Simulation Of Models Research Articles

COBREXA 2: tidy and scalable construction of complex metabolic models.

Constraint-based metabolic models offer a scalable framework to investigate biological systems using optimality principles. Construction and simulation of detailed models that utilize multiple kinds of constraint systems poses a significant coding overhead, complicating implementation of new types of analyses. We present an improved version of the constraint-based metabolic modeling package COBREXA, which utilizes a hierarchical model construction framework that decouples the implemented analysis algorithms into independent, yet re-combinable, building blocks. By removing the need to re-implement modeling components, assembly of complex metabolic models is simplified, which we demonstrate on use-cases of resource-balanced models, and enzyme-constrained flux balance models of interacting bacterial communities. Notably, these models show improved predictive capabilities in both monoculture and community settings. In perspective, the re-usable model-building components in COBREXA 2 provide a sustainable way to handle increasingly complex models in constraint-based modeling. COBREXA 2 is available from https://github.com/COBREXA/COBREXA.jl, and from Julia package repositories. COBREXA 2 works on all major operating systems and computer architectures. Documentation is available at https://cobrexa.github.io/COBREXA.jl/. Supplementary data are available at Bioinformatics online.

Distributions of Wide Binary Stars in Theory and in Gaia Data. I. Generalized Ambartsumian (1937) Approach and the Family of Power-law Distributions of Eccentricity

Abstract The orbital parameter space of wide, weakly bound binary stars has been shaped by the still poorly known circumstances of their formation, as well as by subsequent dynamical evolution in parent clusters and in the field. The advance of the Gaia mission astrometry takes statistical studies of wide stellar systems to an unprecedented level of precision and scope. On the theoretical side of the problem, the old approach proposed by Jeans and developed by Ambartsumian is revisited here. It is shown how certain simplifying assumptions about the phase density of binary systems in the framework of general copula distributions can lead to a family of analytical representations for the marginal distribution of orbital eccentricity, including accommodating and flexible power-law models. We further demonstrate the application of these models in forward Monte Carlo simulations of the measured motion angles between the relative velocity and separation vectors on the example of 170 K listed Gaia binary systems, providing inference in the intrinsic distribution of orbital eccentricity.

Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations

Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations

Wall model for large eddy simulations accounting for particle effect

Wall model for large eddy simulations accounting for particle effect

Evaluating cohesive models in discrete element simulation through drawdown test with new assessment perspectives

Evaluating cohesive models in discrete element simulation through drawdown test with new assessment perspectives

Optimizing data-sampling period in a machine learning-based surrogate model for powder mixing simulations

Optimizing data-sampling period in a machine learning-based surrogate model for powder mixing simulations

Drivers of glacially induced fault reactivation in the Baltic Sea sector of the Tornquist Fan

We analyse the effect of Quaternary glaciations on the complex tectonic pattern within the southwestern Baltic Sea, a sector of the transition zone from the East European Craton to the West European Platform. This area comprises the Caledonian Trans–European Suture Zone in the south and the Tornquist Zone in the north. Multiple fault zones in between, with different strike and dip angles, and characters (normal, thrust/reverse, strike‐slip), document like scars the alternately transpressional and transtensional stress activities since the Palaeozoic. We determine the strike directions and dip angles of more than 40 potential glacially reactivated faults identified in 2D marine reflection seismic data. Finite element simulations of different glacial isostatic adjustment models provide glacially induced Coulomb failure stress changes (ΔCFS) at the faults over time, starting 200 000 years ago (200 ka, Saalian phase) up to 1000 years into the future. Assuming strike‐slip or thrust/reverse background stresses, a potential reactivation of each fault is analysed. The detected reactivation phases are related to the waxing and waning ice masses (Late Saalian ice advances: c. 170–135 ka ago; Weichselian ice advances: 70–60, 45–38, 26–14 ka ago) and point to an activation in front of the ice margin. Comparing the ΔCFS results of the individual faults laterally and over time, we found that the location of the fault, depending on its position during a glacial maximum, has an important effect on its reactivation potential. The closer a fault was located to the former ice margin, the higher was the glacially induced stress during the ice retreat. Based on earlier findings in Germany and Denmark, glacially triggered faults are a typical consequence of the Fennoscandian glaciation throughout northern central Europe, and this also applies to future glaciation phases.

