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Highlights Permeability quantifies the flow conductivity of fabric reinforcements and is key to predicting mold filling times and resin flow in liquid composite molding (LCM). Flow depends on the micron‐level channels within the tows and the millimeter‐level channels between them. This study presents a general multi‐scale permeability prediction framework for fabrics considering realistic inter‐ and intra‐tow geometry. A misaligned fiber representative volume element model was constructed by a random perturbation method at the micro‐scale with orientation parameters identified from microscopic images of the cross‐section. At the meso‐scale, a tow cross‐section wrapping algorithm and an interference elimination algorithm were proposed to construct continuous as‐woven tows from the virtual fiber compression simulation. The governing fluid dynamics equations were solved to obtain the flow field within the multi‐scale gaps and compute the permeability. The simplified model's permeability predictions differed from experimental results by 12.7% to 16%. This work provides valuable insights for further research and development in LCM. Fabric permeability framework considers realistic tow geometry for prediction. Microscale misaligned fiber SVE model predicts tow permeability. Adaptive winding & interference algorithms aid tow description transition. A simplified model predicts permeability reasonably without 3D measurements. [ABSTRACT FROM AUTHOR]

In this study, the viscoelastic constitutive equation of continuous glass fiber reinforced polypropylene composites based on the generalized Maxwell model and SCFM model was constructed by combining genetic algorithm. Based on this, a multi-scale numerical model for describing material relaxation and creep is proposed, and the accuracy of different scale models was compared through experiments. The forming state of the workpiece under various stamping speeds and preheating temperatures was also examined using a hot stamping numerical model. The independent design of the hot stamping hemispherical mold was used to confirm the hot stamping model's accuracy. The study's findings demonstrate that the macroscopic model can accurately simulate the relaxation and creep of composite materials. The hot stamping simulation results are match up well with experimental results. Composite molding defects appeared at the waist of the part, and defects were accompanied by resin loss. [ABSTRACT FROM AUTHOR]

Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites (3D C/SiC). Represent volume element (RVE) models of microscale, void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites. An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture. The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions. Three RVE models of cuboid, hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale. Besides, the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models. The prediction results at the mesoscale correspond closely to the experimental values. The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration. The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures. [ABSTRACT FROM AUTHOR]

This article investigates the strength of nanocomposite filaments produced through extrusion. It involves incorporating carbon nanotubes (CNTs) into Acrylonitrile Butadiene Styrene (ABS) to produce printable nanocomposite filaments for filament fused fabrication technique and measuring their strengths. A multi-scale modeling procedure is developed to predict the strength of nanocomposite filaments through computational modeling. This model analyzes three sequential scales of micro, meso and macro. Determining effective parameter(s) of each scale, a proper representative volume element (RVE) is defied for each scale, seperately. CNT-polymer interaction, CNT length and CNT orientation are taken into account at the scale of micro; while CNT agglomeration is captured at the scale of meso. The strength of nanocomposite filament is finally estimated at the uppermost scale of macro. CNT length, orientation and agglomeration are all treated as random paramters, thus stochastic modeling is conducted in connection with genetic algorithm (GA) to reduce the required runtime of analysis. The outputs of this modeling procedure align closely with experimental observations. [ABSTRACT FROM AUTHOR]

This study explores the microstructural characterization of pearlite phase transformation in high-strength low-alloy API X60 steel, which is used in pipelines. Understanding the formation, phase percentages, and morphology of the pearlitic phase is crucial since it affects the mechanical properties of the considered steel. In this research, a phase-field model, particularly the Cahn-Hilliard approach, was used in order to simulate the formation and morphology of the pearlite phase in response to different heat treatments. Both double- and triple-well potentials were considered for comprehensively studying pearlite's morphology in the simulations. The simulation results were then compared with experimental outcomes obtained by metallography and field-emission scanning electron microscopy analyses. Considering the doublewell potential can help simulate only two phases, ferrite and cementite, which is less compatible with the experiment results than the triple-well potential, which gives the possibility of simulating a three-phase microstructure, ferrite, cementite, and austenite, and a better match with experimental data. The study revealed that as the cooling rate increases, the interlamellar spacing and layer thickness decrease. Additionally, the difference between experimental and simulation results using triple-well potential was approximately ~10%. Therefore, triple-well potential formulation predictions have better agreements with experimental results for the development circumstance of pearlitic structures. [ABSTRACT FROM AUTHOR]

Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecasting models based on graph neural networks (GNNs). However, existing GNN-based methods suffer from two key limitations: (1) current models broaden receptive fields by scaling the depth of GNNs, which is insufficient to preserve the semantics of long-range connectivity between distant but epidemic related areas. (2) Previous approaches model epidemics within single spatial scale, while ignoring the multi-scale epidemic patterns derived from different scales. To address these deficiencies, we devise the Multi-scale Spatio-temporal Graph Neural Network (MSGNN) based on an innovative multi-scale view. To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-regional epidemic signals and integrates them into a multi-scale graph. Based on the learned multi-scale graph, we utilize a newly designed graph convolution module to exploit multi-scale epidemic patterns. This module allows us to facilitate multi-scale epidemic modeling by mining both scale-shared and scale-specific patterns. Experimental results on forecasting new cases of COVID-19 in United State demonstrate the superiority of our method over state-of-arts. Further analyses and visualization also show that MSGNN offers not only accurate, but also robust and interpretable forecasting result. Code is available at https://github.com/JashinKorone/MSGNN. [ABSTRACT FROM AUTHOR]

Abstract Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites (3D C/SiC). Represent volume element (RVE) models of microscale, void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites. An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture. The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions. Three RVE models of cuboid, hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale. Besides, the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models. The prediction results at the mesoscale correspond closely to the experimental values. The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration. The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.

