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Interfacial fluids are ubiquitous in systems ranging from biological membranes to chemical droplets and exhibit a complex behavior due to their nonlinear, multiphase, and multicomponent nature. The development of accurate coarse-grained (CG) models for such systems poses significant challenges, as these models must effectively capture the intricate many-body interactions, both inter- and intramolecular, arising from atomic-level phenomena, and account for the diverse density distributions and fluctuations at the interface. In this study, we use advanced machine learning techniques incorporating force matching and diffusion probabilistic models to construct a robust CG model of interfacial fluids. We evaluate our model through simulations in various settings, including the water–air interface, bulk decane, and dipalmitoylphosphatidylcholine monolayer membranes. Our results show that our CG model accurately reproduces the essential many-body and interfacial properties of interfacial fluids and proves effective across different CG mapping strategies. This work not only validates the utility of our model for multiscale simulations, but also lays the groundwork for future improvements in the simulation of complex interfacial systems. [ABSTRACT FROM AUTHOR]

Collective strong coupling of many molecules to the confined light modes of an optical resonator can influence the photochemistry of these molecules, but the origin of this effect is not yet fully understood. To provide atomistic insights, several approaches have been developed based on quantum chemistry or molecular dynamics methods. However, most of these methods rely on coupling a few molecules (or sometimes only one) to a single cavity mode. To reach the strong coupling regime with such a small number of molecules, much larger vacuum field strengths are employed than in experiments. To keep the vacuum field realistic and avoid potential artefacts, the number of coupled molecules should be significantly increased instead, but that is not always possible due to restrictions on computational hardware and software. To overcome this barrier and model the dynamics of an arbitrarily large ensemble of molecules coupled to realistic cavity fields in atomistic molecular dynamics simulations, we propose to coarse-grain subsets of molecules into one or more effective supermolecules with an enhanced dipole moment and concerted dynamics. To verify the validity of the proposed multiscale model, we performed simulations in which we investigated how the number of molecules that are coupled to the cavity affects excited-state intra-molecular proton transfer, polariton relaxation, and exciton transport. [ABSTRACT FROM AUTHOR]

A central aim of multiscale modeling is to use results from the Schrödinger equation to predict phenomenology on length scales that far exceed those of typical molecular correlations. In this work, we present a new approach rooted in classical density functional theory (cDFT) that allows us to accurately describe the solvation of apolar solutes across length scales. Our approach builds on the Lum–Chandler–Weeks (LCW) theory of hydrophobicity [K. Lum et al., J. Phys. Chem. B 103, 4570 (1999)] by constructing a free energy functional that uses a slowly varying component of the density field as a reference. From a practical viewpoint, the theory we present is numerically simpler and generalizes to solutes with soft-core repulsion more easily than LCW theory. Furthermore, by assessing the local compressibility and its critical scaling behavior, we demonstrate that our LCW-style cDFT approach contains the physics of critical drying, which has been emphasized as an essential aspect of hydrophobicity by recent theories. As our approach is parameterized on the two-body direct correlation function of the uniform fluid and the liquid–vapor surface tension, it straightforwardly captures the temperature dependence of solvation. Moreover, we use our theory to describe solvation at a first-principles level on length scales that vastly exceed what is accessible to molecular simulations. [ABSTRACT FROM AUTHOR]

MiMiC is a framework for performing multiscale simulations in which loosely coupled external programs describe individual subsystems at different resolutions and levels of theory. To make it highly efficient and flexible, we adopt an interoperable approach based on a multiple-program multiple-data (MPMD) paradigm, serving as an intermediary responsible for fast data exchange and interactions between the subsystems. The main goal of MiMiC is to avoid interfering with the underlying parallelization of the external programs, including the operability on hybrid architectures (e.g., CPU/GPU), and keep their setup and execution as close as possible to the original. At the moment, MiMiC offers an efficient implementation of electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) that has demonstrated unprecedented parallel scaling in simulations of large biomolecules using CPMD and GROMACS as QM and MM engines, respectively. However, as it is designed for high flexibility with general multiscale models in mind, it can be straightforwardly extended beyond QM/MM. In this article, we illustrate the software design and the features of the framework, which make it a compelling choice for multiscale simulations in the upcoming era of exascale high-performance computing. [ABSTRACT FROM AUTHOR]

Pulmonary hypertension (PH) is a debilitating disease that alters the structure and function of both the proximal and distal pulmonary vasculature. This alters pressure-flow relationships in the pulmonary arterial and venous trees, though there is a critical knowledge gap in the relationships between proximal and distal hemodynamics in disease. Multiscale computational models enable simulations in both the proximal and distal vasculature. However, model inputs and measured data are inherently uncertain, requiring a full analysis of the sensitivity and uncertainty of the model. Thus, this study quantifies model sensitivity and output uncertainty in a spatially multiscale, pulse-wave propagation model of pulmonary hemodynamics. The model includes fifteen proximal arteries and twelve proximal veins, connected by a two-sided, structured tree model of the distal vasculature. We use polynomial chaos expansions to expedite sensitivity and uncertainty quantification analyses and provide results for both the proximal and distal vasculature. We quantify uncertainty in blood pressure, blood flow rate, wave intensity, wall shear stress, and cyclic stretch. The latter two are important stimuli for endothelial cell mechanotransduction. We conclude that, while nearly all the parameters in our system have some influence on model predictions, the parameters describing the density of the microvascular beds have the largest effects on all simulated quantities in both the proximal and distal arterial and venous circulations.

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignins complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

Ionic liquid electrospray thrusters represent an alternative propulsion method for spacecraft to conventional plasma propulsion because they do not require plasma generation, which significantly increases the thrust efficiency. The porous emitter thruster has the advantages of simple propellant feeding and multi-site emissions, which miniaturize the thruster size and increase thrust. However, the multi-scale nature, that is, nano- to micrometer-sized menisci on the millimeter-size porous needle tip, makes modeling multi-site emissions difficult, and direct observation is also challenging. This paper proposes a simple model for multi-site emissions, which assumes that the ionic conductivity or ion transport in the porous media determines the ion-emission current. The conductivity was evaluated by comparing the experimental and numerical data based on the model. The results suggest that the ionic conductivity of the porous emitter is suppressed by the ion–pore wall friction stress. Additionally, the model indicates that the emission area expansion on the porous emitter creates the unique curve shape of the current vs voltage characteristics for multi-site emissions. [ABSTRACT FROM AUTHOR]

The machine learning (ML) method emerges as an efficient and precise surrogate model for high-level electronic structure theory. Its application has been limited to closed chemical systems without considering external potentials from the surrounding environment. To address this limitation and incorporate the influence of external potentials, polarization effects, and long-range interactions between a chemical system and its environment, the first two terms of the Taylor expansion of an electrostatic operator have been used as extra input to the existing ML model to represent the electrostatic environments. However, high-order electrostatic interaction is often essential to account for external potentials from the environment. The existing models based only on invariant features cannot capture significant distribution patterns of the external potentials. Here, we propose a novel ML model that includes high-order terms of the Taylor expansion of an electrostatic operator and uses an equivariant model, which can generate a high-order tensor covariant with rotations as a base model. Therefore, we can use the multipole-expansion equation to derive a useful representation by accounting for polarization and intermolecular interaction. Moreover, to deal with long-range interactions, we follow the same strategy adopted to derive long-range interactions between a target system and its environment media. Our model achieves higher prediction accuracy and transferability among various environment media with these modifications. [ABSTRACT FROM AUTHOR]

