Computational Model Research Articles

ID: 325822 Computational modelling of interpatient differences in neural recruitment during pudendal nerve stimulation

ID: 325822 Computational modelling of interpatient differences in neural recruitment during pudendal nerve stimulation

Research trends of computational toxicology: a bibliometric analysis.

Computational toxicology utilizes computer models and simulations to predict the toxicity of chemicals. Bibliometric studies evaluate the impact of scientific research in a specific field. A bibliometric analysis of the computational methods used in toxicity assessment was conducted on the Web of Science between 1977 and 2024 February 12. Findings of this study showed that computational toxicology has evolved considerably over the years, moving towards more advanced computational methods, including machine learning, molecular docking, and deep learning. Artificial intelligence significantly enhances computational toxicology research by improving the accuracy and efficiency of toxicity predictions. Generally, the study highlighted a significant rise in research output in computational toxicology, with a growing interest in advanced methods and a notable focus on refining predictive models to optimize drug properties using tools like pkCSM for more precise predictions.

Multiscale Fiber Remodeling in the Infarcted Left Ventricle using a Stress-Based Reorientation Law

The organization of myofibers and extra cellular matrix within the myocardium plays a significant role in defining cardiac function. When pathological events occur, such as myocardial infarction (MI), this organization can become disrupted, leading to degraded pumping performance. The current study proposes a multiscale finite element (FE) framework to determine realistic fiber distributions in the left ventricle (LV). This is achieved by implementing a stress-based fiber reorientation law, which seeks to align the fibers with local traction vectors, such that contractile force and load bearing capabilities are maximized. By utilizing the total stress (passive and active), both myofibers and collagen fibers are reoriented. Simulations are conducted to predict the baseline fiber configuration in a normal LV as well as the adverse fiber reorientation that occurs due to different size MIs. The baseline model successfully captures the transmural variation of helical fiber angles within the LV wall, as well as the transverse fiber angle variation from base to apex. In the models of MI, the patterns of fiber reorientation in the infarct, border zone, and remote regions closely align with previous experimental findings, with a significant increase in fibers oriented in a left-handed helical configuration and increased dispersion in the infarct region. Furthermore, the severity of fiber reorientation and impairment of pumping performance both showed a correlation with the size of the infarct. The proposed multiscale modeling framework allows for the effective prediction of adverse remodeling and offers the potential for assessing the effectiveness of therapeutic interventions in the future. Statement of SignificanceThe organization of muscle and collagen fibers within the heart plays a significant role in defining cardiac function. This organization can become disrupted after a heart attack, leading to degraded pumping performance. In the current study, we implemented a stress-based fiber reorientation law into a computer model of the heart, which seeks to realign the fibers such that contractile force and load bearing capabilities are maximized. The primary goal was to evaluate the effects of different sized heart attacks. We observed substantial fiber remodeling in the heart, which matched experimental observations. The proposed computational framework allows for the effective prediction of adverse remodeling and offers the potential for assessing the effectiveness of therapeutic interventions in the future.

Efficient computational model for particle capture by a heterogeneous filter

Efficient computational model for particle capture by a heterogeneous filter

Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data

Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the Allen–Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400 °C at an average rate of 30 °Cmin, traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal’s microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. These relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

The Bilingual Dual-path model

Abstract Experimental research has yielded many important psycholinguistic findings in bi-/multilingualism. However, cognitive computational models of sentence processing were limited to the single-language case until recently. In this methodological review, we discuss cognitive modelling work that uses the Bilingual Dual-path model to simulate experimental research on bilingual sentence production, processing, and development. As a detailed example of such work, we then report on new simulations conducted with the model. Finally, we suggest directions for future cognitive modelling research using this model.

ID: 336323 Computational modeling of novel electrode arrays for restoring somatosensory feedback in lower limb amputees

ID: 336323 Computational modeling of novel electrode arrays for restoring somatosensory feedback in lower limb amputees

Nanozyme-enhanced enzyme control: Computational Strategies for precise and sustained enzymatic activity in tissue engineering

Nanomaterials are revolutionizing tissue engineering by significantly enhancing enzyme-mediated chemical processes and cellular activities, thereby advancing the development of more effective therapeutic strategies. This research delves into the intricate dynamics of enzyme diffusion within tissue matrices, providing a comprehensive computational modeling framework aimed at optimizing enzyme concentration control in tissue scaffolds. Through advanced numerical algorithms and computational analysis, the study simulates optimal control strategies for regulating nanozyme levels, ensuring precise and sustained enzymatic activity within the scaffolds. A novel aspect of this work is the integration of videographic records, which offers an enriched understanding of the complex interactions between nanomaterials and biological tissues, providing detailed insights into the system’s operational dynamics. The study bridges the gap between experimental methodologies and theoretical models, aligning with the cutting-edge research in chemical physics and physical chemistry by offering novel approaches to tissue engineering challenges. The findings underscore the critical role of nanomaterials in achieving precise enzymatic control, which is pivotal for the development of advanced biomimetic systems and improved therapeutic outcomes. This work contributes to the ongoing frontier research in physical phenomena within chemistry, biology, and materials science, by providing significant new insights into enzyme delivery mechanisms and their application in biomedical contexts. The research not only highlights the importance of innovative methodologies in tissue engineering but also sets the stage for future experimental validations and potential clinical applications

