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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.

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.

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 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]

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]

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]

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]

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]

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]

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]

Despite the initial success of single‐targeted chimeric‐antigen receptor (CAR) T‐cell therapy in hematological malignancies, its long‐term effectiveness is often hindered by antigen heterogeneity and escape. As a result, there is a growing interest in cell therapies targeting multiple antigens (≥2). However, the dose–exposure–response relationship and specific factors influencing the pharmacology of dual‐targeted CAR‐T‐cell therapy remain unclear. In this study, we have developed a multiscale cellular kinetic–pharmacodynamic (CK–PD) model using case studies from CD19/CD22 and GPRC5D/BCMA autologous CAR‐Ts. Initially, an in vitro tumor‐killing model characterized the impact of individual binder affinities and their contribution to overall potency across varying (1) effector: target (ET) ratios and (2) tumor‐associated antigen (TAA) expressing cell lines. Subsequently, an integrated CK–PD model was developed in pediatric acute lymphoblastic leukemia (ALL) patients, which accounted for CAR‐T‐cell product composition and relative antigen abundance in patients' tumor burden to characterize patient‐level multiphasic cellular kinetics using multiple bioanalytical assays (e.g., flow and qPCR‐based readouts). Global sensitivity analysis highlighted relative antigen expression, maximum killing rate constant, and CAR‐T expansion rate constant as major determinants for observed exposure of dual‐targeted CAR‐T‐cell therapy. This modeling framework could facilitate dose‐optimization and construct refinement for dual‐targeted bicistronic CAR‐T‐cell therapies, serving as a valuable tool for both forward and reverse translation in drug development. [ABSTRACT FROM AUTHOR]

The radiobiological mechanisms behind the favorable response of tissues to microbeam radiation therapy (MRT) are not fully described yet. Among other factors, the differential action to tumor and normal tissue vasculature is considered to contribute to MRT efficacy. This computational study evaluates the relevance of tumor growth stage and associated vascular redistribution to this effect. A multiscale approach was employed with two simulation softwares: TOPAS and CompuCell3D. Segmentation images of the angioarchitecture of a non-bearing tumor mouse brain were used. The tumor vasculature at different tumor growth stages was obtained by simulating the tumor proliferation and spatial vascular redistribution. The radiation-induced damage to vascular cells and consequent change in oxygen perfusion were simulated for normal and tumor tissues. The multiscale model showed that oxygen perfusion to tissues and vessels decreased as a function of the tumor proliferation stage, and with the decrease in uniformity of the vasculature spatial distribution in the tumor tissue. This led to an increase in the fraction of hypoxic (up to 60%) and necrotic (10%) tumor cells at advanced tumor stages, whereas normal tissues remained normoxic. These results showed that tumor stage and spatial vascular distribution contribute to the preferential effect of MRT in tumors. [ABSTRACT FROM AUTHOR]

Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disease that affects about 0.1% to 2% of the population worldwide. Despite the development of several novel therapies, there is only limited benefit for many patients. Thus, there is room for new approaches to improve response to therapy, including designing better trials e.g., by identifying subpopulations that can benefit from specific classes of therapy and enabling reverse translation by analyzing completed clinical trials. We have developed an open-source, mechanistic multi-scale model of RA, which captures the interactions of key immune cells and mediators in an inflamed joint. The model consists of a treatment-naive Virtual Population (Vpop) that responds appropriately (i.e. as reported in clinical trials) to standard-of-care treatment options—Methotrexate (MTX) and Adalimumab (ADA, anti-TNF-α) and an MTX inadequate responder sub-population that responds appropriately to Tocilizumab (TCZ, anti-IL-6R) therapy. The clinical read-outs of interest are the American College of Rheumatology score (ACR score) and Disease Activity Score (DAS28-CRP), which is modeled to be dependent on the physiological variables in the model. Further, we have validated the Vpop by predicting the therapy response of TCZ on ADA Non-responders. This paper aims to share our approach, equations, and code to enable community evaluation and greater adoption of mechanistic models in drug development for autoimmune diseases. [ABSTRACT FROM AUTHOR]

