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In this study, a multiscale model is developed through secondary development (UMAT and UEXTERNALDB) in Abaqus with the objective of simulating the thermal deformation process with dynamic recrystallization behavior. The model couples the finite element method (FEM) with the multiphase field model (MPFM), thereby establishing bidirectional coupling between macroscopic mechanical behavior and microstructural evolution. A comparison between the single-element hot compression simulation and experimental results demonstrates that the model accurately simulates both the macroscopic mechanical behavior and microstructural evolution during the thermal deformation process, thereby exhibiting high precision. Simulations of the reduction pretreatment (RP) process under different reduction amounts and billet surface temperatures demonstrate that increasing the reduction amount and billet surface temperature significantly enhances both plastic deformation and the volume fraction of dynamic recrystallization in the billet core. This results in the closure of core voids and the refinement of the core microstructure, thereby providing valuable guidance for the development of optimal reduction pretreatment (RP) processes. [ABSTRACT FROM AUTHOR]

Simple Summary: The Atlantic herring is one of many migratory fish that may use the geomagnetic field to navigate on its annual migration. The exact mechanism used for detecting the geomagnetic field in fish is still an open discussion, and the two main theories on magnetic sensing in animals are in the main focus: magnetite-based or radical pair-based. Here, we explore whether the cryptochrome 4 protein of fish would be able to carry out the necessary electron transfer activation to create a radical pair to be used for magnetic sensing. Marine fish migrate long distances up to hundreds or even thousands of kilometers for various reasons that include seasonal dependencies, feeding, or reproduction. The ability to perceive the geomagnetic field, called magnetoreception, is one of the many mechanisms allowing some fish to navigate reliably in the aquatic realm. While it is believed that the photoreceptor protein cryptochrome 4 (Cry4) is the key component for the radical pair-based magnetoreception mechanism in night migratory songbirds, the Cry4 mechanism in fish is still largely unexplored. The present study aims to investigate properties of the fish Cry4 protein in order to understand the potential involvement in a radical pair-based magnetoreception. Specifically, a computationally reconstructed atomistic model of Cry4 from the Atlantic herring (Clupea harengus) was studied employing classical molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) methods to investigate internal electron transfers and the radical pair formation. The QM/MM simulations reveal that electron transfers occur similarly to those found experimentally and computationally in Cry4 from European robin (Erithacus rubecula). It is therefore plausible that the investigated Atlantic herring Cry4 has the physical and chemical properties to form radical pairs that in turn could provide fish with a radical pair-based magnetic field compass sensor. [ABSTRACT FROM AUTHOR]

Sliding bearings are vital components in modern industry, exerting a crucial influence on equipment performance, with wear being one of their primary failure modes. In addressing the issue of wear diagnosis in sliding bearings, this paper proposes an intelligent diagnostic method based on a multiscale gated convolutional neural network (MGCNN). The proposed method allows for the quantitative inference of the maximum wear depth (MWD) of sliding bearings based on online vibration signals. The constructed model adopts a dual-path parallel structure in both the time and frequency domains to process bearing vibration signals, ensuring the integrity of information transmission through residual network connections. In particular, a multiscale gated convolution (MGC) module is constructed, which utilizes convolutional network layers to extract features from sample sequences. This module incorporates multiple scale channels, including long-term, medium-term, and short-term cycles, to fully extract information from vibration signals. Furthermore, gated units are employed to adaptively assign weights to feature vectors, enabling control of information flow direction. Experimental results demonstrate that the proposed method outperforms the traditional CNN model and shallow machine learning model, offering promising support for equipment condition monitoring and predictive maintenance. [ABSTRACT FROM AUTHOR]

Magnetoelectric (ME) magnetic field sensors are novel sensing devices of great interest in the field of biomagnetic measurements. We investigate the influence of magnetic crosstalk and the linearity of the response of ME sensors in different array and excitation configurations. To achieve this aim, we introduce a combined multiscale 3D finite-element method (FEM) model consisting of an array of 15 ME sensors and an MRI-based human head model with three approximated compartments of biological tissues for skin, skull, and white matter. A linearized material model at the small-signal working point is assumed. We apply homogeneous magnetic fields and perform inhomogeneous magnetic field excitation for the ME sensors by placing an electric point dipole source inside the head. Our findings indicate significant magnetic crosstalk between adjacent sensors leading down to a 15.6% lower magnetic response at a close distance of 5 mm and an increasing sensor response with diminishing crosstalk effects at increasing distances up to 5 cm. The outermost sensors in the array exhibit significantly less crosstalk than the sensors located in the center of the array, and the vertically adjacent sensors exhibit a stronger crosstalk effect than the horizontally adjacent ones. Furthermore, we calculate the ratio between the electric and magnetic sensor responses as the sensitivity value and find near-constant sensitivities for each sensor, confirming a linear relationship despite magnetic crosstalk and the potential to simulate excitation sources and sensor responses independently. [ABSTRACT FROM AUTHOR]