ID: 325017 Patient-specific computational modeling in a longitudinal study of spinal cord stimulation for pain

ID: 325017 Patient-specific computational modeling in a longitudinal study of spinal cord stimulation for pain

Engineering inhibitory repeat domains of Pin-II type proteinase inhibitors indicate their high structural-functional tolerance to mutagenesis

Plant proteinase inhibitors (PIs) are critical in defending against biotic stress. Most PIs contain an inhibitory repeat domain (IRD), which serves as the functional component, displaying a high degree of sequence and structural conservation. In this study, we examined the structural and functional resilience of IRDs using a combination of computational modeling and experimental validation. We have taken an evolution-based approach to enhance the PIs effectiveness of two previously identified Capsicum annuum IRDs, IRD4 and IRD10. Through in silico site-saturation mutagenesis of IRD4 and IRD10, we identified key sites associated with enhanced PI activity for targeted mutagenesis. Binding energy predictions for a mutant IRD library, tested against target proteases, suggested that positions R11 and N32 in IRD4 and N32 and H33 in IRD10 were promising candidates for further modification to improve inhibitory potential. Subsequent experimental validation revealed that the mutant proteins IRD4_R11K and IRD4_N32S exhibited stronger chymotrypsin inhibition than the wild-type (WT) IRD4. Similarly, the mutants IRD10_N32S and IRD10_H33N demonstrated improved trypsin inhibition relative to the WT IRD10. These findings indicate that engineered IRD variants can tolerate structural changes while maintaining or enhancing their inhibitory activity against target proteases. Overall, this study demonstrates the potential of engineering PIs to increase their structural and functional resilience, offering new opportunities for biotechnological applications.

Development of a Novel Beam-Based Finite-Element Approach for the Computationally Efficient Prediction of Residual Stresses and Displacements in Large 3D-Printed Polymer Parts

Recently, 3D printing of large, structural polymer parts has received increasing interest, especially for the creation of recyclable structural parts and tooling. However, the complexity of large-scale 3D polymeric printing often dictates resource-intensive trial and error processes to achieve acceptable parts. Existing computational models used to assess the impact of fabrication conditions typically treat the 3D-printed part as a continuum, incorporate oversimplified boundary conditions and take hours to days to run, making design space exploration infeasible. The purpose of this study is to create a structural model that is computationally efficient compared with traditional continuum models yet retains sufficient accuracy to enable exploration of the design space and prediction of part residual stresses and deformations. To this end, a beam-based finite element methodology was created where beads are represented as beams, vertical springs represent inter-bead transverse force transfer and multi-point, linear constraints enforce strain compatibility between adjacent beads. To test this framework, the fabrication of a large Polyethylene terephthalate glycol (PETG) wall was simulated. The PETG was modeled as linearly elastic with an experimentally derived temperature-dependent coefficient of thermal expansion and elastic modulus using temperature history imported from an ABAQUS thermal model. The results of the simulation were compared to those from a continuum model with an identical material definition, showing reasonable agreement of stresses and displacements. Further, the beam-based model required an order of magnitude less run time. Subsequently, the beam-based model was extended to allow separation of the part from the printing bed and the inclusion of part self-weight during fabrication to assess the significance of these effects that pose challenges for existing continuum models.

A semantic segmentation method integrated convolutional nonlinear spiking neural model with Transformer

Semantic segmentation is a critical task in computer vision, with significant applications in areas like autonomous driving and medical imaging. Transformer-based methods have gained considerable attention recently because of their strength in capturing global information. However, these methods often sacrifice detailed information due to the lack of mechanisms for local interactions. Similarly, convolutional neural network (CNN) methods struggle to capture global context due to the inherent limitations of convolutional kernels. To overcome these challenges, this paper introduces a novel Transformer-based semantic segmentation method called NSNPFormer, which leverages the nonlinear spiking neural P (NSNP) system—a computational model inspired by the spiking mechanisms of biological neurons. The NSNPFormer employs an encoding-decoding structure with two convolutional NSNP components and a residual connection channel. The convolutional NSNP components facilitate nonlinear local feature extraction and block-level feature fusion. Meanwhile, the residual connection channel helps prevent the loss of feature information during the decoding process. Evaluations on the ADE20K and Pascal Context datasets show that NSNPFormer achieves mIoU scores of 53.7 and 58.06, respectively, highlighting its effectiveness in semantic segmentation tasks.

Context-dependent communication under environmental constraints

There is significant evidence that real-world communication cannot be reduced to sending signals with context-independent meaning. In this work, based on a variant of the classical Lewis (1969) signalling model, we explore the conditions for the emergence of context-dependent communication in an agent-based situated model. In particular, we demonstrate that pressure to minimise the vocabulary size is sufficient for such emergence. At the same time, we study the environmental conditions and cognitive capabilities that enable contextual disambiguation of symbol meanings. We show that a) regularities in the context are not necessary for context-dependent communication and that b) environmental constraints on the receiver’s referent choice can be unilaterally exploited by the sender, without disambiguation capabilities on the receiver’s end. Consistent with common assumptions, the sender’s awareness of the context appears to be required for contextual communication. We suggest that context-dependent communication is a situated multilayered phenomenon, crucially influenced by environment properties such as distribution of objects in the context. The computational model developed in this work is a demonstration of how signals may be ambiguous out of context, but still allow for near-perfect communication accuracy.