This work employs a combination of density functional theory-infrared (IR), conductor-like screening model for real solvents (COSMO-RS), and molecular dynamic (MD) methods to investigate the impact of hydroxyl functional groups on CO2 capture within dicationic ionic liquids (DILs). The COSMO-RS reveals that hydroxyl groups in DILs reduce the macroscopic solubility of CO2 but improve the selectivity of CO2 over CO, H2, and CH4 gases. Quantum methods in the gas phase and MD simulations in the liquid phase were conducted to delve deeper into the underlying mechanisms. The IR spectrum analysis confirms red shifts in CO2's asymmetric stretching mode and blue shifts in the CR–HR bond of the dication, indicating CO2–DIL interactions and the weakening of the anion–cation interactions caused by the presence of CO2. The results show that the positioning of anions around hydroxyl groups and HR atoms in rings inhibits the proximity of CO2 molecules, causing the hydrogen atoms within methylene groups to accumulate CO2. van der Waals forces were found to dominate the interaction between ions and CO2. The addition of hydroxyl groups strengthens the electrostatic interactions and hydrogen bonds between dications and anions. The stronger interaction energy between ions in [C5(mim)2-(C2)2(OH)2][NTf2]2 limits the displacement of CO2 molecules within this DIL compared to [C5(mim)2-(C4)2][NTf2]2. Compared to [C5(mim)2-(C4)2][NTf2]2, [C5(mim)2-(C2)2(OH)2][NTf2]2 exhibits stronger ion–ion interactions, higher density, and reduced free volume, resulting in a reduction in CO2 capture. These results provide significant insights into the intermolecular interactions and vibrational properties of CO2 in DIL complexes, emphasizing their significance in developing efficient and sustainable strategies for CO2 capture. [ABSTRACT FROM AUTHOR]

As an emerging technology in liquid inactivation, one of the main challenges of pulsed electric fields (PEFs) inactivation lies in quantitatively describing and predicting its lethality to microorganisms. However, due to its cross-scaled complexity and the consequent numerous regulatory factors, there is currently still no unified framework to understand the PEF dose–response relationship and the population dynamics theoretically. In this study, a simple yet powerful model from multiscale coarse-graining method is proposed to simulate the bacterial inactivation in suspensions during PEF processing. The complex dose–response effects at the macroscale are successfully reconstructed from simple evolution rules and several coarse-graining parameters, while considering the damage and death of a single bacterium at the microscale. Our model uncovers the seemingly chaotic and even controversial dose–response relationship of PEF in literatures and systematically explores the regulatory effect of experimental parameters in a unified framework. One of the interesting findings is that PEF with shorter pulsed width enhances lethality and reduces the minimal inhibitory time at a constant energy output per pulse, owing to the phase transitions in three bacterial population dynamics (Bistability mode, Avalanche mode, and Hybrid mode). Our study provides a new insight for numerically modeling PEF lethality in liquid inactivation and could serve as a guide for dosage management in practical applications. [ABSTRACT FROM AUTHOR]

This work aims to develop and validate a framework for the multiscale simulation of the biological response to ionizing radiation in a population of cells forming a tissue. We present TOPAS-Tissue, a framework to allow coupling two Monte Carlo (MC) codes: TOPAS with the TOPAS-nBio extension, capable of handling the track-structure simulation and subsequent chemistry, and CompuCell3D, an agent-based model simulator for biological and environmental behavior of a population of cells. We verified the implementation by simulating the experimental conditions for a clonogenic survival assay of a 2-D PC-3 cell culture model (10 cells in 10,000 µm2) irradiated by MV X-rays at several absorbed dose values from 0-8 Gy. The simulation considered cell growth and division, irradiation, DSB induction, DNA repair, and cellular response. The survival was obtained by counting the number of colonies, defined as a surviving primary (or seeded) cell with progeny, at 2.7 simulated days after irradiation. DNA repair was simulated with an MC implementation of the two-lesion kinetic model and the cell response with a p53 protein-pulse model. The simulated survival curve followed the theoretical linear-quadratic response with dose. The fitted coefficients α = 0.280 ± 0.025/Gy and β = 0.042 ± 0.006/Gy2 agreed with published experimental data within two standard deviations. TOPAS-Tissue extends previous works by simulating in an end-to-end way the effects of radiation in a cell population, from irradiation and DNA damage leading to the cell fate. In conclusion, TOPAS-Tissue offers an extensible all-in-one simulation framework that successfully couples Compucell3D and TOPAS for multiscale simulation of the biological response to radiation.

A definitive understanding of the interplay between protein binding/migration and membrane curvature evolution is emerging but needs further study. The mechanisms defining such phenomena are critical to intracellular transport and trafficking of proteins. Among trafficking modalities, exosomes have drawn attention in cancer research as these nano-sized naturally occurring vehicles are implicated in intercellular communication in the tumor microenvironment, suppressing anti-tumor immunity and preparing the metastatic niche for progression. A significant question in the field is how the release and composition of tumor exosomes are regulated. In this perspective article, we explore how physical factors such as geometry and tissue mechanics regulate cell cortical tension to influence exosome production by co-opting the biophysics as well as the signaling dynamics of intracellular trafficking pathways and how these exosomes contribute to the suppression of anti-tumor immunity and promote metastasis. We describe a multiscale modeling approach whose impact goes beyond the fundamental investigation of specific cellular processes toward actual clinical translation. Exosomal mechanisms are critical to developing and approving liquid biopsy technologies, poised to transform future non-invasive, longitudinal profiling of evolving tumors and resistance to cancer therapies to bring us one step closer to the promise of personalized medicine.