Abstract Predicting physical properties of complex multi-scale systems is a common challenge and demands analysis of various temporal and spatial scales. However, physics alone is often not sufficient due to lack of knowledge on certain details of the system. With sufficient data, however, machine learning techniques may aid. If data are yet relatively cumbersome to obtain, hybrid methods may come to the rescue. We focus in this report on using various types of neural networks (NN) including NN’s into which physics information is encoded (PeNN’s) and also studied effects of NN’s hyperparameters. We apply the networks to predict the viscosity of an emulsion as a function of shear rate. We show that using various network performance metrics as the mean squared error and the coefficient of determination ( $$R^2$$ R 2 ) that the PeNN’s always perform better than the NN’s, as also confirmed by a Friedman test with a p-value smaller than 0.0002. The PeNN’s capture extrapolation and interpolation very well, contrary to the NN’s. In addition, we have found that the NN’s hyperparameters including network complexity and optimization methods do not have any effect on the above conclusions. We suggest that encoding NN’s with any disciplinary system based information yields promise to better predict properties of complex systems than NN’s alone, which will be in particular advantageous for small numbers of data. Such encoding would also be scalable, allowing different properties to be combined, without repetitive training of the NN’s.

This paper proposes an innovative deep learning-based method called Deep WSI-Stroma for the segmentation and classification of epithelial and mesenchyme regions in whole slide images (WSIs) of breast cancer histopathology. The method adopts an end-to-end convolutional neural network architecture and designs a multi-scale, multi-level network structure that fuses feature maps from different resolutions, enabling effective modeling of both local cellular and global tissue-level features. Attention mechanisms and adversarial training strategies are also incorporated to enhance the model's robustness and generalization ability. Experiments on the TCGA-BRCA dataset demonstrate that Deep WSI-Stroma achieves an average Dice score of 0.873 for the segmentation task, significantly outperforming classical models such as U-Net and FCN. For the classification task, it attains an average F1 score of 0.921 across the three classes, substantially surpassing hand-crafted features with SVM and CNN methods. These superior results validate the excellent performance of the proposed method in handling large-scale, high-resolution WSI data and highlight the immense potential of deep learning techniques. This innovative approach leverages the powerful representation and learning capabilities of deep learning, exhibiting significant advantages in the complex task of histopathological image analysis, and provides important insights and driving forces for related fields. [ABSTRACT FROM AUTHOR]

The significant risk associated with fault reactivation often necessitates slip tendency analyses for effective risk assessment. However, such analyses are challenging, particularly in large areas with limited or absent reliable stress measurements and where the cost of extensive geomechanical analyses or simulations is prohibitive. In this paper, we propose a novel approach using a physics‐informed neural network that integrates stress orientation and satellite displacement observations in a top‐down multi‐scale framework to estimate two‐dimensional slip tendency analyses even in regions lacking comprehensive stress data. Our study demonstrates that velocities derived from a continental scale analysis, combined with reliable stress orientation averages, can effectively guide models at smaller scales to generate qualitative slip tendency maps. By offering customizable data selection and stress resolution options, this method presents a robust solution to address data scarcity issues, as exemplified through a case study of the South Australian Eyre Peninsula. Plain Language Summary: Fault reactivation poses significant risks, often requiring slip tendency analyses for thorough risk assessment. Yet, such analyses face challenges, especially in large areas lacking reliable stress measurements or where extensive geomechanical analyses are too costly. Our paper suggests a new method using a physics‐based neural network. This approach combines compressive direction and satellite displacement observations to estimate slip tendencies in two dimensions, even where stress data is lacking. Our study shows that by using displacements from a continental scale analysis and reliable averages of compressive directions, we can guide models to create smaller‐scale maps indicating where faults are more likely to reactivate. This method allows for customizable data selection and stress resolution, offering a strong solution to data scarcity issues. We demonstrate its effectiveness through a case study of South Australia's Eyre Peninsula. Key Points: Physics‐based neural networks allow two‐dimensional slip tendency analyses without prior full‐stress informationA multi‐scale approach provides required displacement constraints when inferring full stresses from global navigation satellite system (GNSS) and stress orientation dataWe present a new application for GNSS data that would welcome more stations, even in seismically stable areas [ABSTRACT FROM AUTHOR]

AbstractIt is of great significance to improve the design efficiency and guide material selection for carbon fiber reinforced polymer (CFRP) vehicle structures, thereby, contributing to lightweight high-speed trains. This study proposes a material-structure integrated design approach that bridges the computational relationship between design variables across multiple scales and the impact responses of CFRP vehicle structures. Computational models are constructed at the micro-scale, meso-scale, and macro-scale to capture the multi-scale characteristics of the CFRP structures. At micro-scale, finite element simulations are used to determine the effective properties of fiber bundles using a representative volume element (RVE) model. This information is then transferred to the meso-scale RVE model to calculate the homogenized mechanical properties of the lamina. Finally, a macro-scale model is established to investigate the damage mechanisms and impact resistance of the CFRP apron board against the projectile impact. The influences of design variables, such as fiber type and volume fraction, are analyzed to provide further insights into the design of CFRP apron boards. [ABSTRACT FROM AUTHOR]