When a steel box girder is constructed using the jacking method, the contact area between the jack and the bottom of the girder is subjected to complex forces, and it is very critical to ensure the local stability of the girder. When the phenomenon of unsynchronized jacking occurs, it will lead to changes in the contact area and affect the structural safety. In order to solve the above problems, this paper takes the background of the incremental launching construction of the main bridge across the Yellow River on Jiao Ping Expressway, adopts the Midas FEA NX 2021 finite element software to establish a finite element hybrid unit model under the maximum cantilever condition for the first time, and analyzes the local stresses in this state. The results show that the local maximum equivalent stress of the steel box girder is 198.301 MPa, which meets the requirements. The effect of jacking asynchrony on the structural forces is analyzed by simulating jacking asynchrony in the local model. The results show that both vertical jacking asynchrony and lateral deflection will lead to an increase in local stresses in the steel box girder and even steel yielding. On the basis of the above single-parameter study, a two-parameter correlation analysis is carried out to obtain the two-parameter control equation of jacking, the control threshold of the vertical jacking height difference is formulated to be 15 mm, and the dynamic control of lateral deflection is realized according to the control equation. Through comparison, it is found that the two-parameter control threshold of jacking synchronization is reduced, which can supplement the unfavorable state missed during single-parameter control and is a safer and more effective means of control. [ABSTRACT FROM AUTHOR]

Cardiac diseases and failure make up one of largest contributions to global mortality and significantly detriment the quality of life for millions of others. Disorders in the valves of the left ventricle are a prominent example of heart disease, with prolapse, regurgitation, and stenoses—the three main valve disorders. It is widely known that mitral valve prolapse increases the susceptibility to cardiac arrhythmia. Here, we investigate stenoses and regurgitation of the mitral and aortic valves in the left ventricle using a synergistic low-order numerical model. The model synergy derives from the incorporation of the mechanical, chemical, and electrical elements. As an alternative framework to the time-varying elastance (TVE) method, it allows feedback mechanisms at work in the heart to be considered. The TVE model imposes the ventricular pressure–volume relationship using a periodic function rather than calculating it consistently. Using our synergistic approach, the effects of valve disorders on the mechano-electric-feedback (MEF) are investigated. The MEF is the influence of cellular mechanics on the electrical activity, and significantly contributes to the generation of arrhythmia. We further investigate stenoses and regurgitation of the mitral and aortic valves and their relationship with the MEF and generation of arrhythmia. Mitral valve stenosis is found to increase the sensitivity to arrhythmia-stimulating systolic stretch, and reduces the sensitivity to diastolic stretch. Aortic valve stenosis does not change the sensitivity to arrhythmia-stimulating stretch, and regurgitation reduces it. A key result is found when valve regurgitation is accompanied by diastolic stretch. In the presence of MEF disorder, ectopic beats become far more frequent when accompanied by valve regurgitation. Therefore, arrhythmia resulting from a disorder in the MEF will be more severe when valve regurgitation is present. [ABSTRACT FROM AUTHOR]

Concrete is a multiphase composite material composed of coarse aggregate, cement mortar, and interface transition zone (ITZ). It is of great significance to study the effect of ITZ and coarse aggregate on chloride microscopic diffusion behavior for predicting the service life of reinforced concrete (RC) structures. By introducing the random distribution function, a random coarse aggregate model considering the randomness of the thickness of the ITZ was established. Furthermore, a two-dimensional (2D) chloride ion diffusion mesoscopic model was developed by specifying different diffusion properties for different phase materials of concrete. Moreover, the effects of coarse aggregate rate, ITZ thickness, and ITZ diffusion property on chloride ion diffusion behavior were investigated in this paper. The research showed that the aggregate has hindrance and agglomeration action on chloride ion diffusion. Although the volume content of the ITZ was very small, less than 0.2% of the total volume of concrete, the effect of the ITZ on the chloride diffusion in concrete cannot be ignored. More importantly, the mechanism of promoting chloride diffusion in the ITZ was revealed through the chloride diffusion trajectory. The research revealed the transmission mechanism of chloride ions in the meso-structure of concrete and provides theoretical support for the design of RC structures in coastal areas. [ABSTRACT FROM AUTHOR]