A Brief Review on Hydrological Modelling

The interaction between water, climate, soil and land use is primary to the hydrological modelling concept. Hydrological models include spatial and temporal features. Hydrologists utilize hydrologic models as a primary tool for a variety of tasks including managing water resources, managing urban and rural areas, modelling ground water and more. In order to implement hydrologic models with ease, it is necessary to thoroughly comprehend their properties which have been developed and improved through the years. It is difficult to categorize hydrologic models precisely and various hydrologists may use different criteria. The reason is that while numerous models share common traits, the nature of the model is frequently the same. In this paper, the discussion starts with an introduction to ancient hydrology followed by the anthropogenic factors which directly or indirectly affect hydrological flows. Further the paper reviews hydrological modelling and the evolution of hydrologic models. The research aims to demonstrate classification of hydrologic models. The proper application of a model requires an in-depth understanding of it. In this research, eleven watershed hydrologic models were reviewed: ANN model, Unit Hydrograph, SCS-CN model, SVM model, HBV, TOPMODEL, PRMS, MIKESHE, VIC, HEC-HMS, MODFLOW and SWAT. SWAT model, one of the non-point source pollution models, is discussed along with its history and its major input variables. This literature also compiles and discusses applications of the SWAT model. SWAT was found to be promising models for long-term continuous simulations in agricultural watersheds. In agricultural watersheds, SWAT has been found to be the most assuring model for long-term continuous simulations.

Biomechanics of a Physically Impaired Individual from Early Medieval Ranelagh, Ireland

This research describes the methods used to re-create the biomechanics of a physically impaired middle-aged male, SK67, dated 893–1023 C.E., from the early medieval cemetery at Ranelagh, Co. Roscommon, Ireland, who experienced an antemortem femoral head fracture. A gap in research exists between the osteological evidence and the nature of the true biomechanical gait. Computer simulation of musculoskeletal models is an emerging engineering approach to estimate true gait patterns from geometric measurements of skeletal anatomy. This study paves the way for bridging the gap between osteological evidence and true biomechanics by obtaining information from SK67 and using it to build a three-dimensional musculoskeletal model of SK67 using OpenSim. Gait was then simulated using SCONE software (a musculoskeletal simulation optimization system to predict task-specificmotion), thereby revealing SK67’s likely limb function post-impairment. The patterns of habitual biomechanical stress are reflected in the quantity and distribution of cortical bone; based on this approach, the individual’s limbs were compared for signs of weight-bearing by analyzing cortical bone thickness from radiographs. The upper limbs were also examined for entheseal changes that might indicate the use of walking aids. The results of these interdisciplinary methods enabled the biomechanics of SK67 to be revealed and assessed how SK67 would have used walking aids to regain mobility and independence in the wake of their major trauma. The lack of arms on the musculoskeletal models may provide a limitation to this study, as well as the errors/learning curve in altering inputfiles to simulate the gait. This method opens new avenues for future research into reconstructing the biomechanics of individuals with physical impairments within archaeological contexts, across both time and location.

Model of the Venous System for Training Endovascular Treatment in Interventional Neuroradiology

Background: Endovascular treatment of venous disease is introducing new therapeutic options in neuroradiology. These procedures are technically challenging and require extensive physician training. Currently, training is mainly conducted on animal models, which presents drawbacks such as ethical concerns and anatomical differences from human vascular architecture. There is no training model that simulates treating intracranial venous disease using original instruments in a real angiography suite. Methods: This work presents the development of a venous system model for endovascular training simulations for integration into the existing Hamburg ANatomical NEurointerventional Simulator (HANNES) for arterial interventions. Results: The manufacturing process established at HANNES and the material used for the arterial vascular models were successfully transferred to the larger 3D-printed vein models. The application test was conducted in a real angiography suite with original instruments by an experienced neurointerventional physician to evaluate the system in terms of geometric mapping, flow, haptics and probing. Conclusion: This newly developed model provides a first approach to simulate an endovascular intervention in the venous system within the HANNES environment. Future expansions might include specific treatment simulations for conditions such as arteriovenous malformations, dural arteriovenous fistulas, sinus vein thrombosis and hydrocephalus.

Development of a Simplified Human Body Model for Movement Simulations

The main goal of this paper is to develop a simplified model of the human body motion system that enables its application for movement simulations and the analysis of the kinematic and dynamic parameters occurring during the performance of activities. The model is established on the basis of the modified Hanavan model and consists of rigid solids with simple geometry that are connected mostly with spherical joints. Based on anthropometric data from the literature, a complete set of equations parameterizing the dimensions and mass of each segment was formulated. The equations depend on only two body measurements (height and mass). The model is built in the Adams system as a 3D numerical dynamic model and tested using data gathered with an IMU sensors system. A volunteer lifting an object with a bent spine from the ground with both hands is used for this purpose. Three angles (from the IMUs) are applied to each model’s joint to best simulate human movement and to analyze the angular displacements, velocities, and torques. These results are consistent with theoretical expectations and assumptions, thus proving that reproducing human movements with the developed model is possible and that it also allows various parameters of the human body to be obtained.