Composite panels made-up of multi-cellular 3D flat-knitted (M3DFK) fabrics were manufactured in this research and mechanically evaluated in order to analyze their bending functionality after being filled with polyurethane (PU) foam. Using an electronic flat knitting machine, two different groups of M3DFK fabrics varied in their cross-sectional shapes were initially prepared from glass/polyester yarns and then, being molded through vacuum injection method with epoxy resin. A three-point bending test was used to experimentally evaluate the mechanical performance of PU-foam filled composite panels. Also, the composites mechanical behaviors were theoretically investigated using the multi-scale modeling method. The results indicated that the reinforcement structural geometries and foam presence in the composite specimens have a significant impact on their bending properties. The empirical findings revealed that foam injection resulted in a 113.8% and 92.3% increase in energy absorption for double- and single-decker composite structures during the bending process, respectively. According to the results, foam-filled composite structures experience a significant increase in core shear and facing stresses. This increase amounts to 18.4% for the single-decker and 84.7% for the double-decker 3D structure. The results of the simulation method were used to understand the effect of composite structure as well as foam injection on the stress distribution and maximum stress applied during the bending process. Also, no delamination between foam and facing layers was observed. [ABSTRACT FROM AUTHOR]

Serotonin-induced facilitation of synaptic transmission at the sensorimotor synapse in Aplysia occurs in three temporal phases: short-term facilitation (STF), intermediate-term facilitation (ITF), and long-term facilitation (LTF). The delayed onset of LTF is considered to be related to the time-consuming processes of transcription and translation in protein synthesis. In this work, we develop a muti-scale mathematical model that involves gene transcription and protein synthesis with time delays, presynaptic membrane potential, and excitatory postsynaptic potential for the three stages of synaptic facilitation. The positive feedback loop that forms the basis of bistability according to our bifurcation analysis may account for the transition between two different states following stimulations. Our model simulates experimental findings on the generation of STF, ITF and LTF under different applications of 5HT stimulation. We further confirm that ITF is dependent on translation, whereas LTF relies on both translation and transcription. In addition, we find that the threshold of the number of repeated pulses of 5HT required for the induction of LTF changes with different ranges of the time delays. Our model could provide a useful framework for exploration of dynamical mechanisms of different temporal domains of synaptic plasticity, as well as guiding investigations into other processes underlying memory formation. [ABSTRACT FROM AUTHOR]

The thin face layers of three-dimensional (3D) integrated woven spacer fabric-reinforced polymer composites (WSFC) result in the limited mechanical properties. In this work, changing the face layer structures (e.g., thickening, asymmetrizing, differing woven structures) were investigated to improve the mechanical properties of WSFC. The results show that the 3D WSFC in the weft direction have significantly greater flexural strength than those in the warp direction. When the two-dimensional (2D) fiberglass fabrics for thickening the face layer of WSFC are increased from 1 to 3 layers, the flexural strengths of composites in the warp direction improve with 14.3%, 39.3%, and 97.2% as compared with the original composites, respectively. Besides, the flexural behavior and damage mode of WSFC with additional face layers also change. For asymmetric lay-up, the flexural properties of WSFC with the additional layers on the stressed side are better than those of WSFC with the additional layers on the non-stressed side. Furthermore, the multi-scale elastic–plastic damage models of 3D WSFC with changing face layer structures are established, and the numerical simulation is performed to analyze the damage mechanism. [ABSTRACT FROM AUTHOR]

This study delves into the grinding-induced microstructural and mechanical evolution in high-entropy nickel-based superalloys GH4169 and DD5, underscoring their distinct behaviors under varying machining conditions. Leveraging "Random Substitution" in Material Studio, the research developed intricate atomic models to accurately depict the complex chemical compositions and microstructures of these superalloys. Neper software was employed for multi-scale modeling, specifically analyzing the unit cells of GH4169. A critical focus was placed on the effects of key grinding parameters—depth, spindle speed, and feed rate—on the crystallographic deformation of GH4169, contrasting it with the response of DD5. The study highlighted a notable transition in GH4169's material removal mechanism from plastic flow to chip spallation at enhanced grinding depths and feed rates, while maintaining lattice integrity at higher grinding speeds. GH4169 consistently demonstrated greater tangential and normal forces during grinding compared to DD5, reflecting intricate machining complexities. The differential crystal orientations between these superalloys significantly impacted the grinding force distribution and heat dissipation during the process. This comprehensive analysis provides pivotal insights into the micro-level grinding process parameters, enriching both theoretical and practical understanding of material machinability in advanced manufacturing contexts. The study's novelty lies in its application of detailed atomic models and multi-scale modeling to uncover subtle microstructural and mechanical dynamics during the grinding of superalloys. [ABSTRACT FROM AUTHOR]

Herein, state‐of‐the‐art multiscale modeling methods have been described. This research includes notable molecular, micro‐, meso‐, and macroscale models for hard (polymer, metal, yarn, fiber, fiber‐reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite‐element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity‐driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state‐of‐the‐art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data‐driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in‐depth discussion on data‐driven models would provide researchers with the necessary tools to build real‐time composite structure monitoring and lifecycle prediction models. [ABSTRACT FROM AUTHOR]

A key challenge for the virtual characterization of components manufactured using short fiber-reinforced thermoplastics (SFRTs) is the inherent anisotropy which stems from the manufacturing process. To address this, a multi-scale approach is necessary, leveraging deep material networks (DMNs) as a micromechanical surrogate, for a one-stop solution when simulating SFRTs under highly nonlinear long-term load cases like creep and fatigue. Therefore, we extend the a priori fiber orientation tensor interpolation for quasi-static loading (Liu et al. in Intelligent multi-scale simulation based on process-guided composite database. arXiv:2003.09491, 2020; Gajek et al. in Comput Methods Appl Mech Eng 384:113,952, 2021; Meyer et al. in Compos Part B Eng 110,380, 2022) using DMNs with a posteriori approach. We also use the trained DMN framework to simulate the stiffness degradation under fatigue loading with a linear fatigue-damage law for the matrix. We evaluate the effectiveness of the interpolation approach for a variety of load classes using a dedicated fully coupled plasticity and creep model for the polymer matrix. The proposed methodology is validated through comparison with composite experiments, revealing the limitations of the linear fatigue-damage law. Therefore, we introduce a new power-law fatigue-damage model for the matrix in the micro-scale, leveraging the quasi-model-free nature of the DMN, i.e., it models the microstructure independent of the material models attached to the constituents of the microstructure. The DMN framework is shown to effectively extend material models and inversely identify model parameters based on composite experiments for all possible orientation states and variety of material models. [ABSTRACT FROM AUTHOR]