Ultrasonic testing (UT) is an important method for concrete, and ultrasonic pulse velocity is commonly used to evaluate the quality of concrete materials in existing studies. The ultrasonic pulse velocity of concrete materials is affected by many factors; therefore, it is necessary to establish a quantitative prediction model for the ultrasonic pulse velocity of concrete materials. Based on the multiscale homogenization method, concrete material is divided into different scales of homogenized materials, namely cement paste, mortar, and concrete. Then, a multiscale ultrasonic pulse velocity model is established through a combination of elasticity formulation and the hydration model. At the three scales of cement paste, mortar, and concrete, the elastic parameters and ultrasonic pulse velocity were predicted with the water-to-cement ratio of 0.35, 0.5, and 0.65, respectively. The ultrasonic pulse velocity of concrete with different water-to-cement ratios and different ages were measured in the test and predicted by the model. The results show that the predicted value of ultrasonic pulse velocity is within the error range of ±1.5% of the measured ultrasonic pulse velocity, suggesting that the established prediction model of ultrasonic pulse velocity can reliably predict the velocity change in concrete materials. [ABSTRACT FROM AUTHOR]

We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism. [ABSTRACT FROM AUTHOR]

A concurrent multiscale model coupling discrete dislocation dynamics to the finite element method is developed to investigate the plastic mechanism of materials at micron/submicron length scales. In this model, the plastic strain is computed in discrete dislocation dynamics (DDD) and transferred to the finite element method (FEM) to participate in the constitutive law calculation, while the FEM solves the complex boundary problem for DDD simulation. The implementation of the whole coupling scheme takes advantage of user subroutines in the software ABAQUS. The data structures used for information transferring are introduced in detail. Moreover, a FE mesh-based regularization method is proposed to localize the discrete plastic strain to continuum material points. Uniaxial compression tests of single crystal micropillars are performed to validate the developed model. The results indicate the apparent dependence of yield stress on sample size, and its underlying mechanisms are also analyzed. [ABSTRACT FROM AUTHOR]

Erosive wear of hydraulic machines is a common issue, which results in efficiency degradation, the enhancement of cavitation, and the need for expensive maintenance. Although numerical simulations of the erosion process could be very useful, both for understanding and predicting the process, its multiscale nature renders it very difficult to simulate. A previously validated multiscale model of erosion is presented. It consists of two coupled sub-models: On the microscale, the sediment impacts are simulated by means of comprehensive physical models; on the macroscale, the turbulent sediment transport and erosion accumulation are calculated. A multiscale simulation of the erosion of a prototype-scale Pelton bucket impacted by a sediment-laden water jet is presented. The simulation results, namely the erodent flux and the distributions of average impact angle and velocity on the bucket surface, bring insight into the erosion process. Furthermore, the results explain the obtained erosion distribution, which is in very good agreement with the experimental erosion measurements available in the literature for the same test case. [ABSTRACT FROM AUTHOR]

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links. [ABSTRACT FROM AUTHOR]

An advanced elastic-plastic self-consistent polycrystalline model, accounting for intragranular microstructure development and evolution, is coupled with a bifurcation-based localization criterion and applied to the numerical investigation of the impact of microstructural patterns on ductility of single-phase steels. The proposed multiscale model, taking into account essential microstructural aspects, such as initial and induced textures, dislocation densities, and softening mechanisms, allows us to emphasize the relationship between intragranular microstructure of B.C.C. steels and their ductility. A qualitative study in terms of forming limit diagrams for various dislocation networks, during monotonic loading tests, is conducted in order to analyze the impact of intragranular substructure parameters on the formability of single-phase B.C.C. steels. [ABSTRACT FROM AUTHOR]