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature. [ABSTRACT FROM AUTHOR]

The failure of immunotherapies in cancer patients is being widely studied due to the complexities present in the tumor microenvironment (TME), where regulatory T cells (Treg) appear to actively participate in providing an immune escape mechanism for tumors. Therefore, therapies to specifically inhibit tumor-infiltrating Treg represent a challenge, because Treg are distributed throughout the body and provide physiological immune homeostasis to prevent autoimmune diseases. Characterization of immunological and functional profiles could help to identify the mechanisms that need to be inhibited or activated to ensure Treg modulation in the tumor. To address this, quantitative in silico approaches based on mechanistic mathematical models integrating multi-scale information from immune and tumor cells and the effect of different therapies have allowed the building of computational frameworks to simulate different hypotheses, some of which have subsequently been experimentally validated. Therefore, this review presents a list of diverse computational mathematical models that examine the role of Treg as a crucial immune resistance mechanism contributing to the failure of immunotherapy. In addition, this review highlights the relevance of certain molecules expressed in Treg that are associated with the TME immunosuppression, which could be incorporated into the mathematical model for a better understanding of the contribution of Treg modulation. Finally, different preclinical and clinical combinations of molecules are also included to show the trend of new therapies targeting Treg. [ABSTRACT FROM AUTHOR]

To solve the problem that traditional seed recognition methods are not completely suitable for needle-shaped seeds, such as Bidens L., in agricultural production, this paper proposes a model construction idea that combines the advantages of deep residual models in extracting high-level abstract features with multiscale feature extraction fusion, taking into account the depth and width of the network. Based on this, a multiscale feature fusion deep residual network (MSFF-ResNet) is proposed, and image segmentation is performed before classification. The image segmentation is performed by a popular semantic segmentation method, U2Net, which accurately separates seeds from the background. The multiscale feature fusion network is a deep residual model based on a residual network of 34 layers (ResNet34), and it contains a multiscale feature fusion module and an attention mechanism. The multiscale feature fusion module is designed to extract features of different scales of needle-shaped seeds, while the attention mechanism is used to improve the ability to select features of our model so that the model can pay more attention to the key features. The results show that the average accuracy and average F1-score of the multiscale feature fusion deep residual network on the test set are 93.81% and 94.44%, respectively, and the numbers of floating-point operations per second (FLOPs) and parameters are 5.95 G and 6.15 M, respectively. Compared to other deep residual networks, the multiscale feature fusion deep residual network achieves the highest classification accuracy. Therefore, the network proposed in this paper can classify needle-shaped seeds efficiently and provide a reference for seed recognition in agriculture. [ABSTRACT FROM AUTHOR]

The fatigue behavior of metal specimens is influenced by defects, material properties, and loading. This study aims to establish a multi-scale fatigue crack growth model that describes physically short crack (PSC) and long crack (LC) behavior. The model allows the calculation of crack growth rates for uniaxial loading at different stress ratios based on the material properties and specimen geometry. Furthermore, the model integrates the Gaussian distribution theory to consider material heterogeneity and the experimental measurement errors that cause fatigue scatter. The crack growth rate and fatigue life of metal specimens with different notch geometry were predicted. The curves generated by the multi-scale model were mainly consistent with the test data from the published literature at the PSC and LC stages. [ABSTRACT FROM AUTHOR]