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High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation. Plain Language Summary: Stratocumulus clouds cover a large fraction of the globe but are very challenging to reproduce in computer simulations of Earth's atmosphere because of their unique complexity. Previous studies find the model produces too few Stratocumulus clouds as we increase the model resolution, which, in theory, should improve the simulation of important motions for the clouds. This is because the clouds are exposed to more conditions that make them evaporate away. On Earth, stratocumulus clouds reflect a lot of sunlight. In the computer model of Earth, too much sunlight reaches the surface because of too few stratocumulus clouds, which makes it warmer. This study tests two methods to thicken Stratocumulus clouds in the computer model Earth. The first method smooths out some winds, which helps reduce the exposure of clouds to the conditions that make them evaporate. The second method moves water droplets in the cloud away from the conditions that would otherwise make them evaporate. In long simulations, combining these methods helps the model produce thicker stratocumulus clouds with more water. Key Points: We improve a long‐standing stratocumulus (Sc) dim bias in a high‐resolution Multiscale Modeling FrameworkIncorporating intra‐CRM hyperviscosity hedges against the numerics of its momentum solver, reducing entrainment vicinityFurther adding sedimentation boosts Sc brightness close to observed, opening path to more faithful low cloud feedback analysis [ABSTRACT FROM AUTHOR]

The design of food processing plants that are plug-and-play, reconfigurable, modular, mobile and containerised is a complex task. The "Methodology for the integrated, multi-scale and multi-objective design of systems" can be used to design such systems. This was developed and the various tasks that make it up presented in a previous article by Savoure et al. (2023) (references to be specified). The aim of this study is to assess the potential of this methodology to design a mobile fruit and vegetable processing unit. Firstly, a design support tool was developed to (i) simulate the behaviour and estimate the performances of design solutions, (ii) convert their performances into desirability indices using the decision maker's preferences and (iii) search for the design solution(s) that best satisfy the decision maker's design objectives. The results were then visualised and analysed to ensure that the resulting design solution was a satisfying, feasible and coherent trade-off. This analysis also helped to understand (i) which design variables, models and assumptions drove the design decision the most and (ii) which final products, design objectives, performance indicators and preference models influenced the optimisation process the most. In particular, it showed that one of the products (okra puree) limited the performance of the design solution. Finally, this work increased the level of understanding of the design problem and challenged the preferences of the decision maker. [ABSTRACT FROM AUTHOR]

We propose a general mathematical and computational approach to study cellular transport driven by a group of kinesin motors. It is a framework for multi-scale modeling that integrates kinetic models of single kinesin motors, including detachment and reattachment events, to study group behaviors of several motors. By formulating the problem as a semi-Markov process and applying a central limit theorem, asymptotic velocity and diffusivity can be readily calculated, which offers considerable computational advantage over Monte Carlo simulations in tasks such as parameter sensitivity analysis and model selection. We demonstrate the method with some examples. The importance of incorporating the intrinsic microscopic-level dynamics of individual motors is illustrated by showing how changes at the microscopic level propagate to the motor-cargo complex at a mesoscopic level. Particularly, we showcase an example in which changes in the second moment of single-motor characteristics gives rise to different first moment characteristics of the motor group. [ABSTRACT FROM AUTHOR]

In this article, a microscopic constitutive model is established that includes friction, plastic, and spring elements and has clear physical meaning. The friction unit reflects the mutual friction between crack surfaces, the plastic unit reflects the development of concrete plasticity, and the fracture of the spring unit reflects the formation and expansion of interior cracks in concrete. In addition, the integration of the random field theory into this model uncovers the physical underpinnings of the relationship between concrete's nonlinearity and randomness. The multi-scale modeling of the concrete static damage constitutive model is then realized once the parameters of the random field are discovered using the macro test results. In order to apply the model's applicability in finite element programs, a subroutine was ultimately constructed. The experimental data and the anticipated values from the numerical simulation are in good agreement, supporting the model's realism. [ABSTRACT FROM AUTHOR]

Abstract The significant risk associated with fault reactivation often necessitates slip tendency analyses for effective risk assessment. However, such analyses are challenging, particularly in large areas with limited or absent reliable stress measurements and where the cost of extensive geomechanical analyses or simulations is prohibitive. In this paper, we propose a novel approach using a physics‐informed neural network that integrates stress orientation and satellite displacement observations in a top‐down multi‐scale framework to estimate two‐dimensional slip tendency analyses even in regions lacking comprehensive stress data. Our study demonstrates that velocities derived from a continental scale analysis, combined with reliable stress orientation averages, can effectively guide models at smaller scales to generate qualitative slip tendency maps. By offering customizable data selection and stress resolution options, this method presents a robust solution to address data scarcity issues, as exemplified through a case study of the South Australian Eyre Peninsula.

