To address the vibration interference from multiple devices in a floating-raft system, this study proposes a multichannel coupled hybrid adaptive algorithm (H∞-CNGHAFxLMS) based on H∞ control and normalized global error, aimed at enhancing the performance and stability of the traditional filtered-x least mean squares (FxLMS) algorithm. First, a simplified 3-DOF model of the horizontal-longitudinal dynamics of the floating-raft system is established, and the system's dynamic behavior with and without actuators is analyzed. Then, the three-channel FxLMS algorithm is improved by incorporating a feedback path and normalized global error. Simulation results show that the normalized global error–mixed adaptive algorithm (NGHAFxLMS) algorithm provides better vibration suppression performance across three channels compared to the conventional algorithms.To further improve the convergence speed and stability, H∞ control and multichannel coupling compensation are introduced. Simulation results demonstrate that the optimized H∞-CNGHAFxLMS algorithm achieves the lowest steady-state error under the conditions of Kc = 0.2 and γ = 1.0. Experimental validation on a test platform shows that the H∞-CNGHAFxLMS algorithm achieves RMS reductions of 5.52 dB and 5.21 dB under steady-state and non-steady-state conditions, respectively, demonstrating excellent vibration suppression, rapid convergence, and superior robustness.

INTRODUCTION: In wind power generation systems, the unstable variability of wind energy significantly affects control quality and power stability. Conventional PID controllers often show limitations in nonlinear systems or systems with time-varying parameters, especially when integral windup and degraded transient performance occur. OBJECTIVES: This paper proposes an online optimization method for PID parameters based on a Genetic Algorithm (GA), applied to a simplified dynamic model of a wind power generation system, in order to improve the system response quality. METHODS: The studied system is modeled by a second-order transfer function representing the system’s inertia and friction characteristics. The GA is implemented in a real-time optimization manner, using an objective function based on the ITAE criterion to evaluate and select the optimal PID parameter set. RESULTS: Simulation results show that the proposed online GA–PID approach improves settling time, reduces overshoot, and eliminates steady-state error more effectively than fixed PID and conventional anti-windup PID controllers. CONCLUSION: The proposed online GA–PID method is suitable for energy systems with high variability and adaptive control requirements, especially in wind power generation applications.

We show that a class of NJL-like models fails to reproduce the expected conformal limit of the speed of sound, making them unsuitable for analyzing the equation of state of dense matter. We then demonstrate how this issue can be resolved within a simple dynamical quark model. Abstract Published by the Jagiellonian University 2025 authors

This article presents a simple dynamic model that allows students to determine when economic growth is compatible with environmental sustainability. The model captures two key forces: the slowing of economic growth and the decoupling of economic growth from environmental damage. The model highlights a key observation that students often find counterintuitive: economic growth is neither a necessary nor a sufficient condition for crossing a sustainability target, such as keeping global average surface temperatures from rising more than 1.5 °C above pre‑industrial levels. The model is solved by taking the sum of a geometric series. It is designed for undergraduate courses that do not require calculus, but could be applicable in a wider range of settings.

Background: The rapid rise in antimicrobial resistance has become one of the 10 most pressing health problems worldwide in recent years. Antibiotic stewardship offers hope in the fight against antibiotic resistance, but it is currently still falling short of expectations. A better understanding of the dynamics of the interaction between antibiotic consumption and the emergence and spread of resistance is urgently needed. Methods: We discuss a simple dynamic model based on a differential equation to describe the increase in the proportion of a pathogen’s antimicrobial resistance to an antibiotic as a function of the time-dependent consumption of that antibiotic. Furthermore, we investigate the association of heterogeneity in the consumption of antibiotics with the rate of resistant pathogens. Data basis is the hospital information system and the patient data-management system of a German hospital, restricted to the intensive care unit. To quantify heterogeneity, we discuss and compare different entropy measures. Results: For some pathogen–antibiotic pairs, the consumption-dependent dynamic model for the growth in the proportion of antimicrobial resistance provides acceptable predictions, while for others, the model is less suitable. Cross-resistance and complex interactions with other pathogens and antibiotics may be responsible for this, suggesting that the observed dynamic behavior should be complementary, described using heterogeneity models. Time courses of Shannon entropy, the Antibiotic Heterogeneity Index, and the negative Gini Index correlate positively with the time series of the resistance rate. Thus, an increase in heterogeneity correlates with a decreasing resistance rate. However, a time-delayed cross-correlation of a differential entropy measure with resistance share suggests a functional dependence that can be utilized for antibiotic stewardship. Conclusions: Evidence is provided that the amount of consumption of certain antibiotics drives the corresponding proportions of pathogens’ resistance to these antibiotics; however, the model predictions of these univariable models are generally not sufficiently good, pointing to a more complex interaction dynamics. Therefore, we switch to the level of structural features and show that the degree of constantly mixing of the shares of antibiotic consumption has a control function regarding the incidence of resistance. Controlling differential consumption heterogeneity, therefore, appears to be a feasible operational basis for antibiotic stewardship. Experimental studies are demanded to identify functional dependencies; however, the integration of clinical expertise with model-based prediction appears to be a feasible antibiotic stewardship strategy.

