Real-time Dynamics Research Articles

Real-time mode dynamics of Stokes beam in multimode Raman fiber laser with mode-selective mirror

Real-time mode dynamics of Stokes beam in multimode Raman fiber laser with mode-selective mirror

Holotomographic microscopy reveals label-free quantitative dynamics of endothelial cells during endothelialization.

Holotomographic microscopy reveals label-free quantitative dynamics of endothelial cells during endothelialization.

Universal Quantum Dynamics of Bose Polarons

Predicting the emergent properties of impurities immersed in a quantum bath is a fundamental challenge that can defy quasiparticle treatments. Here, we measure the spectral properties and real-time dynamics of mobile impurities injected into a weakly interacting homogeneous Bose-Einstein condensate, using two broad Feshbach resonances to tune both the impurity-bath and intrabath interactions. For attractive impurity-bath interactions, the impurity spectrum features a single branch, which away from the resonance corresponds to a well-defined attractive polaron; near the resonance, we observe dramatic broadening of this branch, suggesting a breakdown of the quasiparticle picture. For repulsive impurity-bath interactions, the spectrum features two branches: the attractive branch that is dominated by excitations with energy close to that of the Feshbach dimer, but has a many-body character, and the repulsive polaron branch. Our measurements show that the behavior of impurities in weakly interacting baths is remarkably universal, controlled only by the bath density and a single dimensionless interaction parameter. Published by the American Physical Society 2025

Systematic mapping of methods for real-time capable multibody simulation of road vehicles using PRISMA

Abstract Road vehicle dynamics studies the performance of road vehicles to improve their driving characteristics using the principles of multibody system dynamics. The growing importance of virtual design methods, especially of real-time applications like driving simulators, raises the demand for more accurate real-time vehicle models. This paper reports a systematic mapping, based on the PRISMA methodology, of scientific publications from 1999 to 2024 presenting real-time vehicle dynamics models and their underlying methods. It includes vehicle models with lumped-mass, lookup-table and multibody-based suspensions, as well as standalone multibody suspension models. Literature was retrieved from Scopus, Web of Science and Google Scholar using keywords targeting real-time multibody simulation of vehicles and suspensions. From a total of 765 records, 109 eligible publications were analyzed in detail. The main findings are: (a) most of the publications consider multibody suspensions and focus on the research of related real-time methods; publications considering models with lumped-mass and lookup-table suspensions mainly focus on their real-time application; (b) the suspension elastokinematics is neglected in most of the multibody suspension models; if included, then mainly through bushing elements, with body flexibility rarely considered; (c) real-time capability can be achieved with various equations of motion formulation and integration methods, depending on the characteristics of the multibody suspension model; (d) diverse model simplifications and approaches in the equations of motion formulation, integration and implementation aim to reduce computation times. This study provides an overview of real-time vehicle dynamics models and methods with detailed explanation of the findings.

Direct Nano-Imaging Reveals the Underestimated Role of Lanthanum Phosphate Formation in Phosphorus Sequestration by Lanthanum Carbonate.

Lanthanum-based materials are recognized as highly effective adsorbents for advanced phosphorus removal, with a prevailing belief that acidic conditions promote phosphorus uptake via enhanced surface complexation. Herein, we demonstrate that lanthanum carbonate exhibits a 1.8-fold higher phosphate adsorption capacity at pH 7 (33.2 mg-P/g) compared to pH 4 (18.5 mg-P/g), attributed to the enhanced formation of LaPO4 nanocrystals. Leveraging in situ atomic force microscopy (AFM), we resolve real-time phosphorus sequestration dynamics, capturing LaPO4 nucleation within minutes, contradicting prior reports that LaPO4 formation is time-intensive. This discrepancy arises because conventional characterization techniques (e.g., X-ray diffraction) overlook transient amorphous LaPO4 intermediates due to insufficient sensitivity, whereas the nanoscale resolution of AFM directly tracks interfacial transformations. A dissolution-nucleation-growth mechanism was then proposed for the interfacial formation of LaPO4. This study revises the mechanistic framework for phosphorus removal and highlights the crucial role of LaPO4 formation in maximizing the utilization efficiency of lanthanum active sites for enhanced phosphate sequestration by La-based materials.