This study explores the microstructural characterization of pearlite phase transformation in high-strength low-alloy API X60 steel, which is used in pipelines. Understanding the formation, phase percentages, and morphology of the pearlitic phase is crucial since it affects the mechanical properties of the considered steel. In this research, a phase-field model, particularly the Cahn–Hilliard approach, was used in order to simulate the formation and morphology of the pearlite phase in response to different heat treatments. Both double- and triple-well potentials were considered for comprehensively studying pearlite’s morphology in the simulations. The simulation results were then compared with experimental outcomes obtained by metallography and field-emission scanning electron microscopy analyses. Considering the double-well potential can help simulate only two phases, ferrite and cementite, which is less compatible with the experiment results than the triple-well potential, which gives the possibility of simulating a three-phase microstructure, ferrite, cementite, and austenite, and a better match with experimental data. The study revealed that as the cooling rate increases, the interlamellar spacing and layer thickness decrease. Additionally, the difference between experimental and simulation results using triple-well potential was approximately ∼10%. Therefore, triple-well potential formulation predictions have better agreements with experimental results for the development circumstance of pearlitic structures.

Herein, state‐of‐the‐art multiscale modeling methods have been described. This research includes notable molecular, micro‐, meso‐, and macroscale models for hard (polymer, metal, yarn, fiber, fiber‐reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite‐element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity‐driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state‐of‐the‐art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data‐driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in‐depth discussion on data‐driven models would provide researchers with the necessary tools to build real‐time composite structure monitoring and lifecycle prediction models.

This paper presents a comprehensive investigation of mesoporous Silica utilizing a multi-scale modeling approach under periodic boundary conditions integrated with machine learning algorithms. The study begins with Molecular Dynamics (MD) simulations to extract Silica's elastic properties and thermal conductivity at the nano-scale, employing the Tersoff potential. Subsequently, the derived material characteristics are applied to a series of generated porous Representative Volume Elements (RVEs) at the microscale. This phase involves the exploration of porosity and void shape effects on Silica's thermal and mechanical properties, considering inhomogeneities' distributions along the X-axis and random dispersion of pore cells within a three-dimensional space. Furthermore, the influence of pore shape is examined by defining open and closed-cell models, encompassing spherical and ellipsoidal voids with aspect ratios of 2 and 4. To predict the properties of porous Silica, a shallow Artificial Neural Network (ANN) is deployed, utilizing geometric parameters of the RVEs and porosity. Subsequently, it is revealed that Silica's thermal and mechanical behavior is linked to pore geometry, distribution, and porosity model. Finally, to classify the behavior of porous Silica into three categories, quasi-isotropic, orthotropic, and transversely-isotropic, three methodologies of decision tree approach, K-Nearest Neighbors (KNN) algorithm, and Support Vector Machines (SVMs) are employed. Among these, SVMs employing a quadratic kernel function demonstrate robust performance in categorizing the thermal and mechanical behavior of porous Silica.

The layered fibers of carbon-fiber-reinforced polymer (CFRP) composites exhibit low thermal conductivity (TC) throughout their thickness due to the poor TC of the polymeric resin. Improved heat transmission inside the hydrogen storage tank during the filling process can reduce further compression work, and improved heat insulation can minimize energy loss. Therefore, it is crucial to understand the thermal properties of composites. This paper reports the thermal behavior of plain-woven CFRP composite using simulation at the micro-, meso-, and macro-scales. The TC was predicted numerically and compared to experimental findings and analytical models. Good results were found. Using the approach of multi-scale modeling, a parametric study was carried out to analyze in depth the influence of certain variables on thermal properties. The study revealed that both fiber volume fraction and temperature significantly influenced the TC of the composite, with the interphase fiber/matrix thickness following closely in terms of impact. The matrix porosity was found to have a relatively slighter impact, particularly within the porosity range of 5 to 15%. [ABSTRACT FROM AUTHOR]

DNA supercoiling is central to many fundamental processes of living organisms. Its average level along the chromosome and over time reflects the dynamic equilibrium of opposite activities of topoisomerases, which are required to relax mechanical stresses that are inevitably produced during DNA replication and gene transcription. Supercoiling affects all scales of the spatio-temporal organization of bacterial DNA, from the base pair to the large scale chromosome conformation. Highlighted in vitro and in vivo in the 1960s and 1970s, respectively, the first Physical models were proposed concomitantly in order to predict the deformation properties of the double helix. About fifteen years later, polymer physics models demonstrated on larger scales the plectonemic nature and the tree-like organization of supercoiled DNA. Since then, many works have tried to establish a better understanding of the multiple structuring and physiological properties of bacterial DNA in thermodynamic equilibrium and far from equilibrium. The purpose of this essay is to address upcoming challenges by thoroughly exploring the relevance, predictive capacity, and limitations of current physical models, with a specific focus on structural properties beyond the scale of the double helix. We discuss more particularly the problem of DNA conformations, the interplay between DNA supercoiling with gene transcription and DNA replication, its role on nucleoid formation and, finally, the problem of scaling up models. Our primary objective is to foster increased collaboration between physicists and biologists. To achieve this, we have reduced the respective jargon to aminimum and we provide some explanatory background material for the two communities. [ABSTRACT FROM AUTHOR]

C/SiC composites have been widely used in aerospace, national defense, and chemical industries due to their excellent mechanical and thermal properties. Accurate knowledge about the mechanical/thermal properties of C/SiC composites is very important for their efficient application in related fields. Based on the representative volume element (RVE) and periodic boundary conditions, a micro/meso single-cell model for C/SiC composites was established in view of the non-uniform and multi-scale characteristics of fiber bundles, such as the volume fraction, the interweaving mode, and the weaving dimension. The finite element software ABAQUS was used to predict the micro-scale thermal and mechanical properties of the fiber bundle, and the fiber bundle properties were introduced into the mesoscopic model to analyze and obtain the macroscopic thermal and mechanical properties of the composite. Based on this multi-scale correlation analysis method, the thermal conductivity and thermal expansion coefficient of fiber bundles and C/SiC composites were further studied at the operating temperatures ranging from 27~1 227 ℃. The study has certain guiding significance for the application of C/SiC composites in engineering. [ABSTRACT FROM AUTHOR]