Cotton dyeing is a very complex process with many variables in which different phenomena occur simultaneously. This study aimed to describe the cotton dyeing process by natural dye, using a mathematical model that consists of three mass conservation equations that depict dyeing cotton in cones, taking a representative volume element at the micro, meso, and macroscales. The first equation describes the concentration changes of the dye in the solution, taking into account the diffusive, convective, adsorptive, and reactive effects. The second equation describes the changes in dye concentration in cotton fiber, considering the diffusive, adsorptive, and reactive effects within an intermediate scale. The last equation describes changes in the concentration of dye in the solution on the macroscale, based on the characteristics of the equipment and the difference in concentration before and after passing through the fiber. In addition, a fluid continuity equation was incorporated, taking into account Darcy's law. In the simulation of the dyeing process with synthetic dye with initial concentrations of 0.408 and 2.06 kg/m3, RMSE of 0.00221 and 0.0289 kg/m3 were obtained, respectively. For the simulation of a dyeing process with natural dyeing, a behavior similar to the experimental data was obtained. [ABSTRACT FROM AUTHOR]

Clarifying species-environment relationships is crucial for the development of efficient conservation and restoration strategies. However, this work is often complicated by a lack of detailed information on species distribution and habitat features and tends to ignore the impact of scale and landscape features. Here, we tracked 11 Oriental White Storks (Ciconia boyciana) with GPS loggers during their wintering period at Poyang Lake and divided the tracking data into two parts (foraging and roosting states) according to the distribution of activity over the course of a day. Then, a three-step multiscale and multistate approach was employed to model habitat selection characteristics: (1) first, we minimized the search range of the scale for these two states based on daily movement characteristics; (2) second, we identified the optimized scale of each candidate variable; and (3) third, we fit a multiscale, multivariable habitat selection model in relation to natural features, human disturbance and especially landscape composition and configuration. Our findings reveal that habitat selection of the storks varied with spatial scale and that these scaling relationships were not consistent across different habitat requirements (foraging or roosting) and environmental features. Landscape configuration was a more powerful predictor for storks' foraging habitat selection, while roosting was more sensitive to landscape composition. Incorporating high-precision spatiotemporal satellite tracking data and landscape features derived from satellite images from the same periods into a multiscale habitat selection model can greatly improve the understanding of species-environmental relationships and guide efficient recovery planning and legislation. [ABSTRACT FROM AUTHOR]

In this article, we focus on the development of a multiscale modeling and recurrent neural network (RNN) based optimization framework of a plasma etch process on a three-dimensional substrate with uniform thickness using the inductive coupled plasma (ICP). Specifically, the gas flow and chemical reactions of plasma are simulated by a macroscopic fluid model. In addition, the etch process on the substrate is simulated by a kinetic Monte Carlo (kMC) model. While long time horizon optimization cannot be completed due to the computational complexity of the simulation models, RNN models are applied to approximate the fluid model and kMC model. The training data of RNN models are generated by open-loop simulations of the fluid model and the kMC model. Additionally, the stochastic characteristic of the kMC model is presented by a probability function. The well-trained RNN models and the probability function are then implemented in computing an open-loop optimization problem, in which a moving optimization method is applied to overcome the error accumulation problem when using RNN models. The optimization goal is to achieve the desired average etching depth and average bottom roughness within the least amount of time. The simulation results show that our prediction model is accurate enough and the optimization objectives can be completed well. [ABSTRACT FROM AUTHOR]

Structural adhesives play an important role in aerospace manufacturing, since they provide fewer points of stress concentration compared to faster joints. The importance of adhesives in aerospace is increasing significantly because composites are being adopted to reduce weight and manufacturing costs. Furthermore, adhesive joints are also studied to determine the crashworthiness of airframe structure, where the main task for the adhesive is not to dissipate the impact energy, but to keep joint integrity so that the impact energy can be consumed by plastic work. Starting from an extensive campaign of experimental tests, a finite element model and a methodology are implemented to develop an accurate adhesive model in a single lap shear configuration. A single lap joint finite element model is built by MSC Apex, defining two specimens of composite material connected to each other by means of an adhesive; by the Digimat multi-scale modeling solution, the composite material is treated; and finally, by MSC's Marc, the adhesive material is characterized as a cohesive applying the Cohesive Zone Modeling theory. The objective was to determine an appropriate methodology to predict interlaminar crack growth in composite laminates, defining the mixed mode traction separation law variability in function of the cohesive energy (Gc), the ratio between the shear strength τ and the tensile strength σ ( β 1 ), and the critical opening displacement υc. [ABSTRACT FROM AUTHOR]