VTX: Real-time high-performance molecular structure and dynamics visualization software.

VTX is a molecular visualization software capable to handle most molecular structures and dynamics trajectories file formats. It features a real-time high-performance molecular graphics engine, based on modern OpenGL, optimized for the visualization of massive molecular systems and molecular dynamics trajectories. VTX includes multiple interactive camera and user interaction features, notably free-fly navigation and a fully modular graphical user interface designed for increased usability. It allows the production of high-resolution images for presentations and posters with custom background. VTX design is focused on performance and usability for research, teaching and educative purposes. VTX is open source and free for non commercial use. Builds for Windows and Ubuntu Linux are available at http://vtx.drugdesign.fr. The source code is available at https://github.com/VTX-Molecular-Visualization. A video displaying free-fly navigation in a whole-cell model is available.

Label-Free Quantification of Nanoplastic-Cell Membrane Interaction by Single Cell Deformation Plasmonic Imaging.

Nanoplastics are a growing environmental concern due to their potential to disrupt cellular functions. Understanding how these particles interact with cell membranes is crucial for assessing their biological effects. In this study, we present a label-free, quantitative method─Single Cell Deformation Plasmonic Imaging (SCDPI)─to measure real-time membrane interaction dynamics at the single-cell level. By examining both fixed and live cells, we characterized the binding behaviors of nanoplastics with varying sizes, surface chemistries, and materials. Our findings show that nanoplastic binding induces cell membrane deformation ranging from a few to tens of nanometers, depending on nanoplastic type and concentration (0-250 μg/mL), influencing membrane-surface interactions. This work provides new mechanistic insights into nanoplastic-cell interactions, demonstrating the potential of SCDPI as a powerful tool for evaluating the cellular impacts of environmental pollutants.

Urban Wind Field Effects on the Flight Dynamics of Fixed-Wing Drones

Urban wind, and particularly turbulence present in the roughness zone near structures, poses a critical challenge for next-generation drones. Complex flow patterns induced by large buildings produce significant disturbances that the vehicle must reject at low altitudes. Traditional turbulence models, such as the von Kármán model, underestimate these localized effects, compromising flight safety. To address this gap, we integrate high-resolution time and spatially varying urban wind fields from Large Eddy Simulations into a flight dynamics simulation framework using vehicle plant models based on configuration geometry and commonly deployed Ardupilot control laws, enabling a detailed analysis of drone responses in urban environments. Our results reveal that high-risk flight zones can be systematically identified by correlating drone response metrics with the spatial distribution of Turbulent Kinetic Energy (TKE). Notably, maximum g-loads coincide with abrupt TKE transitions, underscoring the critical impact of even short-lived wind fluctuations. By coupling advanced computational fluid dynamics with a real-time vehicle dynamics model, this work establishes a foundational methodology for designing safer and more reliable advanced air mobility platforms in complex urban airspaces. This work distinguishes itself from the existing literature by incorporating an efficient vortex lattice aerodynamic solver that supports arbitrary fixed-wing drone platforms through the simple specification of planform geometry and mass properties, and operating full-flights throughout a time and spatially varying urban wind field. This framework enables a robust assessment of stability and control for a wide range of fixed-wing drone platforms operating in urban environments, with delivery drones serving as a representative and practical use case.

Emergent hydrodynamic mode on SU(2) plaquette chains and quantum simulation

We search for emergent hydrodynamic modes in real-time Hamiltonian dynamics of 2+1-dimensional SU(2) lattice gauge theory on a quasi-one-dimensional plaquette chain, by numerically computing symmetric correlation functions of energy densities on lattice sizes of about 20 with the local Hilbert space truncated at jmax=12. Because of the Umklapp processes, we only find a mode for energy diffusion. The symmetric correlator exhibits transport peak near zero frequency with a width approximately proportional to momentum squared at small momentum, when the system is fully quantum ergodic, as indicated by the eigenenergy level statistics. This transport peak leads to a power-law t−12 decay of the symmetric correlator at late time, also known as the long-time tail, as well as diffusionlike spreading in position space. We also introduce a quantum algorithm for computing the symmetric correlator on a quantum computer and find it gives results consistent with exact diagonalization when tested on the IBM emulator. Finally we discuss the future prospect of searching for the sound modes. Published by the American Physical Society 2025