Abstract High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

The synergistic analysis of friction properties of carbon nanotube (CNT)-reinforced polymers at the nanoscale and macroscale can help to obtain the intrinsic mechanism of carbon nanotubes to reduce the friction coefficient of polymers, which is important to guide the modification of polymer friction properties. However, the huge gap in spatial scales makes it difficult for molecular dynamics (MD) simulations at the nanoscale to predict the friction coefficient of virtual contact interfaces, and conducting a large number of macroscopic experiments to obtain natural frictional laws could be more efficient. This study proposes a multi-scale model to investigate the frictional behavior of copper (Cu)-CNT/polytetrafluoroethylene (PTFE). By using the micromechanics Mori–Tanaka homogenization method as a bridge, the nanoscale simulations of the CNT/PTFE elasticity and frictional behavior and the macroscopic finite element simulation of the Cu ring-CNT/PTFE block contact are coupled, thus integrating the nanoscale frictional laws of Cu-CNT/PTFE obtained from molecular dynamics simulations into the actual contact interface. The results of multi-scale friction simulations show that the filling of CNTs can effectively improve the elastic and frictional properties of the PTFE matrix, and the degree of improvement is related to the orientation and mass fraction of the CNTs. Under a mean contact pressure of 0.5 MPa and a rotating speed of 30 rpm, the friction coefficient continuously decreases (from 0.198 to 0.156) with increasing CNTs mass fraction (0%, 1.25%, 2.5%, 5%). The simulation results were verified by copper ring-CNT/PTFE block friction experiments. [ABSTRACT FROM AUTHOR]

Introduction: The lower esophageal sphincter (LES) controls the passage into the stomach and prevents reflex of contents into the esophagus. Dysfunctions of this region typically involves impairment of muscular function, leading to diseases including gastro-esophageal reflux disease and achalasia. The main objective of this study was to develop a finite element model from a unique human LES dataset reconstructed from an ultra-mill imaging setup, and then to investigate the effect of anatomical characteristics on intraluminal pressures. Methods: A pipeline was developed to generate a mesh from a set of input images, which were extracted from a unique ultra-mill sectioned human LES. A total of 216 nodal points with cubic Hermite basis function was allocated to reconstruct the LES, including the longitudinal and circumferential muscles. The resultant LES mesh was used in biomechanical simulations, utilizing a previously developed LES mathematical model based on the Visible Human data to calculate intraluminal pressures. Anatomical and functional comparisons were made between the Ultramill and Visible human models. Results: Overall, the Ultra-mill model contained lower cavity (1,796 vs. 5,400 mm3 ) and muscle (1,548 vs. 15,700 mm³ ) volumes than the Visible Human model. The Ultra-mill model also developed a higher basal pressure (13.8 vs. 14.7 mmHg) and magnitude of pressure (19.8 vs. 18.9 mmHg) during contraction. Out of all the geometric transformations (i.e., uniform enlargement of volume, lengthening along the center-axis, dilation of the diameter, and increasing muscle thickness), the muscle volume was found to be the main contributor of basal and magnitude of pressures. Increases in length also caused proportional increases to pressures, while dilation of diameter had a less influential reverse effect. Discussion: The findings provide information on interindividual variability in LES pressure and demonstrates that anatomy has a large influence on pressures. This model forms the basis of more complex simulations involving food bolus transport and predicting LES dysfunctions. [ABSTRACT FROM AUTHOR]

Patch‐repaired plain woven composite structures were exposed to internal damage caused by impact during service, which reduced mechanical properties. A multi‐scale modeling strategy was proposed to study the impact resistance characteristics of patch‐repaired plain woven carbon‐fiber‐reinforced‐polymer (CFRP) laminates. The micro representative volume element (RVE) was extracted from the yarn; a local homogenization method was used to obtain the equivalent cross‐ply laminate (ECPL) cells corresponding to the mesoscale RVE. The finite element model (FEM) of patch‐repaired plain woven laminate was established by extending the ECPL cells. The FEM was checked by the low‐velocity impact (LVI) tests, and the impact parameter errors were within 10%. Furthermore, impact resistance of repaired specimens under different impact conditions (e.g., the impact energy was 3 ~ 20 J) was studied. Finally, patch parameters (patch thickness, patch size, off‐axis angle) of the repaired specimen were optimized by multi‐objective optimization based on the surrogate model and response surface method (RSM). After multi‐objective function optimization, the optimal patch parameter configuration (the thickness of the patch was 0.87 mm, the diameter was 66 mm, and the off‐axis angle was 44.2°) reduced the absorbed energy value of the repaired specimen from 4.9089 to 4.1640 J, and the delamination area of the repaired specimen from 605 to 280 mm2. All the results provide a reference for patch repair of plain woven composites. [ABSTRACT FROM AUTHOR]

Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.

IntroductionMathematical modeling has emerged as a crucial component in understanding the epidemiology of infectious diseases. In fact, contemporary surveillance efforts for epidemic or endemic infections heavily rely on mathematical and computational methods. This study presents a novel agent-based multi-level model that depicts the transmission dynamics of gonorrhea, a sexually transmitted infection (STI) caused by the bacterium Neisseria gonorrhoeae. This infection poses a significant public health challenge as it is endemic in numerous countries, and each year sees millions of new cases, including a concerning number of drug-resistant cases commonly referred to as gonorrhea superbugs or super gonorrhea. These drug-resistant strains exhibit a high level of resistance to recommended antibiotic treatments.MethodsThe proposed model incorporates a multi-layer network of agents' interaction representing the dynamics of sexual partnerships. It also encompasses a transmission model, which quantifies the probability of infection during sexual intercourse, and a within-host model, which captures the immune activation following gonorrhea infection in an individual. It is a combination of agent-based modeling, which effectively captures interactions among various risk groups, and probabilistic modeling, which enables a theoretical exploration of sexual network characteristics and contagion dynamics.ResultsNumerical simulations of the dynamics of gonorrhea infection using the complete agent-based model are carried out. In particular, some examples of possible epidemic evolution are presented together with an application to a real case study. The goal was to construct a virtual population that closely resembles the target population of interest.DiscussionThe uniqueness of this research lies in its objective to accurately depict the influence of distinct sexual risk groups and their interaction on the prevalence of gonorrhea. The proposed model, having interpretable and measurable parameters from epidemiological data, facilitates a more comprehensive understanding of the disease evolution.