Chemotaxis is an oriented movement of cells and organisms in response to chemical signals, and plays an important role in the life of many cells and microorganisms, such as the transport of embryonic cells to developing tissues and immune cells to infection sites. Since the pioneering works of Keller and Segel, there has been a great deal of literature on the qualitative analysis of chemotaxis systems. As an important extension of the Keller–Segel system, a variety of chemotaxis–haptotaxis models have been proposed in order to gain a comprehensive understanding of the invasion–metastasis cascade. From a mathematical point of view, the rigorous analysis thereof is a nontrivial issue due to the fact that partial differential equations (PDEs) for the quantities on the macroscale are strongly coupled with ordinary differential equations (ODEs) modeling the subcellular events. It is the goal of this paper to describe recent results of some chemotaxis–haptotaxis models, inter alia macro cancer invasion models proposed by Chaplain et al., and multiscale cancer invasion models by Stinner et al., and also to introduce some open problems. [ABSTRACT FROM AUTHOR]

Many biological processes are triggered or driven by mechanical forces in the cytoskeletal network, but these transducing forces have rarely been assessed. Striated muscle, with its well-organized structure provides an opportunity to assess intracellular forces using small-angle X-ray fiber diffraction. We present a new methodology using Monte Carlo simulations of muscle contraction in an explicit 3D sarcomere lattice to predict the fiber deformations and length changes along thin filaments during contraction. Comparison of predicted diffraction patterns to experimental meridional X-ray reflection profiles allows assessment of the stepwise changes in intermonomer spacings and forces in the myofilaments within living muscle cells. These changes along the filament length reflect the effect of forces from randomly attached crossbridges. This approach enables correlation of the molecular events, such as the current number of attached crossbridges and the distributions of crossbridge forces to macroscopic measurements of force and length changes during muscle contraction. In addition, assessments of fluctuations in local forces in the myofilaments may reveal how variations in the filament forces acting on signaling proteins in the sarcomere M-bands and Z-discs modulate gene expression, protein synthesis and degradation, and as well to mechanisms of adaptation of muscle in response to changes in mechanical loading. [ABSTRACT FROM AUTHOR]

Copper oxide is one of the promising oxygen carrier materials in chemical looping with oxygen uncoupling (CLOU) technology, cycling between Cu2O and CuO. In this study, a multiscale model was developed to describe the oxidation kinetics of the Cu-based oxygen carrier particle with oxygen, including surface, grain, and particle scale. It was considered that the solid product grows with the morphology of disperse islands on the grain surface, and O2 contacts with two different kinds of grain surfaces in the grain scale model, that is, Cu2O surface (solid reactant surface) and CuO surface (solid product surface). The two-stage behavior of the oxidation reaction of the Cu-based oxygen carrier was predicted successfully using the developed model, and the model results showed good agreement with experimental data in the literature. The effects of oxygen partial pressure, temperature, and particle structure on the oxidation performance were analyzed. The modeling results indicated that the transition of the conversion curve occurs when product islands cover most part of the grain surface. The oxygen partial pressure and particle structure have an obvious influence on the duration time of the fast reaction stage. Furthermore, the influence of the external mass transfer and the change of effectiveness factor during the oxidation reaction process were discussed to investigate the controlling step of the reaction. It was concluded that the external mass transfer step hardly affects the reaction performance under the particle sizes normally used in CLOU. The value of the effectiveness factor increases as the reaction goes by, which means the chemical reaction resistance at grain scale increases resulting from the growing number of product islands on the grain surface. [ABSTRACT FROM AUTHOR]

A model for calculating the hysteretic friction force for a multilevel wavy surface sliding in dry conditions over the surface of a viscoelastic foundation is suggested, taking into account adhesion force acting in the direction normal to the contact surface. At each scale level, the contact problem for a 3D periodic wavy indenter is solved by using the strip method to reduce the problem to 2D formulation in a strip. Different regimes of contact and adhesion interaction are possible in each strip, including partial and saturated contact. The friction force is calculated as a sum of two terms. The first term is due to hysteretic losses occurring when asperities of this scale level cyclically deform the viscoelastic foundation during sliding. The second term is the law of friction determined from the solution of the contact problem at the inferior scale level. For the case of a two-level wavy surface, the contribution of both levels into the total friction force is calculated and analyzed depending on the sliding velocity and specific energy of adhesion of the contacting surfaces. [ABSTRACT FROM AUTHOR]

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links.