Multi-scale predictive models for the shock sensitivity of energetic materials connect energy localization ("hotspots") in the microstructure to macro-scale detonation phenomena. Calculations of hotspot ignition and growth rely on models for chemical reaction rates expressed in Arrhenius forms; these chemical kinetic models, therefore, are foundational to the construction of physics-based, simulation-derived meso-informed closure (reactive burn) models. However, even for commonly used energetic materials (e.g., HMX in this paper) there are a wide variety of reaction rate models available. These available reaction rate models produce reaction time scales that vary by several orders of magnitude. From a multi-scale modeling standpoint, it is important to determine which model best represents the reactive response of the material. In this paper, we examine three global Arrhenius-form rate models that span the range of reaction time scales, namely, the Tarver 3-equation, the Henson 1-equation, and the Menikoff 1-equation models. They are employed in a meso-informed ignition and growth model which allows for connecting meso-scale hotspot dynamics to macro-scale shock-to-detonation transition. The ability of the three reaction models to reproduce experimentally observed sensitivity is assessed by comparing the predicted criticality envelope (Walker–Wasley curve) with experimental data for pressed HMX Class V microstructures. The results provide a guideline for model developers on the plausible range of time-to-ignition that are produced by physically correct Arrhenius rate models for HMX. [ABSTRACT FROM AUTHOR]

This document, published in the Journal of Applied Physics, discusses the importance of multiscale modeling and analysis in the field of biophysics. The authors emphasize the need for tools and analyses that integrate across physical scales and time scales in order to advance medical science. The document includes a collection of studies on various topics such as cardiac biomechanics, tissue mechanics, cerebral fluid flow, cell mechanics, protein modeling, and molecular fluorescence microscopy. The authors hope that readers will find these studies informative and inspiring. [Extracted from the article]

Laser-powder bed fusion additive manufacturing (LPBF-AM) of metals is rapidly becoming one of the most important materials processing pathways for next-generation metallic parts and components in a number of important applications. However, the large parametric space that characterizes laser-based LPBF-AM makes it challenging to understand what are the variables controlling the microstructural and mechanical property outcomes. Sensitivity studies based on direct LPBF-AM processing are costly and lengthy to conduct, and are subjected to the specifications and variability of each printer. Here we develop a fast-throughput numerical approach that simulates the LPBF-AM process using a cellular automaton model of dynamic solidification and grain growth. This is accompanied by a polycrystal plasticity model that captures grain boundary strengthening due to complex grain geometry and furnishes the stress-strain curves of the resulting microstructures. Our approach connects the processing stage with the mechanical testing stage, thus capturing the effect of processing variables such as the laser power, laser spot size, scan speed, and hatch width on the yield strength and tangent moduli of the processed materials. When applied to pure Cu and stainless 316L steel, we find that laser power and scan speed have the strongest influence on grain size in each material, respectively.

In this paper, multiscale modeling of polymer composites consisting of Graphene Foam (GF) and Polydimethylsiloxane (PDMS) is conducted, and their Thermal Conductivity (TC) is investigated through the use of nano-to-microscale analyses. The TC of the PDMS matrix and GF is calculated using the Molecular Dynamics (MD) method. The effective properties of the composites are computed utilizing the Mechanics of Structure Genome (MSG) coupled with Carrera Unified Formulation (CUF) as a novel micromechanical method, which allows an accurate description of the problem resulting in a high-fidelity analysis. Due to the unique interconnected structure of GF, the TC of GF/PDMS composite reaches 0.406 Wm–1 K–1 for GF with 63% porosity, which is about 69% ± 2% higher than that of neat PDMS. [ABSTRACT FROM AUTHOR]

Complex fracture geometries are often created during hydraulic fracturing because of the existences of natural fractures, beddings, geomechanical heterogeneity or stress interference among closely-spaced hydraulic fractures. Proppant transport in simple planar fractures has been thoroughly investigated in recent years, while proppant migration and placement in complex fractures, such as fracture networks, are still unclear. In this study, the latest experimental and numerical studies dedicated to the proppant transport in complex fracture geometries are reviewed and the effects of various parameters, including treatment parameters and fracture geometries, are summarized. The proppant diversion modes at the intersection of primary and secondary fractures, that are proppant flowing around the corner and proppant falling into secondary fractures, are also summarized. The recommendations for future work that focuses on the development of a multi-scale modeling framework and the combination of proppant transport modeling and reservoir simulation are also detailly discussed. This work is expected to be helpful for researchers to capture the global picture of proppant transport in complex fracture geometries and engineers to optimize hydraulic fracturing operations. [ABSTRACT FROM AUTHOR]

Carbon/carbon (C/C) composites have strict requirements for their thermal properties in extreme environments, especially at high temperature, where their properties are crucial for long-term life. The structure of C/C composites is exceptionally complex and exhibits multi-scale characteristics, and their thermophysical properties are closely related to temperature. Therefore, exploring their thermal response and heat transfer mechanisms through experimental method is relatively costly. This paper constructs a multi-scale finite element model to investigate the influence of structure and manufacturing defects on performance, and analyzes the thermophysical properties of the composites at High-temperature. By constructing a micro-representative volume element (Micro-RVE) that includes fibers, matrix, and pores, and using a steady-state heat transfer analysis method, the influence of material phase distribution on performance is studied. At the same time, a Meso-RVE reflecting the layering form, needle punching effect and manufacturing defects is established, and the transient heat transfer analysis method is used to investigate the influence of the structural form on performance. Finite element analysis shows that, the simulation values of the three C/C composites of unidirectional fiber bundles, short-chopped fiber felts, and needle punched C/C composites show good consistency with the experimental results in the published literature. This paper uses numerical methods to calculate the thermophysical properties of C/C composites from room temperature to 900°C and explores their variation with temperature. The constructed multi-scale model provides an accurate and effective method for predicting the thermophysical properties of C/C composites and also provides new perspectives and insights for thermal-mechanical coupling analysis and structural design. [ABSTRACT FROM AUTHOR]

The multiscale model of building structures, as a balanced solution between accuracy and cost, has been widely used in the analysis of structural seismic performance. A reasonable interface connection method can accurately ensure load transfer and motion coordination between models of different scales. In this paper, a novel interface connection device and the corresponding design method for a multiscale test model of building structures were proposed, in which the upper structure with smaller sized components was replaced by a simplified story‐scale model, and the lower structure was adopted as a component‐scale model. The overall and local equations of motion for this multiscale model were established. For the interface connection between different scale models, a design method considering multiparametric similarity of shear force, axial force, and bending moment was proposed. In this method, the internal nodes at the interface of the component scale model were decomposed, and the coupling relationship of internal force between two adjacent nodes was established. The axial force of each node was decoupled into the interstory shear force and bending moment provided together. Additionally, the overturning moment is provided by adding the overlapping domain. According to the equilibrium relationships of the nodes at the interface, the corresponding transfer matrix was provided, and the design method of the interface connection device was proposed. The accuracy and feasibility of the method were validated by static and shaking table tests on a frame structure. [ABSTRACT FROM AUTHOR]

This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification of candidates with desirable properties. Recently, the development of highly accurate ML interatomic potentials and generative models has not only improved the robust prediction of physical properties, but also significantly accelerated the discovery of materials. In the past couple of years, ML methods have enabled high‐precision first‐principles predictions of electronic and optical properties for large systems, providing unprecedented opportunities in materials science. Furthermore, ML‐assisted microstructure reconstruction and physics‐informed solutions for partial differential equations have facilitated the understanding of microstructure–property relationships. Most recently, the seamless integration of various ML platforms has led to the emergence of autonomous laboratories that combine quantum mechanical calculations, large language models, and experimental validations, fundamentally transforming the traditional approach to novel materials synthesis. While highlighting the aforementioned recent advances, existing challenges are also discussed. Ultimately, ML is expected to fully integrate atomic‐scale simulations, reverse engineering, process optimization, and device fabrication, empowering autonomous and generative energy system design. This will drive transformative innovations in energy conversion, storage, and harvesting technologies. [ABSTRACT FROM AUTHOR]