Multi-scale habitat selection modeling (HSM) has garnered attention due to its ability to incorporate scale dependence of species. The key of multi-scale HSM is to select the appropriate combination of scales for different resources or environmental conditions, and then construct a set of multi-scale environmental covariates as the features of HSM. However, the existing scale selection methods do not determine the combination of scales under a unified model. In this study, a combinatorial optimization approach is proposed. We regard the combination of different scales as a search space, and use a heuristic optimization algorithm to search for the best-fitting model to determine the optimal scales for each resource or environmental condition. In a case study conducted in Yancheng National Nature Reserve, the proposed approach is applied to model the habitat selection of the endangered red-crowned crane. We compare the proposed method with single-scale, random-scale and other multi-scale approaches. The results show that the combination of scales selected based on the proposed method obtained the best accuracy in the spatial prediction of habitat suitability with a test AUC of 0.865 for the daytime scenario and 0.932 for the nighttime scenario. Moreover, the selected scales are utilized to generate response curves, providing suggestions for habitat restoration and management of the red-crowned crane population in the nature reserve.

Simple Summary: According to CDC, autism spectrum disorder (ASD) was diagnosed in one of every 36 children in the United States in 2020. Over 2000 genes have been identified as altered in ASD, yet a coherent model of ASD etiology is lacking. Here, we present a novel hypothesis of ASD origins based on common gene network features involving an interplay of glial and neuronal cells in the developing brain, starting during fetal development. We report statistical findings with implications for understanding the causes of ASD, early detection, and the development of new treatments. Fetal neuroinflammation and prenatal stress (PS) may contribute to lifelong neurological disabilities. Astrocytes and microglia, among the brain's non-neuronal "glia" cell populations, play a pivotal role in neurodevelopment and predisposition to and initiation of disease throughout lifespan. One of the most common neurodevelopmental disorders manifesting between 1–4 years of age is the autism spectrum disorder (ASD). A pathological glial–neuronal interplay is thought to increase the risk for clinical manifestation of ASD in at-risk children, but the mechanisms remain poorly understood, and integrative, multi-scale models are needed. We propose a model that integrates the data across the scales of physiological organization, from genome to phenotype, and provides a foundation to explain the disparate findings on the genomic level. We hypothesize that via gene–environment interactions, fetal neuroinflammation and PS may reprogram glial immunometabolic phenotypes that impact neurodevelopment and neurobehavior. Drawing on genomic data from the recently published series of ovine and rodent glial transcriptome analyses with fetuses exposed to neuroinflammation or PS, we conducted an analysis on the Simons Foundation Autism Research Initiative (SFARI) Gene database. We confirmed 21 gene hits. Using unsupervised statistical network analysis, we then identified six clusters of probable protein–protein interactions mapping onto the immunometabolic and stress response networks and epigenetic memory. These findings support our hypothesis. We discuss the implications for ASD etiology, early detection, and novel therapeutic approaches. We conclude with delineation of the next steps to verify our model on the individual gene level in an assumption-free manner. The proposed model is of interest for the multidisciplinary community of stakeholders engaged in ASD research, the development of novel pharmacological and non-pharmacological treatments, early prevention, and detection as well as for policy makers. [ABSTRACT FROM AUTHOR]

The influence of inescapable imperfections associated with fiber arrangement in the filament winding process on the burst pressure of a composite vessel is analyzed. Firstly, the influence of fiber packing, fiber spacing and fiber contiguity is analyzed through a two-scale analysis. Mechanical properties of the layers are estimated considering aforementioned parameters at the scale of micro and the burst pressure is estimated at the scale of macro. As the second approach, fiber-bundle crossovers and undulation arisen from filament winding process are also considered at the scale of meso. Thus, a multi-scale modeling is performed capturing micro, meso and macro scales. [ABSTRACT FROM AUTHOR]

We apply the Green–Kubo (G–K) approach to obtain the thermal conductivity tensor of β‐1,3,5,7‐tetranitro‐1,3,5,7‐tetrazoctane (β‐HMX) as a function of pressure and temperature from equilibrium molecular dynamics (MD) simulations. Direct application of the G–K formula exhibits slow convergence of the integrated thermal conductivity values even for long (120 ns) accumulated simulation times. To partially mitigate this slow convergence, we apply a recently implemented numerical procedure that involves physically justified filtering of the MD‐calculated G–K heat current and fitting the integrated time‐dependent thermal conductivity to a physically motivated double‐exponential function. The thermal conductivity tensor was determined for pressures 0.1 MPa≤P≤ ${\le P\le }$ 30 GPa and temperatures 300 K≤T≤ ${\le T\le }$ 900 K. The thermal conductivity καβT,P ${{\kappa }^{\alpha \beta }\left(T,P\right)}$ increases with increasing pressure, by approximately an order of magnitude over the interval considered, and decreases with increasing temperature. The MD predictions are compared to experimental and other theoretically determined values for the thermal conductivity of β‐HMX. A simple, semi‐empirical fitting form is proposed that captures the behavior of κααT,P ${{\kappa }^{\alpha \alpha }\left(T,P\right)}$ over the pressure and temperature intervals studied. [ABSTRACT FROM AUTHOR]

