method for landslide susceptibility assessment, which comprehensively considered both static and dynamic factors. Neural network methods were used for susceptibility analysis. Land use and land cover (LULC) change and InSAR deformation were then integrated into the traditional susceptibility zoning to obtain a refined susceptibility map with higher accuracy. Validation was conducted on the improved landslide susceptibility map using site landslide data. The results showed that the LULC were proven to be the core driving factors for landslide occurrence in the study area. The GRU model achieved the highest model performance (AUC = 0.886). The introduction of InSAR surface deformation and land use and land cover change data could rationalize the inappropriateness of traditional landslide susceptibility zoning, correcting the false positive and false negative areas in the traditional landslide susceptibility map caused by human activities. Ultimately, 12.25% of the study area was in high-susceptibility zones, with 3.10% of false positive and 0.74% of false negative areas being corrected. The proposed method enabled refined analysis of landslide susceptibility over large areas, providing technical support and disaster prevention and mitigation references for geological hazard susceptibility assessment and land management planning.

Transient thermal fracture investigation of a cracked functionally graded layer via Guyer-Krumhansl model

Recent experimental evidence proves the Guyer-Krumhansl (G-K) model’s capability of predicting the nonclassical transient heat transfer process in heterogeneous composites. The objective of this article is to explore the dynamic thermal and fracture behaviors of a cracked orthotropic functionally graded material (FGM) layer subject to abrupt thermal shocks via the G-K equation. The thermal and elastic governing equations determined by the G-K model are transformed into the singular integral equations, which are calculated numerically to assess the non-Fourier transient temperatures and the resultant dynamic thermal stress intensity factors. Parametric comparisons are carried out for the effects of the non-Fourier thermal lagging time and thermal nonlocal length on the transient thermoelastic responses. The findings would benefit the applications of unconventional heat conduction theories and shed light on the scientific understanding of FGM’s fracture behaviors.

Financial network communities and methodological insights: a case study for Borsa Istanbul Sustainability Index

This study investigates the influence of Environmental, Social, and Governance (ESG) scores on the clustering and community formation of companies within various network models. Using daily closing prices of 78 companies operating in the Borsa Istanbul Sustainability Index, we constructed correlation, mutual information (both continuous and discrete), and causality (both linear and nonlinear) networks to analyse intercompany relationships. We performed community detection using the Leading Eigenvector and Girvan–Newman methods, which revealed that companies within the same sector, particularly in the financial and manufacturing sectors, tend to form tight-knit communities. These intra-sectoral clusters reflect strong market behaviour correlations driven by sector-specific factors. Additionally, mixed-sector communities highlighted the presence of significant inter-sector dependencies. To assess the impact of ESG scores on these communities, nonparametric tests such as the Kruskal–Wallis, Conover, and log-rank were applied. Results showed that specific ESG factors, including emission, CSR strategy, innovation, and human rights, significantly influenced community formation. For instance, companies with strong performance in emission reduction and CSR strategies were found to form more cohesive communities, emphasizing the role of sustainability in shaping financial networks. Study findings underscore the critical role of ESG factors in financial market dynamics, promoting sustainable investment practices by highlighting the importance of integrating ESG considerations into investment decisions. These results suggest that sustainability metrics not only affect individual company performance but also contribute to the formation of interconnected communities with shared sustainability practices.

Low- and middle-income countries’ external debt cycle and global economic prospect

The rapid growth of peripheral countries has brought significant changes to the global economic and financial system, making it crucial to examine the relationship between their cyclical debt fluctuations and the world economy. Using the Bayesian dynamic factor model, this paper documents the presence of a set of common factors, known as global debt cycles, in the external debt accumulation of the low- and middle-income countries (LMICs). The interesting finding is that this debt cycles differ from well-known factors for global financial cycles in capital flows, such as U.S. monetary policy and global risk aversion. Even after controlling the factors for global financial cycles, our results reveal a significantly negative predictive effect of estimated debt cycles on global economic growth. Moreover, we demonstrate that this negative effect is primarily dominated by LMICs occupying prominent positions in international debt market networks, especially during the post-2008 financial crisis period. These findings emphasize the increasing importance of peripheral countries in the world system, which has been largely ignored in existing literature.