ABSTRACT Fetal asphyxia, a condition resulting from the combined effects of hypoxia and hypercapnia, leads to approximately 900,000 annual deaths worldwide. One cause is umbilical cord compression during labor‐induced uterine contractions, disrupting the transport of metabolites to and from the placenta, and resulting in asphyxia. Current fetal well‐being assessment relies on monitoring fetal heart rate and uterine contractions as indicators of oxygen delivery to the brain. To enhance our understanding of this complex relationship, this study aims to develop a modular mathematical model including fetal blood gas dynamics, the autonomic nervous system, and cerebral blood flow regulation. The novelty of this study lies in the capability of the model to simulate fetal growth. These submodels are part of a larger multiscale mathematical model describing fetal circulation in the second half of pregnancy. The blood gas model realistically replicates partial oxygen and carbon dioxide pressures in umbilical arteries and veins during healthy fetal development reported in the literature. An in silico experiment is conducted to simulate umbilical cord occlusion and is compared with lamb experiments to verify the realism of the regulation models during fetal growth. Our findings suggest that premature infants are more susceptible to umbilical cord occlusion, exhibiting elevated cerebral perfusion pressure and flow. This modular mathematical model may serve as a valuable tool for testing hypotheses related to the fetal regulatory system. [ABSTRACT FROM AUTHOR]

The effectiveness of multiscale modeling, consisting of quantum chemical reaction path calculations, molecular dynamics simulations, and microscopic and macroscopic finite element analysis, developed for the process-induced residual deformation of carbon fiber-reinforced plastic was experimentally evaluated. In this study, non-crimp fabric composites with an arbitrary thermoset resin were fabricated, and process-induced deformation was measured. Process-induced residual deformations due to cure shrinkage and thermal shrinkage of cross-ply laminates under conditions similar to those in the experiment were predicted using multiscale modeling. The predicted deformation shape and maximum deformation value are in good agreement with the fabrication experiments. Multiscale modeling can consider the combined factors that affect the deformation (i.e. size, elastic moduli, coefficient of thermal expansion, and fiber volume fraction) and can provide important knowledge regarding the development of high-performance composite structures and for stable manufacturing. [ABSTRACT FROM AUTHOR]

This study explores the application of Carbon Fiber Reinforced Plastics (CFRP) in lightweight design for wearable exoskeleton suits, crucial for success in industrial settings. We aimed to predict the lifespan of the exoskeleton in terms of temperature and time rather than merely confirming its ability to withstand loads. The material's viscoelastic behavior, establishing a master curve for long-term creep compliance, was characterized using Dynamic Mechanical Analysis (DMA). We also performed mechanical tests under various temperature conditions to measure strength in the load and fiber directions and carried out statistical analysis. From the test results, we constructed the master curves considering the probability of predicting the long-term strength variations. Subsequently, A multi-scale model assessed the exoskeleton frame's failure, revealing no failures in the current model under given load conditions, with the highest failure index of 0.7 for matrix tensile failure. Combining the predicted long-term strength results, the lifespan prediction for matrix tensile failure under a reference temperature of 50°C was approximately five years. The findings and methodology of this study can serve as a guide for predicting long-term performance and lifespan when applying CFRP to exoskeletons. [ABSTRACT FROM AUTHOR]

Asperities within pre-existing fractures of coals can experience local damage during the fracture closure due to external loading. Previous research postulates that this local asperity damage can lead to strain rate-dependency without causing permanent deformation to the bulk of the coal specimens. This study aims to comprehensively investigate this behavior by developing a theoretical model that characterizes the strain rate-dependency driven by fracture asperity damage in coal. To achieve this objective, an initial series of micro-scale mechanical tests are conducted on joint specimens to establish a model for effective stress acting on asperities. Building upon this model, a theoretical foundation is further developed to describe the strain rate-dependent asperity damage evolution and resulting energy dissipation. These frameworks are subsequently incorporated into elasticity and damage mechanics to capture the strain rate-dependent stress–strain relationships. To validate the proposed model across multiple scales, additional triaxial tests on core-scale specimen and micro-scale mechanical tests on joint specimens are performed. The experimentally measured strain rate-dependency aligns well with the predictions of the proposed model, indicating a successful development of a robust model. The results of the model developed in this study reveal that the strain rate-dependency in fractured coals is governed by several factors, including asperity damage, mechanical properties of the coal specimens and effective stress acting on asperities of pre-existing fractures within the bulk of coal. Moreover, it is shown that the effective stress acting on asperities is significantly affected by both applied normal stress and joint roughness coefficient (JRC). The insights derived from this study demonstrate that the strain rate-dependency induced by micro-scale asperity damage of pre-existing fractures leads to observable strain rate-dependency in bulk specimens at core-scale and the proposed model can adequately capture this behavior. Highlights: Micro-scale mechanical tests are conducted on coal joint specimens to establish the equation for effective stress acting on joint asperities. A multi-scale theoretical model is developed for the strain rate dependency within coal resulting from fracture asperity damage. The proposed model is validated by experimental data obtained from multi-scale experiments. The effective stress acting on joint (fracture) asperities is governed by both the applied normal stress and JRC. The theoretical model indicates that micro-scale asperity damage leads to the strain rate dependency at core-scale level. [ABSTRACT FROM AUTHOR]

In the use of deep learning for spacecraft anomaly detection, a key issue arises from the insufficient extraction of temporal dependencies in telemetry data. This can lead to an inability to accurately discern whether distribution changes in the data are caused by substantive anomalies or merely a consequence of model underfitting. To address this issue, we design a Temporal Dependency Extraction Enhanced Autoencoder model for multi-scale learning of telemetry data. Firstly, this model incorporates Multi-Scale Temporal Dependency Extraction blocks, which integrate self-attention, autoregressive, and feed-forward networks, aimed at systematically dissecting the long-term dependencies, historical information, and complex patterns in telemetry data. Building on these blocks, our model can efficiently and accurately reconstruct telemetry data while maintaining computational efficiency. Furthermore, we utilize an anomaly quantification metric based on the smoothed Manhattan distance, combined with the Drift Streaming Peaks-over-Threshold strategy for setting anomaly thresholds, thus establishing a comprehensive and precise anomaly alerts framework. Finally, we validate our approach using a dataset from the Attitude Control System of a Geostationary Earth Orbit satellite. The experimental results show that our method not only detects anomalies earlier than traditional methods but also provides an in-depth quantitative analysis of anomaly characteristics. [ABSTRACT FROM AUTHOR]