Abstract We present an updated reconstruction of the dark energy equation of state, w(a), using the newly released DESI DR2 Baryon Acoustic Oscillation (BAO) data in combination with Pantheon+ and DES5Y Type Ia supernovae measurements, respectively. Building on our previous analysis, which employed a non-parametric flexknot reconstruction approach, we examine whether the evidence for dynamical dark energy persists with the improved precision of the DESI DR2 dataset. We find that while the overall qualitative structure of w(a) remains consistent with our earlier findings, the statistical support for dynamical dark energy is reduced when considering DESI DR2 data alone, particularly for more complex flexknot models with higher numbers of knots. However, the evidence for simpler dynamical models, such as wCDM and CPL (which correspond to n = 1 and n = 2 knots respectively), increases relative to ΛCDM with DESI DR2 alone, with CPL being the preferred dynamical model, consistent with previous DESI analyses. When combined with Pantheon+ data, the conclusions remain broadly consistent with our earlier work, but when instead combined with DES5Y supernovae data, there is an increased preference for flexknot models for all values of n considered. This results in all such models being preferred over ΛCDM, with the CPL model being the most favoured by a Bayes factor of ∼2.3 relative to ΛCDM.

Abstract In the field of complex industrial control, the leveling task of multi-cylinder hydraulic presses imposes stringent requirements on control accuracy and system stability. Traditional control methods struggle to balance the performance and stability when facing unknown models due to their reliance on precise system modeling. In contrast, reinforcement learning optimizes policies through autonomous interaction, achieving both model-agnostic capability and multi-scenario adaptability. Based on the simplified dynamic model of the multi-cylinder hydraulic press, this study proposes a new control strategy based on reinforcement learning. The approach integrates the soft actor–critic (SAC) algorithm with Lyapunov constraints and state-error integral compensation for leveling control. Embedding Lyapunov constraints within SAC ensures system stability, while the integral compensation minimizes steady-state error and enhances precision. Experimental results demonstrate that—under simplified modeling assumptions—the proposed method retains SAC’s inherent advantages while significantly improving stability and leveling accuracy in specific complex scenarios (e.g., model-defined disturbances). By merging classical control theory with modern machine learning, this work offers new insights for designing reinforcement-learning controllers in complex settings and establishes a foundation for future validation on more realistic physical models. It aims to provide a reference for potential industrial deployment.