Toward a direct measurement of partial restoration of chiral symmetry at J-PARC E16 via density-induced chiral mixing

The degeneracy of chiral partners is an ideal signal for measuring the restoration of the spontaneously broken chiral symmetry in QCD. In this work we investigate the observability of the ϕ - f1(1420) degeneracy in the J-PARC E16 experiment, which measures dielectrons emitted from 30-GeV pA collisions. We for this purpose make use of an effective Lagrangian approach, which naturally incorporates the broken charge-conjugation symmetry in nuclear matter and the ensuing anomaly-induced mixing between vector and axial-vector mesons, to compute the spectral function relevant for the experimental measurement. The real-time dynamics of the pA collision is obtained from a transport simulation. Including experimental background and resolution effects on top of that, we find that a signal of the ϕ - f1(1420) mixing can be observed at around 2.5 σ, with the Run2 statistics planned for the J-PARC E16 experiment with an ideal mixing strength. Published by the American Physical Society 2025

Continuous psychophysics: past, present, future.

Continuous psychophysics: past, present, future.

Synchronous Oscillation Suppression in Grid-Forming Converters Using Ultra-Local Model Predictive Control

Grid-forming (GFM) converters play a vital role in future power systems due to their ability to independently establish voltage and frequency. However, their interaction with AC circuits may give rise to synchronous oscillations, which pose a threat to system stability and dynamic performance. This paper investigates the issue of synchronous oscillations and proposes an ultra-local model predictive control strategy for their suppression. First, a small-signal power dynamic model is developed to analyze the mechanism behind these oscillations. It is revealed that this problem is related to the electromagnetic dynamics of power transfer and is strongly influenced by the line impedance characteristics. Then, a predictive control framework is formulated, which incorporates oscillation suppression into the control objective and enables the real-time optimization of the active power reference. To avoid reliance on detailed system models, an ultra-local modeling approach is introduced. In this framework, a fixed-time sliding mode observer is employed to estimate the system power dynamics in real time, enabling the prediction of future states without requiring grid-side parameters and facilitating the design of a model-free controller. Simulation results verify that the proposed method effectively mitigates synchronous oscillations while significantly enhancing system stability and robustness.

Optimization of border gateway routing protocol with Lagrange multiplier and gradient descent integration for network

This study has a research object, namely data transmission lines. In this study, there are problems that must be solved related to the optimization of network transmission routes that are dynamic and adaptive to changes in real-time conditions, including latency factors, connection stability, and algorithm integration that can accommodate large-scale network needs efficiently in terms of transmission. The results obtained from this study are in the form of a model that can identify route management and optimize the border gateway protocol. The results of the study show that the application of this method can optimize the transmission path by considering network constraints and real-time condition dynamics. This study has an interpretation that the proposed model is proven to be effective in improving network performance, with increased efficiency, reduced constraints, and the ability to adapt to changes in network conditions. This is evidenced by the accuracy in the form of quantitative effectiveness by producing 95 % accuracy with the Reinforcement Learning model, able to significantly increase efficiency and accuracy compared to traditional methods in BGP routing optimization. The characteristics contained in this study include the ability to manage and identify transmission routes to improve network efficiency, reduce latency, increase throughput, minimize the number of hops in managing BGP transmission routes. There are limitations related to input data processing that require deeper annotation. This study contributes to BGP route optimization with machine learning algorithms that can be applied in complex and dynamic networks