This paper presents a methodology where a macroscopic linear material response incorporates microscopic variations, such as transient interactions and micro-inertia effects. This is achieved by implementing the temporal coupling between macro and microstructures, along with the spatial coupling, within a dynamic computational homogenisation framework. In the context of dynamic multiscale modelling, the temporal coupling method offers significant advantages by effectively reducing deviations emerging from micro-inertia effects and transient phenomena. The effectiveness of the developed procedure is validated by a comparison of the macroscopic results with the solutions of direct numerical simulation for a one-dimensional periodic laminate bar with different contrast levels. The homogenised results obtained using the developed procedure indicate that a better prediction of the macroscopic requires a larger Representative Volume Element (RVE) which improves the estimation of multiscale strain energy and a larger time window which improves the estimation of multiscale kinetic energy. The simultaneous increase in the RVE size and the time averaging window yields the best results in predicting the macroscopic response. [ABSTRACT FROM AUTHOR]

Studying tumor immune microenvironment (TIME) is pivotal to understand the mechanism and predict the outcome of cancer immunotherapy. Systems biology mathematical models can consider and control various factors of TIME and therefore explore the anti-tumor immune response meticulously. However, the role of tumor vasculature in the recruitment of T cells and the mechanism of T cell migration through TIME have not been studied comprehensively. In this work, we developed a hybrid discrete-continuum multi-scale model to study TIME. The mathematical model includes angiogenesis and T cell recruitment via tumor vasculature. Moreover, solid tumor growth, vascular growth and remodeling, interstitial fluid flow, hemodynamics, and blood rheology are all considered in the model. In addition, different aspects of T cells, including their migration, proliferation, subtype conversion, and interaction with tumor cells are thoroughly included. The model reproduces spatiotemporal distribution of tumor infiltrating T cells that mimics histopathological patterns. Furthermore, TIME model robustly recapitulates different phases of tumor immunoediting. We also examined a number of biomarkers to predict the outcome of immune checkpoint blockade (ICB) treatment. The results demonstrated that although tumor mutational burden (TMB) may predict non-responders to ICB, a combination of different biomarkers is essential to predict the majority of the responders. Based on our results, the ICB response rate varies significantly from 28 to 89% depending on the values of different parameters, even in the cases with high TMB. [ABSTRACT FROM AUTHOR]

Despite significant research efforts in the continuum modeling of biological growth, certain aspects have been overlooked. For instance, numerous investigations have examined the influence of morphogenetic cell behaviors, like division and intercalation, on the mechanical response of passive (non-growing) tissues. Yet, their impact on active growth dynamics remains inadequately explored. A key reason for this inadequacy stems from challenges in the continuum treatment of cell-level processes. While some coarse-grained models have been proposed to address these shortcomings, a focus on cell division and cell expansion has been missing, rendering them unusable when it comes to modeling growth. Moreover, existing studies are limited to two-dimensional tissues and are yet to be formally extended to three-dimensional multicellular systems. To address these limitations, we here present a generalized multiscale model for three-dimensional aggregates that accounts for complex morphogenetic movements that include division, expansion, and intercalation. The proposed continuum theory thus allows for a comprehensive exploration into the growth and dissipation mechanics of proliferating aggregates, such as spheroids and organoids. [ABSTRACT FROM AUTHOR]

Lymph Nodes (LNs) are crucial to the immune and lymphatic systems, filtering harmful substances and regulating lymph transport. LNs consist of a lymphoid compartment (LC) that forms a porous bulk region, and a subcapsular sinus (SCS), which is a free-fluid region. Mathematical and mechanical challenges arise in understanding lymph flow dynamics. The highly vascularized lymph node connects the lymphatic and blood systems, emphasizing its essential role in maintaining the fluid balance in the body. In this work, we describe a mathematical model in a steady setting to describe the lymph transport in a lymph node. We couple the fluid flow in the SCS governed by an incompressible Stokes equation with the fluid flow in LC, described by a model obtained by means of asymptotic homogenisation technique, taking into account the multiscale nature of the node and the fluid exchange with the blood vessels inside it. We solve this model using numerical simulations and we analyze the lymph transport inside the node to elucidate its regulatory mechanisms and significance. Our results highlight the crucial role of the microstructure of the lymph node in regularising its fluid balance. These results can pave the way to a better understanding of the mechanisms underlying the lymph node's multiscale functionalities which can be significantly affected by specific physiological and pathological conditions, such as those characterising malignant tissues. [ABSTRACT FROM AUTHOR]

To solve the problems that the YOLOv5 object detection network has low detection accuracy, false detection, and missed detection of small objects for trapped people and medical rescue supplies when there is interference in the light background during indoor rescue, this paper proposes a multi-scale small object detection network multi-scale small YOLOv5s (MS-YOLOv5s). A CAC3 module that integrates the attention mechanism is proposed to capture object feature information in both channel and spatial directions; the neck BiFPN feature pyramid network is improved to improve the model's ability to fuse features of different scales, and the activation function of the convolution module is replaced by SiLU, to improve the adaptive ability of the model for small object detection. The model is deployed on the mobile rescue detection platform. The experimental results show that the mAP @ 0.5 of MS-YOLOV5s is 7.8% and 24.9% higher than that of YOLOv5s at different scales and different postures of trapped people, and the FPS reaches about 12, which can meet the needs of indoor mobile detection, proving the effectiveness of the method proposed in this paper and the robustness of the network model. [ABSTRACT FROM AUTHOR]

In this study, we apply optimal control theory to an immunoepidemiological model of HIV and opioid epidemics. For the multiscale model, we used four controls: treating the opioid use, reducing HIV risk behaviour among opioid users, entry inhibiting antiviral therapy, and antiviral therapy which blocks the viral production. Two population-level controls are combined with two within-host-level controls. We prove the existence and uniqueness of an optimal control quadruple. Comparing the two population-level controls,wefind that reducing the HIV risk of opioid users has a stronger impact on the population who is both HIV-infected and opioid-dependent than treating the opioid disorder. The within-host-level antiviral treatment has an effect not only on the co-affected population but also on the HIV-only infected population. Our findings suggest that the most effective strategy for managing the HIV and opioid epidemics is combining all controls at both within-host and between-host scales. [ABSTRACT FROM AUTHOR]

The research of hydrographic variability can be performed using geospatial analyses of habitats that explicitly represent the distribution of physicochemical factors in different periods. In the present study, water characteristics were recorded monthly in four interdecadal sampling campaigns (1980, 1998, 2010, and 2016) in Terminos Lagoon. Based on these records, geostatistical methods and multivariate spatial analyses were conducted to describe the lagoon's spatiotemporal changes, testing the distribution of fish community attributes. Interpolation of physicochemical factors revealed directional trends of change indicative of an influence of environmental characteristics seasonality, represented in the principal components by spatial gradients on estuarine condition (PC1, salinity: 52%), habitat complexity (PC2, depth: − 63%), and water quality (PC3, pH: − 55%). The spatial classification of habitats by hierarchical clustering resulted in different environmental units representing discrete hydrographic zones (Ward's cut-off height: 0.004), such that five zones were formed in the cold fronts season (a highly hydrodynamic period) instead of four as in the rainy and dry seasons. Concordant distribution patterns of fish community attributes with the identified hydrographic zones were observed (p ≤ 0.05), suggesting that ichthyofauna responded differently to ecological variability at the seasonal scale and sorted spatially according to the local environmental heterogeneity. Overall, the continuous and discrete spatial structures found in the Terminos Lagoon provide spatially explicit scenarios of the estuarine seascape, a deep insight into the realized niche of the fish assemblages by zones in different seasons, and hydrographic characterization data to parameterize multiscale fishery models. [ABSTRACT FROM AUTHOR]