How do school experiences in adolescence affect educational investment in adulthood? The case of parental investment in private tutoring in Germany

AbstractThis article examines the role of parents’ school experiences in adolescence on their educational investment strategies for their own children in middle adulthood. We focus our analysis on the parents’ decision to organize private tutoring (also called shadow education) for their children. Previous international research largely agrees that families with a high socioeconomic status (SES) are more likely to invest in tutoring to achieve competitive advantages in the educational race for higher credentials and maintain a high social status. In Germany, however, recent studies suggest that tutoring in Germany primarily serves underperforming children from les educated families to acquire relatively higher school degrees by compensating the higher demands of academic school tracks. We propose a theoretical decision model based on rational choice and life course theory incorporating psychological factors related to school performance of the parents (such as performance anxiety and ability self-concept) and operationalize four components that are intended to explain the (un)equal use of tutoring: (1) children’s school background, (2) parents’ cost and (3) benefit considerations, as well as (4) parents’ school biography. In addition, we hypothesize on the intergenerational transmission of performance-related factors from parents to their children and how this affects SE investment. We test our hypotheses quantitatively through logit regressions and a structural equation model using unique longitudinal data for 558 families (parents and their children) from the German “LifE” study (1979–2012). Our study produced two key findings: On the one hand, the parents’ previous school performance plays a direct role in the extent to which they themselves are able to provide their children with the necessary support in the event of poor performance. On the other hand, the performance-related experiences of the parents during school are to a certain extent transmitted to their children and thus influence their grades, ability self-concept and performance anxiety. These factors, in turn, are decisive for the decision to organize SE. Our findings indicate that instead of reducing the decision-making process for SE to absolute dimensions of student performance and parents’ SES and related cost-benefit calculations, dynamic factors behind this family decision related to intergenerational transmission and individual characteristics should be considered.

DNIM: Deep-sea netting intelligent enhancement and exposure monitoring using bio-vision

Intelligent monitoring of deep-sea nets is affected by light attenuation, light scattering, and limited dynamic range factors of the camera, which can cause color shift, low visibility, and over/underexposure in monitoring, resulting in reduced farming efficiency, and misdetection of biological behavior. Therefore, we propose a method for underwater net tank scene enhancement and exposure using multicolor space and bio-vision, called DNIM. Specifically, we first propose an intelligent enhancement and exposure monitoring method for deep-sea nets using bio-vision, which eliminates the effect of exposure on underwater imaging by introducing a bio-vision system that uses a brightness transformation function to generate a series of exposure images. Inspired by the image fusion method, we designed a color correction and visibility recovery method using bio-vision. In the Lab color model, color shifts in bio-vision are corrected by guiding feature transformations with contrast-constrained adaptive histogram equalization; in the RGB color model, depth information is recovered by creating a linear model for the depth of field of underwater blurred images based on the characteristics of the bio-vision system. To obtain more qualified results, we also propose an efficient perceptual fusion module to mix the output of visibility recovery and color correction. Finally, we propose two deep-sea net tank culture datasets: salmon bait-eating behavior dataset (SBBD) and salmon non-bait-eating behavior dataset (SNBD). By comparing the experimental analysis in four real scenario datasets, our DNIM achieved superior results compared to 16 advanced underwater enhancement methods. The code is publicly available at: https://github.com/An-Shunmin/DNIM.

Effect of tailing powder content on dynamic behavior of iron tailing porous concrete: An experimental study

Iron tailing porous concrete (ITPC) is a new type of green-engineered composite that uses iron ore tailing (IOT) powder to partially replace cement in conventional foamed concrete. This study investigates the dynamic properties of ITPC with a range of IOT content (15 % to 35 %) under high-strain rate loads, as well as the hydration products and microstructures of ITPC. Additionally, foamed concrete specimens without IOT powder are prepared and tested as references. The X-ray diffraction (XRD) results indicate that the addition of IOT powder causes the presence of iron oxide (Fe2O3) and silicon dioxide (SiO2) in the ITPC, resulting in a decrease in calcium silicate hydrate (C-S-H) gel and ettringite (AFt). A split Hopkinson pressure bar (SHPB) facility is used to perform impact tests at expected strain rates from 120 to 250 s−1. The experimental results demonstrate that the properties of ITPC obtained in dynamic loading tests exhibit significant IOT content dependence and strain rate effects. Moreover, scanning electron microscopy (SEM) analysis reveals that excessive IOT content results in the formation of numerous micro-cracks and loose pore structures within the ITPC specimens. ITPC with IOT content ranging from 25 % to 30 % achieves the optimal performance with high impact resistance and effective utilization of iron tailing waste. Specifically, at the expected strain rates of 120, 200, and 250 s−1, increasing the IOT content from 15 % to 35 % resulted in reductions of compressive strength by 37.52 %, 51.34 %, and 51.22 %, respectively. The corresponding dynamic increase factors of ITPC achieved the maximum values of 1.25 (25 % IOT content), 2.16 (30 % IOT content) and 3.12 (30 % IOT content), respectively.

A two-step dynamic factor modelling approach for forecasting inflation in small open economies

We build a dynamic factor model to forecast inflation in a small open economy. The model is estimated with both market and survey data, and a unique two-step methodology to incorporate exogenous factors. Estimations with market data provide a better fit for in-sample and out-of-sample values of inflation. More importantly, our model outperforms univariate and estimated DSGE models, the more common approaches to inflation forecasting that perform well for advanced economies. Our findings, therefore, suggest that a dynamic factor modelling approach for a small open economy such as Jamaica can be a good alternative to the preferred methods for forecasting inflation in advanced economies.