The effective thermal conductivity is the most useful characteristic property to distinguish between two or more insulation materials. It is always the same by using different boundary conditions or different representative elementary volume sizes. The objective of this paper is to set variational formulations of different types of boundary conditions Dirichelet boundary condition (EBC), Neumann boundary condition (NBC), Mixed boundary condition (MBC) and Periodic boundary condition (PBC). Then effective thermal conductivity are investigated by scale effect study of the representative elementary volume size of the different categories of Polyurethane foam, closed cell foam, open cell foam and mixed cell foam. The apparent conductivity remain the same for MBC, PBC and EBC in the case of closed and open unit cell foam. The effective thermal conductivity for the different categories of PU foam converge as the REVs sizes increase. A comparative study with numerical, analytical and experimental thermal conductivity is performed in order to validate the results. A hybrid model is proposed in order to overcome the computational cost of the investigation of the effective thermal conductivity of mixed cell foam.

Introduction: The lower esophageal sphincter (LES) controls the passage into the stomach and prevents reflex of contents into the esophagus. Dysfunctions of this region typically involves impairment of muscular function, leading to diseases including gastro-esophageal reflux disease and achalasia. The main objective of this study was to develop a finite element model from a unique human LES dataset reconstructed from an ultra-mill imaging setup, and then to investigate the effect of anatomical characteristics on intraluminal pressures.Methods: A pipeline was developed to generate a mesh from a set of input images, which were extracted from a unique ultra-mill sectioned human LES. A total of 216 nodal points with cubic Hermite basis function was allocated to reconstruct the LES, including the longitudinal and circumferential muscles. The resultant LES mesh was used in biomechanical simulations, utilizing a previously developed LES mathematical model based on the Visible Human data to calculate intraluminal pressures. Anatomical and functional comparisons were made between the Ultra-mill and Visible human models.Results: Overall, the Ultra-mill model contained lower cavity (1,796 vs. 5,400 mm3) and muscle (1,548 vs. 15,700 mm3) volumes than the Visible Human model. The Ultra-mill model also developed a higher basal pressure (13.8 vs. 14.7 mmHg) and magnitude of pressure (19.8 vs. 18.9 mmHg) during contraction. Out of all the geometric transformations (i.e., uniform enlargement of volume, lengthening along the center-axis, dilation of the diameter, and increasing muscle thickness), the muscle volume was found to be the main contributor of basal and magnitude of pressures. Increases in length also caused proportional increases to pressures, while dilation of diameter had a less influential reverse effect.Discussion: The findings provide information on interindividual variability in LES pressure and demonstrates that anatomy has a large influence on pressures. This model forms the basis of more complex simulations involving food bolus transport and predicting LES dysfunctions.

Aortic valve disease (AVD) often coexists with coronary artery disease (CAD), but whether and how the two diseases are correlated remains poorly understood. In this study, a zero–three dimensional (0-3D) multi-scale modeling method was developed to integrate coronary artery hemodynamics, aortic valve dynamics, coronary flow autoregulation mechanism, and systemic hemodynamics into a unique model system, thereby yielding a mathematical tool for quantifying the influences of aortic valve stenosis (AS) and aortic valve regurgitation (AR) on hemodynamics in large coronary arteries. The model was applied to simulate blood flows in six patient-specific left anterior descending coronary arteries (LADs) under various aortic valve conditions (i.e., control (free of AVD), AS, and AR). Obtained results showed that the space-averaged oscillatory shear index (SA-OSI) was significantly higher under the AS condition but lower under the AR condition in comparison with the control condition. Relatively, the overall magnitude of wall shear stress was less affected by AVD. Further data analysis revealed that AS induced the increase in OSI in LADs mainly through its role in augmenting the low-frequency components of coronary flow waveform. These findings imply that AS might increase the risk or progression of CAD by deteriorating the hemodynamic environment in coronary arteries. [ABSTRACT FROM AUTHOR]

The spread of emitted potentially virus‐laden aerosol particles is known to be highly dependent on whether a mask is worn by an infected person and on the emission scenario, i.e., whether the person is coughing, speaking, or breathing. The aim of this work is to investigate in detail the fates of particles emitted by a person wearing a perfectly fitting, a naturally fitted mask with leakage, and no mask depending on the emission scenario. Therefore, a two‐scale numerical workflow is proposed where parameters are carried through from a micro‐scale where the fibers of the mask filter medium and the aerosol particles are resolved to a macro‐scale and validated by comparison to experimental measurements of fractional filtration efficiency and pressure drop of the filter medium as well as pressure drop of the mask. It turns out that masks reduce the number of both emitted and inhaled particles significantly even with leakage. While without a mask, the person opposite of an infected person is generally at the highest risk of being infected, a mask worn by an infected person speaking or coughing will deflect the flow leading to the fact that the person behind the infected person might inhale the largest number of aerosol particles. [ABSTRACT FROM AUTHOR]

In this study, the influence of interface transition zone (ITZ) on the elastic properties of concrete and rock like heterogeneous materials is investigated. Direct simulations of a representative volume element are realized by using a fast Fourier transform based method and the obtained results are used as the reference solutions. Some widely used analytical homogenization models are evaluated by comparing the theoretical predictions with the reference solutions. Based on this evaluation, an artificial neural network (ANN) model is constructed in order to improve the analytical models. The proposed ANN model is trained, tested and validated against the reference solution. Its efficiency and good accuracy are clearly demonstrated. [ABSTRACT FROM AUTHOR]