During the operation of sandy railways, the challenge posed by wind-blown sand is a persistent issue. An in-depth study on the influence of wind-blown sand content on the macroscopic and microscopic mechanical properties of the ballast bed is of great significance for understanding the potential problems of sandy railways and proposing reasonable and adequate maintenance and repair strategies. Building upon existing research, this study proposes a new assessment indicator for sand content. Utilizing the discrete element method (DEM) and fully considering the complex interactions between ballast and sand particles, three-dimensional (3D) multi-scale analysis models of sandy ballast beds with different wind-blown sand contents are established and validated through field experiments. The effects of varying wind-blown sand content on the microscopic contact distribution and macroscopic mechanical behavior (such as resistance and support stiffness) of ballast beds are carefully analyzed. The results show that with the increase in sand content, the average contact force and coordination number between ballast particles gradually decrease, and the disparity in contact forces between different layers of the ballast bed diminishes. The longitudinal and lateral resistance of the ballast bed initially decreases and then increases, with a critical point at 10% sand content. At 15% sand content, the lateral resistance is mainly shared by the ballast shoulder. The longitudinal resistance sharing ratio is always the largest on the sleeper side, followed by that at the sleeper bottom, and the smallest on the ballast shoulder. When the sand content exceeds 10%, the contribution of sand particles to stiffness significantly increases, leading to an accelerated growth rate of the overall support stiffness of the ballast bed, which is highly detrimental to the long-term service performance of the ballast bed. In conclusion, it is recommended that maintenance and repair operations should be promptly conducted when the sand content of the ballast bed reaches or exceeds 10%. [ABSTRACT FROM AUTHOR]

The standard conception of cardiac conduction is based on the cable theory of nerve conduction, which treats cardiac tissue as a continuous syncytium described by the Hodgkin–Huxley equations. However, cardiac tissue is composed of discretized cells with microscopic and macroscopic heterogeneities and discontinuities, such as subcellular localizations of sodium channels and connexins. In addition to this, there are heterogeneities in the distribution of sympathetic and parasympathetic nerves, which powerfully regulate impulse propagation. In the continuous models, the ultrastructural details, i.e. the microscopic heterogeneities and discontinuities, are ignored by ‘coarse graining’ or ‘smoothing’. However, these ultrastructural components may play crucial roles in cardiac conduction and arrhythmogenesis, particularly in disease states. We discuss the current progress of modelling the effects of ultrastructural components on electrical conduction, the issues and challenges faced by the cardiac modelling community, and how to scale up conduction properties at the subcellular (microscopic) scale to the tissue and whole‐heart (macroscopic) scale in future modelling and experimental studies, i.e. how to link the ultrastructure at different scales to impulse conduction and arrhythmogenesis in the heart. [ABSTRACT FROM AUTHOR]

Background: Fermentation processes are essential for the production of small molecules, heterologous proteins and other commercially important products. Traditional bioprocess optimisation relies on phenomenological models that focus on macroscale variables like biomass growth and protein yield. However, these models often fail to consider the crucial link between macroscale dynamics and the intracellular activities that drive metabolism and proteins synthesis. Results: We introduce a multiscale model that not only captures batch bioreactor dynamics but also incorporates a coarse-grained approach to key intracellular processes such as gene expression, ribosome allocation and growth. Our model accurately fits biomass and substrate data across various Escherichia coli strains, effectively predicts acetate dynamics and evaluates the expression of heterologous proteins. By integrating construct-specific parameters like promoter strength and ribosomal binding sites, our model reveals several key interdependencies between gene expression parameters and outputs such as protein yield and acetate secretion. Conclusions: This study presents a computational model that, with proper parameterisation, allows for the in silico analysis of genetic constructs. The focus is on combinations of ribosomal binding site (RBS) strength and promoters, assessing their impact on production. In this way, the ability to predict bioreactor dynamics from these genetic constructs can pave the way for more efficient design and optimisation of microbial fermentation processes, enhancing the production of valuable bioproducts. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper introduces a novel approach that combines proper orthogonal decomposition (POD) with thermodynamics‐based artificial neural networks (TANNs) to capture the macroscopic behavior of complex inelastic systems and derive macro‐elements in geomechanics. The methodology leverages POD to extract macroscopic internal state variables from microscopic state information, thereby enriching the macroscopic state description used to train an energy potential network within the TANN framework. The thermodynamic consistency provided by TANN, combined with the hierarchical nature of POD, allows to reproduce complex, nonlinear inelastic material behaviors, as well as macroscopic geomechanical systems responses. The approach is validated through applications of increasing complexity, demonstrating its capability to reproduce high‐fidelity simulation data. The applications proposed include the homogenization of continuous inelastic representative unit cells and the derivation of a macro‐element for a geotechnical system involving a monopile in a clay layer subjected to horizontal loading. Eventually, the projection operators directly obtained via POD are exploited to easily reconstruct the microscopic fields. The results indicate that the POD‐TANN approach not only offers accuracy in reproducing the studied constitutive responses, but also reduces computational costs, making it a practical tool for the multiscale modeling of heterogeneous inelastic geomechanical systems. [ABSTRACT FROM AUTHOR]

During manufacturing of a solid oxide cell stack, tolerances on the metallic interconnects affect contact pressure distribution in the active area, which is known to influence electrical contact resistance. Finite Element Methods may be used to analyze the effects of these tolerances, however, due to complex interconnect designs and large number of contacts in a stack, such simulations are computationally expensive. In this work, with an aim to reduce computational cost, the homogenization approach is investigated for thermal expansion and relaxation behavior on interconnect contacts during operation while considering their dimensional variability. This is achieved by designing a novel constitutive material model and implementing it into the finite element framework. The output of this model is then used to evaluate the contact resistance at the SOC contacts, creating a theoretical framework to determine contact losses in a stack considering tolerance distributions on the interconnect contacts. • Tolerances on interconnects studied using homogenization approaches. • Constitutive equations representing tolerances as material properties. • Contact pressure at operational temperatures considering dimensional variability. • Effects of thermal expansion, temperature dependent moduli and creep/relaxation. • Finite element approach to calculate contact resistance at interconnect. [ABSTRACT FROM AUTHOR]