Research of the impact of environmentally persistent free radicals on chemical element behaviour in the soil–plant system

Environmentally persistent free radicals (EPFRs) may pose a potential risk to the ecosystem and human health via oxidation stress and are considered emerging contaminants. Being stable with a lifetime of minutes or several months and abundant in transitional matrices (e.g. biochar), EPFRs continue to affect deposits (e.g. soil) and related media (plants) when the transitional matrices (e.g. biochar) are applied. The impact of EPFR on the plant uptake of chemical elements (CEs) was studied in the field conditions where, for two years, biochar and fertilisers were applied to the agricultural soil for winter triticale cultivation. EPFRs determination methods, along with the element uptake indices (bioaccumulation and biophilicity) and the method of the dynamic factors were applied. Results have shown that EPFRs have influenced the soil properties relevant to CE soil bioavailability and bioaccumulation in plants. The impact of EPFRs on CE transport in the soil–plant system was observed to influence the biogeochemical behaviour of CEs in the soil–plant system. This work provides the first findings on EPFRs-induces changes on CE bioavailability and bioaccumulation intensity, indicating the higher plant uptake risk of some potentially toxic elements (such as Cr).

Response of semicircular canyons and movable cylindrical cavities to SH waves in anisotropic half-space geology

The development of tunnels or the laying of underground pipelines are essential engineering projects in modern society, and in canyon tunnels and underground pipeline projects, the surface motion and cavity edge motion have been topics of concern in ground vibration problems. We investigate the wave scattering problem in an elastic half-space anisotropic medium containing a semicircular canyon and a subsurface movable cylindrical cavity by using the wave function expansion method, the complex function method, and the mirror method. By deriving the governing equation and transforming it into the standard form of the Helmholtz equation satisfying the zero-stress boundary condition, we solve the corresponding displacement functions. Introducing a position correction coefficient, the scattered wavefield in a half-space anisotropic medium is constructed by the mirror method, which improves the problem of scattered wave source singularity in anisotropic half-space media. Then, combining the free boundary conditions with a Fourier series expansion method, we solve the unknown coefficients in the equations. The correctness of the method is verified by degenerating it to a classical analytic solution. Finally, using frequency- and time-domain analysis, we investigate the effects of the relevant parameters on the surface motion [Formula: see text], the dynamic stress concentration factor, and the displacement amplitude [Formula: see text]. The results indicate that rock anisotropy and the presence of semicircular canyons have a significant effect on the dynamic response of subsurface structures. This not only provides a theoretical basis for practical unlined tunnels or pipeline projects but also can provide a basis for seismic design of underground structures.

Modeling Ocean Swell and Overtopping Waves: Understanding Wave Shoaling with Varying Seafloor Topographies

One risk posed by hurricanes and typhoons is local inundation as ocean swell and storm surge bring a tremendous amount of energy and water flux to the shore. Numerical wave tanks are developed to understand the dynamics computationally. The three-dimensional equations of motion are solved by the software ‘Open Field Operation And Manipulation’ v2206. The ‘Large Eddy Simulation’ scheme is adopted as the turbulence model. A fifth-order Stokes wave is taken as the inlet condition. Breaking, ‘run-up’, and overtopping waves are studied for concave, convex, and straight-line seafloors for a fixed ocean depth. For small angles of inclination (<10°), a convex seafloor displays wave breaking sooner than a straight-line one and thus actually delivers a smaller volume flux to the shore. Physically, a convex floor exhibits a greater rate of depth reduction (on first encounter with the sloping seafloor) than a straight-line one. Long waves with a speed proportional to the square root of the depth thus experience a larger deceleration. Nonlinear (or ‘piling up’) effects occur earlier than in the straight-line case. All these scenarios and reasoning are reversed for a concave seafloor. For large angles of inclination (>30°), impingement, reflection, and deflection are the relevant processes. Empirical dependence for the setup and swash values for a convex seafloor is established. The reflection coefficient for waves reflected from the seafloor is explored through Fourier analysis, and a set of empirical formulas is developed for various seafloor topographies. Understanding these dynamical factors will help facilitate the more efficient designing and construction of coastal defense mechanisms against severe weather.