In granular media, the crushing of individual particles is influenced by the number of contacts with neighbouring particles. This well-known phenomenon of "cushioning" shields the individual particles from crushing when the number of contacts is high. However, in open systems that involve extensive granular flow and bulk motion, like those found in industrial mills, the neighbouring particles continually exchange positions due to segregation and mixing, thereby altering the number of neighbouring contacts and their sizes, affecting the crushing of individual particles. Therefore, a critical challenge for properly modelling comminution in such systems lies in tracking the fluxes of the various particle size classes. Here, we explore the physics that governs the mechanisms of segregation and mixing within the multiscale heterarchical modelling paradigm. Building upon the framework developed in Part I, which integrated the heterarchical aspects of the physics of crushing along streamlines, we further account for segregation and mixing, and demonstrate their impact on the comminution efficiency of autogenous grinding mills. In particular, segregation is shown to greatly enhance the extent of particle crushing within the mill. Accordingly, we posit that this mechanism cannot be ignored. In summary, the new model sheds light on previously obscured dynamics within industrial mills, as well as enables the field to predict the time evolution of the particle size distribution at any point in the mill domain. This modelling capability opens the doors to new developments for estimating and improving milling efficiencies. [ABSTRACT FROM AUTHOR]

The tire/road noise is one of the major problems facing the tire industry due to the nuisance felt by the vehicle's driver and passengers. Moreover, it is expected in the coming years that this sound nuisance will be one of the main sources of vehicle noise due to the transition from combustion engine driven vehicles to electric vehicles. Tire manufacturers have therefore refined the design of their tire structures to find technological solutions to reduce traffic noise. Tire/road noise is also generated by various mechanisms which depend on different parameters such as the properties of the tire, road texture and driving conditions. This noise is partly caused by the acoustic radiation induced by the tire vibrations due to contacts with the road. The simulation and analysis of the tire's vibrations remains a challenge for the tire industry. Indeed, the simulation of the full dynamic response of a rolling patterned tire requires not only taking onto account various nonlinearities but also the multi-scale nature of the generated dynamic response. Contrary to a straightforward strategy that consists in using a time integrator to predict the multi-scale dynamic response, the strategy proposed in this study is based on a two-step approach to separate the dynamics occurring at different scales. The mathematical formulation of the proposed method is detailed as are the different modeling choices to simulate tire rolling under imposed load. The sensitivity of the tire's vibrations response is analyzed under different rolling conditions and with respect to certain key tire tread pattern parameters. • An adapted two-step approach to simulate nonlinear vibrations by separating the dynamics occurring at different scales. • Mathematical formulation of the proposed method with different modeling choices to simulate tire rolling under imposed loads. • Application to patterned tires rolling on a smooth surface. • Examples of the tire's vibrations response under different rolling conditions and key tire tread pattern parameters. [ABSTRACT FROM AUTHOR]

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

Falling damage is the most common form of damage sustained by kiwifruit during the process of picking and post-processing, and it is difficult to conduct a quantitative analysis of this phenomenon through traditional experimental methods. In order to deeply understand the sensitivity of kiwifruit to falling collision damage, the finite element numerical simulation method was used to evaluate and predict the sensitivity of kiwifruit to falling collision damage during harvesting. First, we obtained the appearance characteristics of kiwifruit through reverse engineering technology and determined the geometric and mechanical property parameters of kiwifruit through physical mechanics experiments. Then, according to the characteristics of fruit tissue structure, a multiscale finite element model, including the skin, pulp, and core, was constructed to simulate the effects of different falling heights, collision angles, and contact surface materials on fruit damage, and the accuracy of the model was verified through falling experiments. Finally, based on the simulation results, the Box–Behnken design was employed within the response surface methodology to establish a sensitivity prediction model for the drop damage sensitivity of kiwifruit across different contact materials. The results showed that the maximum relative error between the speed change obtained using finite element simulation and the speed obtained by the high-speed camera was 5.19%. The model showed high rationality in energy distribution, with the maximum value of hourglass energy not exceeding 0.08% of the internal energy. On the contact surface material with a large elastic modulus, a higher falling height and larger collision angle will significantly increase the risk of fruit bruise. When the contact surface material was a steel plate, the falling height was 1 m, and the collision angle was 90°; the maximum bruise sensitivity of kiwifruit reached 6716.07 mm3 J−1. However, when the contact surface material was neoprene, the falling height was 0.25 m, and the collision angle was 0°, the damage sensitivity was the lowest, at 1570.59 mm3 J−1. The multiscale finite element model of kiwifruit falling collision constructed in this study can accurately predict the damage of kiwifruit during falling collision and provide an effective tool for the quantitative analysis of kiwifruit falling collision damage. At the same time, this study can also provide guidance for the design and optimization of the loss reduction method of the harvesting mechanism, which has important theoretical significance and practical value. [ABSTRACT FROM AUTHOR]