Ti 3 SiC 2 MAX phase ceramic has effectively enhanced the oxidation resistance of C/SiC composites. However, there is still a need for a numerical ablation model that can analyze the ablative behavior of 2D C/SiC composites. To address this, an efficient, phenomenological multi-scale ablation model, including the thermal property theoretical model, oxidation, decomposition, and sublimation ablation models, is established for revealing the effect of Ti 3 SiC 2 on the ablation resistance of the 2D C/SiC composite. The ablative behavior is evaluated using the continuous-wave laser with different laser power densities as the heat source and used as a basis for numerical model verification. The results show that the Ti 3 SiC 2 MAX phase ceramic can improve the ablation resistance of the 2D C/SiC composite under different laser power densities. The ablation roughness is reconstructed through the mesoscopic geometry structure and maximum/minimum ablation depth. The numerical model can analyze the effect of the mesoscopic geometry structure parameters on the ablation behavior. The model can provide a predicting method for the quantitative ablative calculation of 2D C/SiC and matrix-modified composites. • An efficient, phenomenological multi-scale ablation model is established. • The ablation resistance mechanism of Ti 3 SiC 2 MAX phase ceramic for the 2D C/SiC composite is revealing. • The mesoscale ablation roughness of 2D C/SiC composites is reconstruction. • The effect of mesoscopic geometry structure parameters on the ablation behavior is analyzed. [ABSTRACT FROM AUTHOR]

In multi-scale predictive models of shock-to-detonation transition (SDT), microstructure-aware burn models are employed to connect meso-scale dynamics with macro-scale energy release. Meso-scale calculations of hotspot ignition and growth rely on chemical reaction rates expressed in Arrhenius forms. However, even for well-studied energetic materials such as HMX, the reaction time scales and pathways provided by available reaction kinetic schemes vary widely. Uncertainties in reaction rate parameters can amplify to produce large uncertainties in predicted initiation envelopes (James curves or Pop-plots). To better pin down reaction rate parameters for HMX, we perform an inverse analysis to extract the rate constants in an Arrhenius model (assuming a single reaction global kinetics model) that best predicts an experimentally obtained criticality envelope. Interestingly, the estimated global kinetic scheme is shown to conform to the well-known Henson 1-equation model. To establish the validity of the inverse analysis, we further carry out a forward analysis using the determined rate law and demonstrate good predictions of SDT for a different class of pressed HMX material. While the inverse analysis has been applied herein to HMX, the paper presents a general route to arrive at global kinetics models that can be applied to a wide class of EM species. [ABSTRACT FROM AUTHOR]

Detecting objects in intricate scenes has always presented a significant challenge in the field of machine vision. Complex scenes typically refer to situations in images or videos where there are numerous densely distributed and mutually occluded objects, making the object detection task even more difficult. This paper introduces a novel head detection algorithm, YOLO-Based Head Detection in Complex Scenes (YOLO-HDCS). Firstly, in complex scenes, head detection typically involves a large number of small objects that are randomly distributed. Traditional object detection algorithms struggle to address the challenge of small object detection. For this purpose, two new modules have been constructed: one is a feature fusion module based on context enhancement with scale adjustment, and the other is an attention-based convolutional module. These modules are characterized by high detection efficiency and high accuracy. They significantly improve the model's multi-scale detection capabilities, thus enhancing the detection ability of the system. Secondly, it was found in practical operations that the original IoU function has a serious problem with overlapping detection in complex scenes. There is an IoU function that can ensure that the final selection boxes cover the object as accurately as possible without overlapping. This not only improves the detection performance but also greatly aids in enhancing the detection efficiency and accuracy. Our method achieves impressive results for head detection in complex scenarios, with average accuracy of 82.2%, and has the advantage of rapid loss convergence during training. [ABSTRACT FROM AUTHOR]

Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the environment and the life and health of people near landfills. This study focuses on using UAV photogrammetry to monitor geometric changes in reclaimed landfills. This approach highlights the advantages of UAVs in expanding the monitoring and providing precise information critical for decision-making in the reclamation process. This study presents the result of annual photogrammetry measurements at the Słabomierz–Krzyżówka reclaimed landfill, located in the central part of Poland. The Multiscale Model to Model Cloud Comparison (M3C2) algorithm was used to determine deformation at the landfill. The results were simultaneously compared with the landfill's reference (angular–linear) measurements. The mean vertical displacement error determined by the photogrammetric method was ±2.3 cm. The results showed that, with an appropriate measurement methodology, it is possible to decide on changes in geometry reliably. The collected 3D data also gives the possibility to improve the decision-making process related to repairing damage or determining the reclamation direction of the landfill, as well as preparing further development plans. [ABSTRACT FROM AUTHOR]

Laser Powder Bed Fusion (LPBF) enables the efficient production of near-net-shape oxide dispersion-strengthened (ODS) alloys, which possess superior mechanical properties due to oxide nanoparticles (e.g., yttrium oxide, Y-O, and yttrium-titanium oxide, Y-Ti-O) embedded in the alloy matrix. To better understand the precipitation mechanisms of the oxide nanoparticles and predict their size distribution under LPBF conditions, we developed an innovative physics-based multiscale modeling strategy that incorporates multiple computational approaches. These include a finite volume method model (Flow3D) to analyze the temperature field and cooling rate of the melt pool during the LPBF process, a density functional theory model to calculate the binding energy of Y-O particles and the temperature-dependent diffusivities of Y and O in molten 316L stainless steel (SS), and a cluster dynamics model to evaluate the kinetic evolution and size distribution of Y-O nanoparticles in as-fabricated 316L SS ODS alloys. The model-predicted particle sizes exhibit good agreement with experimental measurements across various LPBF process parameters, i.e., laser power (110–220 W) and scanning speed (150–900 mm/s), demonstrating the reliability and predictive power of the modeling approach. The multiscale approach can be used to guide the future design of experimental process parameters to control oxide nanoparticle characteristics in LPBF-manufactured ODS alloys. Additionally, our approach introduces a novel strategy for understanding and modeling the thermodynamics and kinetics of precipitation in high-temperature systems, particularly molten alloys. [ABSTRACT FROM AUTHOR]

The performance of composite materials and structures at high-velocity impacts reaching or exceeding their ballistic limit is crucial for assessing their strength and safety in aerospace applications. In this highly transient impact regime, composite materials are subject to multiple complex failure modes and their properties are susceptible to strain rate effects, making very challenging the simulation and understanding of their ballistic impact performance. This study presents: (1) a multi-scale computational framework for predicting the ballistic limit of composite plates, and (2) experimental results of ballistic impacts on carbon/epoxy plates. The multi-scale model uses a micromechanical approach to account for strain-rate dependency, to calculate micro-stress effects on the matrix and fiber properties, and to predict their coupled effect on effective composite properties. Intralaminar damage initiation and evolution are identified using the maximum stress criterion, but the degradation of properties in the matrix and fibers is predicted with the micromechanics model. Mixed-mode damage laws are implemented to simulate delamination, which guarantees accurate and reliable results. The proposed multi-scale model has been implemented and integrated into ABAQUS/Explicit (VUMAT). Experimental results from high-velocity steel ball impacts on woven IM-65 Carbon/RTM6 epoxy composite plates conducted on a high-speed impact test bench are also presented including non-destructive evaluation of the types of damage and failure. The experimental results are finally used to validate the model predictions for the ballistic limit and the predicted types of damage and failure. [ABSTRACT FROM AUTHOR]