Monetary Policy Effectiveness under the Ultra‐Low Interest Rate Environment: Evidence from Yield Curve Dynamics in Japan

I examine the effectiveness of monetary policy under the ultra‐low interest rate environment in Japan through the lens of yield curve dynamics. To that end, I employ the dynamic Nelson–Siegel model with time‐varying parameters, thereby computing indicators for tracing monetary easing effects. I show that the estimation performance of the yield curve models is sufficiently improved even under the ultra‐low interest rate environment by extending the dynamic Nelson–Siegel model to allow a loading parameter to vary over time, in addition to three factors of yield curve dynamics: level, slope, and curvature. However, I also demonstrate that the identification of the level and loading parameters is critical in assessing monetary policy effects. I reveal that monetary easing effects under the Quantitative and Qualitative Monetary Easing (QQE) are produced by flattening the yield curve in the ultra‐long‐term maturity zone over 10 years, while monetary easing effects from maturities shorter than ten years remain almost unchanged. Monetary policy fails to produce sufficient monetary easing effects within the time frame of the standard macroeconomic stabilization policy, even with the full‐fledged implementation of unconventional monetary policy measures under the ultra‐low interest rate environment in Japan.

Evaluating concrete material models for blast analysis using 3D finite element analysis

Blast loads are extremely detrimental to structural elements of a building and can result in minor damage to complete failure of a building. Finite element analysis (FEA) is an effective tool to study the behavior of structural elements exposed to blast loading. The focus of this study was to evaluate the performance of three concrete material models available in LS-DYNA and compare the results to experimental results from blast loaded reinforced concrete (RC) panel supported on four sides (two-way bending). Concrete material models chosen for this study were Winfrith (MAT_84-85), Karagozian & Case (MAT_072R3), and Continuous Surface Cap Model (MAT_159). These are widely used in finite element modeling related to civil structures and can account for strain rate dependent properties. Each concrete material model was implemented with and without strain rate effects. The finite element model had concrete material as solid elements and steel reinforcement material as beam elements with simply supported boundary conditions. Results from two different blast loading methods are presented: (1) blast pulse load curve from experimental data (2) blast input by defining type, location, and amount of explosive in terms of TNT. The finite element models were evaluated by comparing fundamental period, peak displacement, and residual displacement to experimental results. In this study, the model using MAT_072R3 most closely matched experimental behavior in comparison to MAT_84-85 and MAT_159. Additional discussion is presented about: (1) single degree of freedom (SDOF) analysis, (2) comparison of numerical and experimental crack patterns, and (3) behavior of concrete material models accounting for compressive and tensile dynamic increase factors (DIFs).

Optimized Landslide Susceptibility Mapping and Modelling Using the SBAS-InSAR Coupling Model

Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely associated with geo-environmental conditions. However, landslide hazards are often characterized by significant surface deformation. The Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology plays a pivotal role in detecting and characterizing surface deformation. This work endeavors to assess the accuracy of SBAS-InSAR coupled with ensemble learning for LSM. Within this research, the study area was Shiyan City, and 12 static evaluation factors were selected as input variables for the ensemble learning models to compute landslide susceptibility. The Random Forest (RF) model demonstrates superior accuracy compared to other ensemble learning models, including eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting Decision Tree, and K-Nearest Neighbor. Furthermore, SBAS-InSAR was utilized to obtain surface deformation rates both in the vertical direction and along the line of sight of the satellite. The former is used as a dynamic characteristic factor, while the latter is combined with the evaluation results of the RF model to create a landslide susceptibility optimization matrix. Comparing the precision of two methods for refining LSM results, it was found that the method integrating static and dynamic factors produced a more rational and accurate landslide susceptibility map.

Corrections to Diffusion in Interacting Quantum Systems

The approach to equilibrium in interacting classical and quantum systems is a challenging problem of both theoretical and experimental interest. One useful organizing principle characterizing equilibration is the dissipative universality class, the most prevalent one being diffusion. In this paper, we use the effective field theory (EFT) of diffusion to systematically obtain universal power-law corrections to diffusion. We then employ large-scale simulations of classical and quantum systems to explore their validity. In particular, we find universal scaling functions for the corrections to the dynamical structure factor ⟨n(x,t)n⟩, in the presence of a single U(1) or SU(2) charge in systems with and without particle-hole symmetry, and present the framework to generalize the calculation to multiple charges. Classical simulations show remarkable agreement with EFT predictions for subleading corrections, pushing precision tests of effective theories for thermalizing systems to an unprecedented level. Moving to quantum systems, we perform large-scale tensor-network simulations in unitary and noisy 1D Floquet systems with conserved magnetization. We find a qualitative agreement with EFT, which becomes quantitative in the case of noisy systems. Additionally, we show how the knowledge of EFT corrections allows for fitting methods, which can improve the estimation of transport parameters at the intermediate times accessible by simulations and experiments. Finally, we explore nonlinear response in quantum systems and find that EFT provides an accurate prediction for its behavior. Our results provide a basis for a better understanding of the nonlinear phenomena present in thermalizing systems. Published by the American Physical Society 2024