A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems

In recent years, routing problems have attracted significant attention in the fields of operations research and computer science due to their fundamental importance in logistics and transportation. However, most existing learning-based methods employ simplistic context embeddings to represent the routing environment, which constrains their capacity to capture real-time visitation dynamics. To address this limitation, we propose a deep reinforcement learning-based decision-making framework (DRL-DM) built upon an encoder–decoder architecture. The encoder incorporates a batch normalization fronting mechanism and a gate-like threshold block to enhance the quality of node embeddings and improve convergence speed. The decoder constructs a dynamic-aware context embedding that integrates relational information among visited and unvisited nodes, along with the start and terminal locations, thereby enabling effective tracking of real-time state transitions and graph structure variations. Furthermore, the proposed approach exploits the intrinsic symmetry and circularity of routing solutions and adopts an actor–critic training paradigm with multiple parallel trajectories to improve exploration of the solution space. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate that DRL-DM consistently outperforms heuristic and learning-based baselines, achieving up to an 8.75% reduction in tour length. Moreover, the proposed method exhibits strong generalization capabilities, effectively scaling to larger problem instances and diverse node distributions, thereby highlighting its potential for solving complex, real-life routing tasks.

Improving Influenza Nomenclature Based on Transmission Dynamics.

Influenza A viruses (IAVs) evolve rapidly, exhibit zoonotic potential, and frequently adapt to new hosts, often establishing long-term reservoirs. Despite advancements in genetic sequencing and phylogenetic classification, current influenza nomenclature systems remain static, failing to capture evolving epidemiological patterns. This rigidity has led to delays or misinterpretations in public health responses, economic disruptions, and confusion in scientific communication. The existing nomenclature does not adequately reflect real-time transmission dynamics or host adaptations, limiting its usefulness for public health management. The 2009 H1N1 pandemic exemplified these limitations, as it was mischaracterized as "swine flu" despite sustained human-to-human transmission and no direct pig-to-human transmission reported. This review proposes a real-time, transmission-informed nomenclature system that prioritizes host adaptation and sustained transmissibility (R0 > 1) to align influenza classification with epidemiological realities and risk management. Through case studies of H1N1pdm09, H5N1, and H7N9, alongside a historical overview of influenza naming, we demonstrate the advantages of integrating transmission dynamics into naming conventions. Adopting a real-time, transmission-informed approach will improve pandemic preparedness, strengthen global surveillance, and enhance influenza classification for scientists, policymakers, and public health agencies.

Extracellular polymeric substances in indigenous microalgal-bacterial consortia: advances in characterization techniques and emerging applications.

Extracellular polymeric substances (EPS) synthesized by indigenous microalgal-bacterial consortia (IMBC) play multifunctional roles in enhancing wastewater treatment efficiency, nutrient sequestration, and ecological system stability. This comprehensive review critically evaluates state-of-the-art analytical methods for characterizing EPS composition, physicochemical properties, and functional dynamics, including colorimetry, Fourier-transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and confocal laser scanning microscopy (CLSM). While these methods provide critical insights into EPS structure-function relationships, challenges persist in resolving spatial heterogeneity, real-time secretion dynamics, and molecular-scale interactions within complex IMBC systems. Emerging technologies such as expansion microscopy (ExM), electrochemical impedance spectroscopy (EIS), and integrated multi-omics approaches are highlighted as transformative tools for in situ EPS profiling, offering nanoscale resolution and temporal precision. By synthesizing these innovations, this review proposes a multidisciplinary framework to decode EPS-mediated microbial symbiosis, optimize IMBC performance, and advance applications in sustainable bioremediation, bioenergy, and circular resource recovery.

Light-driven interlayer propagation of collective mode excitations in layered superconductors

Superconductors exhibit a nonlinear interaction with an applied light, which can resonantly excite the collective amplitude (Higgs) mode. Here we study light-induced dynamics of layered superconductors, where each layer is coupled to adjacent layers via the Josephson coupling and the first few layers near the surface are driven by an in-plane-polarized light. We study the system under the assumption that the interlayer Coulomb interactions are sufficiently screened out and that the phase-difference mode becomes available in the low-energy regime. We find that interlayer transport is induced via excitations of the collective amplitude and phase-difference modes, even when the applied electric field is parallel to the planes. We provide analytic calculations as well as numerical simulations of the real-time dynamics and investigate the influence on the light-induced interlayer Josephson current and intralayer third-harmonic generation. Published by the American Physical Society 2025

A Novel Framework for Commercial Loan Pricing and Risk Assessment Using Systemic Time Elasticity, Temporal Liquidity Distortion, and Recursive Economic Resilience