ABSTRACT Current potato seed cutting processes suffer from low automation and poor cutting quality, primarily due to the lack of accurate and adaptive cutting mechanisms. This study presents the first application of a Delta parallel manipulator for automated potato seed cutting. A high‐speed cutting apparatus was designed, modeled, and experimentally validated. The key physical and mechanical properties of the “Holland 15” variety were first measured to establish the design basis. Comprehensive kinematic and simplified dynamic models were then developed to verify the manipulator's theoretical feasibility. A Response Surface Methodology (RSM) based on a Box–Behnken Design (BBD) was employed to analyze the influence of three key factors—conveyor speed, cutting speed, and belt tension—on system performance, characterized by cutting efficiency and qualification rate. Analysis of Variance (ANOVA) confirmed that both second‐order models were statistically significant ( p < 0.0001). Multi‐objective optimization determined the optimal parameters: 0.25 m/s conveyor speed, 2.5 m/s cutting speed, and 15 N belt tension. Verification experiments under these conditions achieved an average cutting efficiency of 417.52 kg/h and a qualification rate of 89.56%, with prediction errors below 2%, validating the model's high accuracy and reliability. Although the current system performs excellently under laboratory conditions, its robustness under variable field environments and across different potato varieties remains to be verified. Future work will integrate machine vision for adaptive bud‐eye detection, extend testing to multiple cultivars, and assess industrial scalability. The study confirms the feasibility and superiority of Delta‐based dynamic cutting, providing a solid technical foundation for intelligent seed‐cutting equipment.

Abstract Characterizing the critical circumferential angle of total pressure inlet distortion, where compressor performance and stability are degraded, is of paramount importance for gas turbine inlet integration. This effort utilizes steady and transient full-annulus simulations to characterize the critical angle of total pressure inlet distortion of the single stage centrifugal compressor (SSCC) at Purdue University. This is a high-specific speed centrifugal compressor with a vaned diffuser. A method of tracking distortion through the impeller utilizing rothalpy is presented. A comparison of reduced frequency methods to determine the critical distortion extent and the physical mechanism behind this has been conducted. Both integrated and approximate methods of calculating the reduced frequency of inlet distortion are presented. The critical angle of distortion for this compressor correlates with the acoustic propagation time through the rotor. At the critical distortion extent, a stationary stall cell was developed in the diffuser leading to a reduction in stage efficiency and total pressure ratio (TPR). The stalled diffuser vane correlated to the acoustic propagation location of the ingested distortion. The critical acoustic reduced frequency for this centrifugal compressor was unity. This allowed for use of a single passage steady model compared to the full-annulus transient to assess the potential for a simplified computational fluid dynamics (CFD) model to estimate the critical angle with less computational cost. The critical angle of distortion calculated from the steady single passage model was within 0.2 deg to that of the full-annulus transient simulations.

Simplified dynamic models and harmonics relationships of hybrid distribution transformer

Abstract Based on an example of a four dimensional ecological community composed of three ant species and a group of phorid flies, a simplified dynamic model is proposed and analyzed qualitatively. The model contains an intransitive triplet of ant species coupled with a predator/prey pair, where the prey is one of the ants in the intransitive competition, thus coupling a 3D oscillator with a 2D oscillator. Relaxing basic symmetries, a chaotic attractor emerges in which two distinct unstable modalities are recognizable, identifiable with the two biological processes involved. The unstable manifolds of each of the instabilities are of distinct dimensionalities, a characteristic known as hetero-chaos. It is suggested that the qualitative arrangement may be common as a modality within large ecological systems, thus proposing hetero-chaos as possibly common in ecosystems that have chaotic behavior.

Magnetic Resonance-Based Hemodynamic Model in Symptomatic Intracranial Atherosclerotic Disease.

A topology-based simplified dynamic model and solving algorithm for LCC-HVDC converters considering commutation failure

Simplified dynamics models cannot accurately describe actual vehicle behavior under different road conditions, while complex models significantly reduce the computational efficiency of model predictive control (MPC). To obtain accurate vehicle dynamics models, this paper proposes an online Levenberg-Marquardt neural network (OLMNN) with a two-stage learning strategy. In the first stage, the OLMNN is trained offline using simulated data to capture the basic vehicle dynamics characteristics. In the second stage, an adaptive online Levenberg-Marquardt (OLM) algorithm is employed to collect real-time state data and update the network parameters in real time, enabling the model to adapt to nonlinear vehicle characteristics. A feedforward-feedback control strategy is designed to enhance computational efficiency. The feedforward control constructs an optimization problem based on the OLMNN to generate steady-state steering angles, while the feedback MPC focuses on correcting errors using a simplified predictive model. This control strategy avoids the computational burden of incorporating complex models into MPC while improving control accuracy. Simulations and hardware-in-the-loop (HIL) experiments validate that the proposed control strategy achieves excellent tracking performance and computational efficiency under various driving conditions.