ID: 342272 Computational Modeling of the Neural Response to Inferferential Spinal Cord Stimulation

ID: 342272 Computational Modeling of the Neural Response to Inferferential Spinal Cord Stimulation

Computational Evaluating on the Role of Sac Centerline Length on hemorrhage Risk of Anterior Cerebral Artery with saccular aneurysm

The present article investigates geometrical features of an aneurysm, particularly the sac centreline length (SCL), in determining the hemodynamic environment within the aneurysm for evaluation of the haemorrhage risk. Indeed, understanding how these geometric characteristics influence blood flow patterns is essential for assessing rupture risk and optimizing treatment strategies. Computational fluid dynamic is used to perform computational modelling of the blood hemodynamic in ACA cases. Wall shear stress (WSS), OSI and pressure over sac surface are investigated and compared for selected saccular aneurysm to evaluate risk of aneurysm rupture when sac centreline length is varied. Comparison of the results show that the inverse relationship between sac centerline length and these key hemodynamic parameters (OSI, velocity, WSS) underscores the importance of geometrical factors in assessing aneurysm rupture risk. Longer sac centerline lengths appear to be associated with more stable flow dynamics and reduced mechanical stress, while shorter sac lengths correlate with more disturbed and high-stress conditions.

The Changing Phases of Bromopentafluorobenzene.

As part of a much larger study on non-covalent interactions in binary adducts, we have explored the solid-state structures of bromopentafluorobenzene (C6F5Br) using differential scanning calorimetry (DSC), variable-temperature powder X-ray diffraction (VT-PXRD), and single-crystal X-ray diffraction (SXD). DSC data initially indicated a single solid-state phase below the freezing point, but revealed additional weak transitions upon heating. The crystal structures of three solid-state phases have been solved. The SXD data showed that phases I and IV are centrosymmetric, whilst phase II is polar. However, the structure of phase III remains elusive due to the changing phase behaviour of C6F5Br that is determined as much as by kinetics as thermodynamics. The results underline the need for multiple analytical techniques to study non-covalent interactions and offer valuable data for refining computational models in crystal structure prediction and machine learning. A comparison with the iodinated counterpart is also made.

Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach

Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural network (PINN) model that encodes the physics of a closed-loop blood circulation system embedding a left ventricle (LV). The PINN model is trained to satisfy a system of ordinary differential equations (ODEs) associated with a lumped parameter description of the circulatory system. The model predictions have a maximum error of less than 5% when compared to those obtained by solving the ODEs numerically. An inverse modeling approach using the PINN model is also developed to rapidly estimate model parameters (in ∼ 3 min) from single-beat LV pressure and volume waveforms. Using synthetic LV pressure and volume waveforms generated by the PINN model with different model parameter values, we show that the inverse modeling approach can recover the corresponding ground truth values for LV contractility indexed by the end-systolic elastance Ees with a 1% error, which suggests that this parameter is unique. The PINN inverse modeling approach is then applied to estimate Ees using waveforms acquired from 11 swine models, including waveforms acquired before and after administration of dobutamine (an inotropic agent) in 3 animals. The estimated Ees is about 58% to 284% higher for the data associated with dobutamine compared to those without, which implies that this approach can be used to estimate LV contractility using single-beat measurements. The PINN inverse modeling can potentially be used in the clinic to simultaneously estimate LV contractility and other physiological parameters from single-beat measurements.

ID: 337509 A computational model of porcine vagus nerve stimulation and compound action potential sensing

ID: 337509 A computational model of porcine vagus nerve stimulation and compound action potential sensing

Wake-induced vibration of ultra-long suspenders adjacent to bridge tower

The tower wake usually induces large-amplitude vibrations in adjacent suspenders of a long-span suspension bridge. In order to interpret the underlying mechanism of the significant wake-induced vibrations of ultra-long suspenders, the wake-induced vibration characteristics of tandem suspenders beside the bridge tower are investigated using computational fluid dynamics based simulation. First, the computational model of the bridge tower and suspenders is established. Subsequently, the effect of tower wake on suspender vibration is analyzed considering the inflow velocity and relative position between the tower and suspenders. Based on the proper orthogonal decomposition (POD), the flow characteristics behind the tower and around the suspenders are investigated, and the governing mechanism of wake-induced vibrations of the suspenders is revealed ultimately. The results indicate that the wake-induced vibration of the suspender exhibits a lock-in phenomenon with large cross-wind amplitudes at specific incoming wind velocities. The wind loads on the suspender in both along-wind and cross-wind directions exhibit components with frequencies that are multiples of the vortex shedding frequency. The flow field can be accurately constructed using the first four POD modes with the largest energy. The symmetric POD modes are the primary components that contribute to the significant wake-induced vibrations of the suspenders.