FINITE ELEMENT MODELLING OF IMPEDANCE TUBE TEST

Impedance tubes are used to characterize porous acoustic materials. The report proposes a suitable model for numerical simulations of the test of materials in an impedance tube. Numerical simulations make it possible to generate synthetic data on the acoustic characteristics of the material when varying the parameters in the material model. These data are suitable for using machine learning algorithms as well as for solving inverse problems for identifying the material parameters in the mathematical model. The Johnson-Champoux-Allard model is used in the work, which is suitable for a wide class of porous acoustic materials.

Height Control Method for Air Suspension Systems Based on Model-Free Adaptive Control and Improved Genetic Algorithm

<div>This article presents a height control method for air suspension systems, which are influenced by strong nonlinearity and multiple coupling factors, based on model-free adaptive control (MFAC) using full-form dynamic linearization (FFDL). To address the impact of different damping coefficients of the shock absorber on the height control effect, an improved genetic algorithm is employed to globally optimize the relevant parameters involved in the design of the control law, thereby enhancing the height control performance. The precision of modeling the air suspension system has a direct impact on the simulation of both static and dynamic vehicle models, as well as the accuracy of height control. In this article, an equivalent thermodynamic model of the air suspension system is established based on the principle of energy conservation for height control research. Considering the nonlinearity of the air suspension system and the need to make additional assumptions before modeling, a MFAC method using FFDL is adopted for controller design. Traditional height control methods do not consider the impact of changes in the shock absorber damping coefficient on the height control effect. For different damping coefficients, the body height tracking error is large when using the same height control law initialization parameters. Therefore, an improved genetic algorithm is employed to globally optimize the MFAC parameters under different damping states. The effectiveness of the thermodynamic model of the air suspension system and the MFAC method for height control, with parameters tuned using the improved genetic algorithm, was validated through MATLAB/Simulink simulations.</div>

Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data

Abstract. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite developed by the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) launched in May 2024 carries a novel 94 GHz cloud profiling radar (CPR) with Doppler capability. This work describes the open-source instrument simulator Orbital-Radar, which transforms high-resolution radar data from field observations or forward simulations of numerical models to CPR primary measurements and uncertainties. The transformation accounts for sampling geometry and surface effects. We demonstrate Orbital-Radar's ability to provide realistic CPR views of typical cloud and precipitation scenes. The presented case studies show small-scale convection, marine stratus clouds, and Arctic mixed-phase cloud cases. These results provide valuable insights into the capabilities and challenges of the EarthCARE CPR mission and its advantages over the CloudSat CPR. Finally, Orbital-Radar allows for evaluating kilometre-scale numerical weather prediction models with EarthCARE CPR observations. So, Orbital-Radar can generate calibration and validation (Cal/Val) data sets already pre-launch. Nevertheless, an evaluation of synthetic CPR output data to accurate EarthCARE CPR data is missing.

AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE

Abstract. The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) is a newly developed aerosol optical module that improves the representation of aerosol optical properties for radiative transfer simulations in atmospheric models. It incorporates the nonsphericity and inhomogeneity (NSIH) of internally mixed aerosol particles through a deep learning method. Specifically, the AI-NAOS considers black carbon (BC) to be fractal aggregates and models soil dust (SD) as super-spheroids, encapsulated partially or completely with hygroscopic aerosols such as sulfate, nitrate, and aerosol water. To obtain AI-NAOS, a database of the optical properties for the models was constructed using the invariant imbedding T-matrix method (IITM), and deep neural networks (DNN) were trained based on this database. In this study, the AI-NAOS was integrated into the mesoscale version 5.1 of Global/Regional Assimilation and Prediction System with Chinese Unified Atmospheric Chemistry Environment (GRAPES_Meso5.1/CUACE). Real-case simulations were conducted during a winter with high pollution, comparing BC aerosols evaluated using three schemes with spherical aerosol models (external-mixing, core-shell, and volume-mixing schemes) and the AI-NAOS scheme. The results showed that the NSIH effect led to a moderate estimation of absorbing aerosol optical depth (AAOD) and obvious changes in aerosol radiative effects, shortwave heating rates, temperature profiles, and boundary layer height. The AAOD values based on three spherical schemes were 70.4 %, 125.3 %, and 129.3 % over the Sichuan Basin, benchmarked to AI-NAOS results. Compared to the external-mixing scheme, the direct radiative effect (DRE) induced by the NSIH effect reached +1.6 W m−2 at the top of the atmosphere (TOA) and −2.9 W m−2 at the surface. The NSIH effect could enhance the shortwave heating rate, reaching 23 %. Thus, the warming effect at 700 hPa and the cooling effect on the ground were strengthened by 21 % and 13 %, reaching +0.04 and −0.10 K, which led to a change in the height of the planetary boundary layer (PBL) by −11 m. In addition, the precipitation was inhibited by the NSIH effect, causing a 15 % further decrease. Therefore, the NSIH effects demonstrated their non-negligible impacts and highlighted the importance of incorporating them into chemical weather models.