Traditional loan-pricing frameworks assume linear risk and stable markets and fail to capture shifts in borrower resilience during volatility. This study introduces three corrective models: • STEM forecasts the number of months until a borrower’s cash flow exceeds debt obligations using operational data and sector volatility, enabling lenders to preempt distress with timely term adjustments. • TLDM revalues loans hourly, adapting to shifts in borrower liquidity and market funding costs, ensuring that pricing reflects real-time market dynamics. • The RERF scores survival odds after repeated stress events— such as covenant breaches or rate spikes—by weighing leverage, reserves, and distress history, guiding banks to allocate capital efficiently. Tested on 14 historical corporate loans, these models reduce default misclassification by 32% and improve valuation accuracy by 26% over RAROC and IRB. They align with Basel III by improving risk-weighted asset classification and with IFRS 9 by refining forward-looking provisioning and impairment staging. To operationalize the models, we integrated advanced machine learning. XGBoost reduced the parameter calibration error by 15%, improving the STEM forecast accuracy by 10%. LSTM networks identified borrower distress 20% earlier, cutting false negatives in RERF by 12%, and reducing TLDM's response lag during liquidity shocks. The SHAP explanations ensured regulatory transparency and auditability. Each model has defined limitations. STEM overestimates recovery timelines for pre-revenue firms by up to six months. TLDM underreacts to liquidity events when data lags and missing cash flow breaks by 12–24 hours. The RERF underweights systemic tail risk, missing 15% of correlated losses in the 2008-style simulations. These limitations guide future research. STEM should be tested on gig-economy cash flows to improve early stage borrower modeling. The RERF can be adapted for crypto-backed volatility. The TLDM can integrate geopolitical risk signals for real-time reactivity in cross-border lending. Beyond banks, these models can inform regulatory stress-testing standards, enabling supervisors to better identify systemic vulnerabilities and foster industry-wide resilience. With these advancements, STEM, TLDM, and RERF usher in a new generation of lending modelsbuilt to anticipate crises and price risk with adaptive precision.

Ratiometric Dual-Response Quantum Dot Spherical Nucleic Acid for Monitoring Viral Secondary Bacterial Infections.

Viral-bacterial coinfections present intricate pathologies that exacerbate disease progression and elevate mortality rates. Understanding the dynamic interplay between viruses and bacteria during coinfection is critical for developing effective therapeutic interventions. However, current diagnostic tools primarily rely on static detection methods, limiting their ability to monitor real-time infection dynamics. Here, we introduce a ratiometric, dual-responsive quantum dot spherical nucleic acid (QD-SNA) probe capable of simultaneously detecting viral- and bacterial-specific markers in vivo. This probe enables real-time monitoring of coinfections, as demonstrated in a mouse model of influenza virus (H1N1) and methicillin-resistant Staphylococcus aureus infection. By providing dynamic, visual insights into the coinfection process, the QD-SNA probe holds significant potential for preclinical drug screening and the diagnosis of respiratory pathogen infections.

Integrating fluid dynamics: leveraging Simscape for modelling and real-time control of continuous flow ohmic heater

ABSTRACT Ohmic heating (OH) is an innovative thermal processing technology known for its rapid and uniform heating capabilities. It presents several challenges due to its complex multi-physics nature, involving electrical, thermal, and fluid dynamics phenomena. The nonlinearity of OH systems, influenced by variations in electrical conductivity and temperature, makes physical modelling a necessity to capture these dynamics accurately. To address this, we developed a MATLAB model using the Simscape toolbox, designed to investigate the effects of OH technology under various physical conditions of food products. This model was validated against both an established mathematical model and a pilot scale continuous flow ohmic heater (CFOH), achieving a normalised mean square error (NRMSE) of ± 0.005 and a mean absolute percentage error (MAPE) of ± 5%. It effectively simulates temperature variations in response to changes in the thermophysical properties of food, facilitating real-time system dynamics evaluations. Additionally, the model explores significant factors such as electrical conductivity, product temperature, density, flow rate, and electric field strength. The successful application of this physical model underscores its potential in designing model-based control systems to enhance the efficiency and performance of the CFOH processes.