A new failure criterion has been developed to improve modeling of orthotropic structural composites subjected to quasi-static and impact loadings. Rather than using an analytical expression that traditionally has been employed to predict failure, a point cloud failure surface is constructed in the stress/strain space using a combination of virtual and laboratory testing. These discrete points are obtained by building a micromechanical model that is subjected to uniaxial and multiaxial states of stress until the first failure of a finite element in the model is detected. The post-peak response behavior is then activated till the element meets the erosion criterion and is deleted from the model. One of the challenges in using the generated point cloud data is the ability to correctly predict when failure onset in a finite element takes place without being too conservative. Three predictive methods are compared in this paper – Approximate Nearest Neighbor (ANN), Simplified Approximate Nearest Neighbor (SANN), and Neural Network (NN). Point cloud data from a unidirectional composite, the T800-F3900, commonly used for aerospace applications, is used for comparative evaluation of these methods. The performances are first evaluated using a standalone program not connected with FE analysis. Finally, two of these methods (SANN and NN) are implemented in a commercial finite element program, LS-DYNA, and their performances are evaluated by simulating a laboratory impact test. Results indicate that the SANN and NN implementations are robust, efficient, and accurate. [ABSTRACT FROM AUTHOR]

Existing segmentation-based text detection methods generally face the problems of insufficient receptive fields, insufficient text information filtering, and difficulty in balancing detection accuracy and speed, limiting their ability to detect arbitrary-shaped text in complex backgrounds. To address these problems, we propose a new text detection method fusing the pure ConvNet model InceptionNeXt and the multi-scale attention mechanism. Firstly, we propose a text information reinforcement module to fully extract effective text information from features of different scales while preserving spatial position information. Secondly, we construct the InceptionNeXt Block module to compensate for insufficient receptive fields without significantly reducing speed. Finally, the INA-DBNet network structure is designed to fuse local and global features and achieve the balance of accuracy and speed. Experimental results demonstrate the efficacy of our method. Particularly, on the MSRA-TD500 and Total-text datasets, INA-DBNet achieves 91.3% and 86.7% F-measure while maintaining real-time inference speed. Code is available at: https://github.com/yuyu678/INANET. [ABSTRACT FROM AUTHOR]

Ni‐based bimetallic catalysts (NiM) show promise to replace expensive Pt‐based catalysts for hydrogen oxidation reaction (HOR). However, the effect of dopant and reaction conditions on the adsorbate‐driven surface reconstruction of NiM nanoparticles remains largely unexplored. Here, we use a multiscale modeling approach – integrating density functional theory, kubic harmonics interpolation, and microkinetic modeling – to investigate the interplay between dopant, reaction conditions, facet, adsorbate type, adsorbate coverage, in situ surface structure, and performance for NiM nanoparticles during HOR. Clear periodic trends appear in dopant effects on key adsorption energies, with dopants showing 7‐fold greater effects when located in the surface as compared to subsurface. Multi‐faceted nanoparticle models showed a non‐uniform correlation between O* coverage and surface reconstruction. HOR performance was facet‐dependent, with the highest performance occurring at reactive fronts formed between regions of high O* and OH* coverage. The nanoparticle averaged performance showed promotional effects for nearly all dopants compared to pure Ni, with the best‐performing dopants located preferentially in the surface layer (e. g. Au, Pd, Ag). Taken together, this work emphasizes the importance of understanding the interplay between reaction conditions, surface reconstruction, and HOR performance for NiM nanoparticles, enabling researchers to both predict and control the working nanoscale catalyst structure. [ABSTRACT FROM AUTHOR]

In the urgent pursuit of deep decarbonization, the steel industry has paid significant attention to the hydrogen‐intensive direct reduction process, where the H2‐to‐CO (volume) ratio of feed gas for the shaft furnace (SF) is increased compared to the current level. In this work, the reduction process of iron oxide pellets in the SF is investigated numerically to clarify how the in‐furnace thermochemical and aerodynamic states are affected by increasing hydrogen content, with the goal to provide guidelines for more efficient operations of the unit. Multiscale modeling is conducted to investigate the complicated gas–solid countercurrent reactive flows in the SF where the reduction behavior of an individual iron oxide pellet is described using a three‐interface unreacted shrinking core model. The results show that an increase in the hydrogen content leads to a lower temperature level and hence a deteriorated thermochemical state. It also induces a lower gas pressure drop over the in‐furnace packed bed. [ABSTRACT FROM AUTHOR]

The 3D‐braided composites have been widely used in aerospace and other fields due to the excellent mechanical properties, designability, and resistance to delamination. Accurately analyzing and evaluating mechanical properties of braided composite structures is the key, which successfully designs related structural components. However, 3D‐braided composites have complex internal meso–microscopic structures, and directly establishing a solid structure model to predict mechanical behavior poses certain challenges. The multiscale research methods are a type of approach that studies the cross‐scale structural characteristics at macro–meso–microscopic scales and couples relevant scales into a whole. Its emergence provides the possibility for evaluating the overall structural performance of 3D‐braided composites. In this article, first, the multiscale analysis models of 3D‐multidirectional‐braided composites are reviewed, and then the theoretical and numerical research progress on the static and dynamic multiscale mechanical performance of regular components of 3D‐braided composites is summarized. In addition, due to the distortion and variation of geometric shapes of 3D‐braided composites on the meso–microscopic structure; therefore, also in this article, the research progress on multiscale mechanical performance characteristics of special‐shaped components is summarized. Finally, the key problems in the multiscale research of 3D‐braided composites are presented. [ABSTRACT FROM AUTHOR]

Polylactic acid, a biodegradable thermoplastic derived from renewable sources, has notable environmental advantages and versatile properties. However, recycled PLA often experiences reduced mechanical strength and altered chemical composition due to the recycling process, limiting its suitability for 3D printing filament applications. Recognizing the inherent limitations of recycled PLA (rPLA), this research employs a unique method of coating rPLA with dopamine (DA) and reinforcing it with kenaf fibers (KF) at various weight fractions, introducing a novel multiscale modeling approach to address the challenges of interfacial bonding and tensile performance in recycled polymers. The methodology integrates experimental single filament tensile testing with multiscale finite element modeling to accurately predict the composite’s mechanical behavior across different scales. The findings reveal that a 5% kenaf fiber weight/volume fraction provides the optimal balance between tensile strength, stiffness, and toughness, achieving a significant 49.3% improvement over the unreinforced rPLA. However, increasing the kenaf fiber content beyond 5% leads to a decline in mechanical properties, attributed to higher fiber agglomeration and suboptimal fiber-matrix interactions. The novelty of this research lies in its combined use of PDA coating, natural kenaf fiber reinforcement, and advanced multiscale modeling to enhance and predict the performance of rPLA-PDA/kenaf composites, paving the way for sustainable, high-performance materials in 3D printing applications. [ABSTRACT FROM AUTHOR]