Benchmarking residue-resolution protein coarse-grained models for simulations of biomolecular condensates.

Intracellular liquid-liquid phase separation (LLPS) of proteins and nucleic acids is a fundamental mechanism by which cells compartmentalize their components and perform essential biological functions. Molecular simulations play a crucial role in providing microscopic insights into the physicochemical processes driving this phenomenon. In this study, we systematically compare six state-of-the-art sequence-dependent residue-resolution models to evaluate their performance in reproducing the phase behaviour and material properties of condensates formed by seven variants of the low-complexity domain (LCD) of the hnRNPA1 protein (A1-LCD)-a protein implicated in the pathological liquid-to-solid transition of stress granules. Specifically, we assess the HPS, HPS-cation-π, HPS-Urry, CALVADOS2, Mpipi, and Mpipi-Recharged models in their predictions of the condensate saturation concentration, critical solution temperature, and condensate viscosity of the A1-LCD variants. Our analyses demonstrate that, among the tested models, Mpipi, Mpipi-Recharged, and CALVADOS2 provide accurate descriptions of the critical solution temperatures and saturation concentrations for the multiple A1-LCD variants tested. Regarding the prediction of material properties for condensates of A1-LCD and its variants, Mpipi-Recharged stands out as the most reliable model. Overall, this study benchmarks a range of residue-resolution coarse-grained models for the study of the thermodynamic stability and material properties of condensates and establishes a direct link between their performance and the ranking of intermolecular interactions these models consider.

Conversate: Supporting Reflective Learning in Interview Practice Through Interactive Simulation and Dialogic Feedback

Job interviews play a critical role in shaping one's career, yet practicing interview skills can be challenging, especially without access to human coaches or peers for feedback. Recent advancements in large language models (LLMs) present an opportunity to enhance the interview practice experience. Yet, little research has explored the effectiveness and user perceptions of such systems or the benefits and challenges of using LLMs for interview practice. Furthermore, while prior work and recent commercial tools have demonstrated the potential of AI to assist with interview practice, they often deliver one-way feedback, where users only receive information about their performance. By contrast, dialogic feedback , a concept developed in learning sciences, is a two-way interaction feedback process that allows users to further engage with and learn from the provided feedback through interactive dialogue. This paper introduces Conversate, a web-based application that supports reflective learning in job interview practice by leveraging large language models (LLMs) for interactive interview simulations and dialogic feedback. To start the interview session, the user provides the title of a job position (e.g., entry-level software engineer) in the system. Then, our system will initialize the LLM agent to start the interview simulation by asking the user an opening interview question and following up with questions carefully adapted to subsequent user responses. After the interview session, our back-end LLM framework will then analyze the user's responses and highlight areas for improvement. Users can then annotate the transcript by selecting specific sections and writing self-reflections. Finally, the user can interact with the system for dialogic feedback, conversing with the LLM agent to learn from and iteratively refine their answers based on the agent's guidance. To evaluate Conversate, we conducted a user study with 19 participants to understand their perceptions of using LLM-supported interview simulation and dialogic feedback. Our findings show that participants valued the adaptive follow-up questions from LLMs, as they enhanced the realism of interview simulations and encouraged deeper thinking. Participants also appreciated the AI-assisted annotation, as it reduced their cognitive burden and mitigated excessive self-criticism in their own evaluation of their interview performance. Moreover, participants found the LLM-supported dialogic feedback to be beneficial, as it promoted personalized and continuous learning, reduced feelings of judgment, and allowed them to express disagreement.