As plants grow taller, increasing conductive pathlength imposes hydraulic resistance, challenging the maintenance of water transport to leaves. While tip-to-base conduit widening along the stem helps mitigate this resistance, theoretical models and empirical data suggest that stem widening alone is insufficient to fully compensate. Here, we explore whether leaf length could contribute to maintaining hydraulic conductance by influencing vessel diameters in the stem. Across a diverse set of angiosperm species, we found that leaf length strongly predicts vessel diameter at the petiole base, and that petiole vessel diameter, in turn, scales positively with vessel diameter at the twig tip. These relationships imply that longer leaves are associated with wider conduits in the stem, potentially boosting stem-wide permeability. Simple fluid dynamic models show that the steep rate of conduit widening in angiosperm leaves plausibly buffers the resistance costs of increased leaf length. Because vessel diameter scales with the fourth power of conductance, modest increases in leaf length, and thus stem conduits, could lower the resistance not buffered by conduit widening in the stem. Leaf length during height growth may serve as a key mechanism in maintaining hydraulic supply, complementing conduit widening in the stem.

Economic development hinges on structural change, that is, transformations in what an economy produces. The field of economic complexity has investigated this process through two related but distinct branches: one studying how economies diversify, the other how the complexity of an economy is reflected in its output. However, a formal connection between these approaches, and their relationship to classic accounts of structural transformation (for example, from agriculture to manufacturing), remains unclear. Here we introduce a simple dynamical model that links these perspectives through one core idea: economies diversify preferentially into activities related to those they already do. Studying this model yields three main results: It generates quantities resembling economic complexity metrics, suggests these metrics summarize long-term structural change rather than directly infer an economy’s complexity, and reproduces stylized facts of development. Our framework formally connects the field’s conceptual strands, bridges short and long timescales of change, and adds granularity to classic descriptions of development.

In order to reveal the coupling mechanism of a suspended permanent magnet levitation (SPML) train-track-bridge system, this paper adopts multi-body dynamics and finite element methods to establish a coupled model of the SPML train-track-bridge system. For the track–bridge coupling method, two mechanical modeling approaches have been proposed: the nodal coupling and force element connection models. Through a comparative analysis between the on-site experimental results and simulation data, the reliability and validity of the proposed coupled model were verified. The research results indicate that the data obtained from the two modeling methods are consistent. Compared with the force element connection method, the nodal coupling method simplified the modeling process, enhanced computational efficiency and reduced computing time. For the optimization of the bogie structure, the top-mounted magnetic rail significantly reduced the vibration responses of the train and track girder, lowering the lateral vibration frequency in the range of 20-30 Hz. Additionally, the top-mounted magnetic rail optimized the lateral force between the guide wheel and the track girder, effectively reducing the roll motion of the bogie and enhancing the train’s stability.

Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations

BackgroundAccrual of participants into clinical trials is a fundamental and important aspect for the management of trial progress. Monitoring of trial accrual often provides insight into the potential timing of key study events, such as interim analysis, as well as the feasibility of the overall trial to enroll the projected sample size in the original estimated timeline.MethodsA Bayesian first order simple dynamic linear model with weakly informative priors is utilized to characterize enrollment rates temporally within pre-defined time windows (quarterly) for the duration of a trial.ResultsApplication of the model to three ongoing clinical trials demonstrates the utility of the model to characterize the observed accrual patterns. Additionally, the applications demonstrate the flexibility of the model to react to variable accrual patterns without overreacting to the variability of accrual within a trial due to expected causes, such as seasonal variability in disease incidence, or unexpected causes, such as a global pandemic.ConclusionsMuch statistical literature has been dedicated to predicting when key study events are likely to occur by utilizing current estimated rates of participant accrual; however, study teams, sponsors, and funding agencies have interest in the previous trends in participant accrual. This work presents an addition to the literature which allows parties interested in assessing trial progress to do so by providing a flexible framework for the standardized characterization of trial accrual which is not overly sensitive to the expected variability of trial accrual.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13063-025-08748-3.

Combining data assimilation of states and parameters for more precise infectious disease prediction☆