A Hybrid Data Decomposition and Deep Learning Approach for Solana Price Prediction Incorporating Market Factors

In this study, a hybrid data decomposition model for predicting the Solana (SOL) price is proposed, integrated by advanced signal processing and machine learning techniques. The model utilizes Variational Mode Decomposition (VMD) to decompose the Solana price series into 10 intrinsic modes, each representing different frequency components of the data. These 10 decomposed modes are then used as features, along with other relevant market factors, including Solana volume, halving index, Solana trend, and Bitcoin closing price. Then, the Bidirectional Long Short-Term Memory (BiLSTM) network is chosen to capture the complex temporal dependencies in these features and forecast future prices. To evaluate the efficacy of the approach, this study compares the hybrid models performance with a baseline LSTM model, which uses only raw historical price data. The results demonstrate that the hybrid model outperforms the benchmark model, showing higher predictive accuracy and robustness. A trading simulation for models was then conducted, and the findings indicate a significant potential for the experiment model to function as an effective trading support system, reinforcing its applicability in practical market environments. This work contributes to the field of cryptocurrency price prediction by offering a more sophisticated methodology that combines time series decomposition with deep learning techniques, providing valuable insights for traders and investors.

Lyman-α feedback prevails at Cosmic Dawn: implications for the first galaxies, stars, and star clusters

ABSTRACT Radiation pressure from Lyman-$\alpha$ (Ly$\alpha$) scattering is a potentially dominant form of early stellar feedback, capable of injecting up to $\sim 100 \, \times$ more momentum into the interstellar medium (ISM) than ultraviolet continuum radiation pressure and stellar winds. Ly$\alpha$ feedback is particularly strong in dust-poor environments and is thus especially important during the formation of the first stars and galaxies. As upcoming galaxy formation simulations incorporate Ly$\alpha$ feedback, it is crucial to consider processes that can limit it to avoid placing Lambda-cold dark matter in apparent tension with recent JWST observations indicating efficient star formation at Cosmic Dawn. We study Ly$\alpha$ feedback using a novel analytical Ly$\alpha$ radiative transfer solution that includes the effects of continuum absorption, gas velocity gradients, Ly$\alpha$ destruction (e.g. by $2p \rightarrow 2s$ transitions), ISM turbulence, and atomic recoil. We verify our solution for uniform clouds using extensive Monte Carlo radiative transfer (MCRT) tests, and resolve a previous discrepancy between analytical and MCRT predictions. We then study the sensitivity of Ly$\alpha$ feedback to the aforementioned effects. While these can dampen Ly$\alpha$ feedback by a factor $\lesssim \textrm {few} \times 10$, we find it remains $\gtrsim 5 - 100 \, \times$ stronger than direct radiation pressure and therefore cannot be neglected. We provide an accurate fit for the Ly$\alpha$ force multiplier $M_{\rm F}$, suitable for implementation in subgrid models for galaxy formation simulations. Our findings highlight the critical role of Ly$\alpha$ feedback in regulating star formation at Cosmic Dawn, and underscore the necessity of incorporating it into simulations to accurately model early galaxy evolution.

Performance Tuning of Polarizable Gaussian Multipole Model in Molecular Dynamics Simulations.

Molecular dynamics (MD) simulations are essential for understanding molecular phenomena at the atomic level, with their accuracy largely dependent on both the employed force field and sampling. Polarizable force fields, which incorporate atomic polarization effects, represent a significant advancement in simulation technology. The polarizable Gaussian multipole (pGM) model has been noted for its accurate reproduction of ab initio electrostatic interactions. In this study, we document our effort to enhance the computational efficiency and scalability of the pGM simulations within the AMBER framework using MPI (message passing interface). Performance evaluations reveal that our MPI-based pGM model significantly reduces runtime and scales effectively while maintaining computational accuracy. Additionally, we investigated the stability and reliability of the MPI implementation under the NVE simulation ensemble. Optimal Ewald and induction parameters for the pGM model are also explored, and its statistical properties are assessed under various simulation ensembles. Our findings demonstrate that the MPI-implementation maintains enhanced stability and robustness during extended simulation times. We further evaluated the model performance under both NVT (constant number, volume, and temperature) and NPT (constant number, pressure, and temperature) ensembles and assessed the effects of varying timesteps and convergence tolerance on induced dipole calculations. The lessons learned from these exercises are expected to help the users to make informed decisions on simulation setup. The improved performance under these ensembles enables the study of larger molecular systems, thereby expanding the applicability of the pGM model in detailed MD simulations.