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]

Creating statistically equivalent virtual microstructures (SEVM) for polycrystalline materials with complex microstructures that encompass multi-modal morphological and crystallographic distributions is a challenging enterprise. Cold spray-formed (CSF) AA7050 alloy containing coarse-grained prior particles and ultra-fine grains (UFG) and additively manufactured (AM) Ti64 alloys with alpha laths in beta substrates. The paper introduces an approach strategically integrating a Generative Adversarial Network (GAN) for multi-modal microstructures with a synthetic microstructure builder DREAM.3D for packing grains conforming to statistics in electron backscatter diffraction (EBSD) maps for generating SEVMs of CSF and AM alloy microstructures. A robust multiscale model is subsequently developed for self-consistent coupling of crystal plasticity finite element model (CPFEM) for coarse-grained crystals with an upscaled constitutive model for UFGs. Sub-volume elements are simulated for efficient computations and their responses are averaged for overall stress-strain response. The methods developed are important for image-based micromechanical modeling that is necessary for microstructure-property relations. This paper integrates GANs with the DREAM.3D code to generate virtual microstructures for addressing structure-property relations in complex multi-modal cold spray formed microstructures. A robust multiscale model accounts for grains at two scales. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) has long been the conventional method for targeting deep brain structures, but noninvasive alternatives like transcranial Temporal Interference Stimulation (tTIS) are gaining traction. Research has shown that alternating current influences brain oscillations through neural modulation. Understanding how neurons respond to the stimulus envelope, particularly considering tTIS's high-frequency carrier, is vital for elucidating its mechanism of neuronal engagement. This study aims to explore the focal effects of tTIS across varying amplitudes and modulation depths in different brain regions. An excitatory-inhibitory network using the Izhikevich neuron model was employed to investigate responses to tTIS and compare them with transcranial Alternating Current Stimulation (tACS). We utilized a multi-scale model that integrates brain tissue modeling and network computational modeling to gain insights into the neuromodulatory effects of tTIS on the human brain. By analyzing the parametric space, we delved into phase, amplitude, and frequency entrainment to elucidate how tTIS modulates endogenous alpha oscillations. Our findings highlight a significant difference in current intensity requirements between tTIS and tACS, with tTIS requiring notably higher intensity. We observed distinct network entrainment patterns, primarily due to tTIS's high-frequency component, whereas tACS exhibited harmonic entrainment that tTIS lacked. Spatial resolution analysis of tTIS, conducted via computational modeling and brain field distribution at a 13 Hz stimulation frequency, revealed modulation in deep brain areas, with minimal effects on the surface. Notably, we observed increased power within intrinsic and stimulation bands beneath the electrodes, attributed to the high stimulus signal amplitude. Additionally, Phase Locking Value (PLV) showed slight increments in non-deep areas. Our analysis indicates focal stimulation using tTIS, prompting further investigation into the necessity of high amplitudes to significantly affect deep brain regions, which warrants validation through clinical experiments. [ABSTRACT FROM AUTHOR]

Accurate material performance parameters are the prerequisite for conducting composite material structural analysis and design. However, the complex multiscale structure of ceramic matrix composites (CMCs) makes it extremely difficult to accurately obtain their mechanical performance parameters. To address this issue, a CMC micro-scale constituents (fiber bundles and matrix) elastic parameter inversion method was proposed based on the integration of macro–micro finite element models. This model was established based on the μCT scan data of a plain-woven CMC tensile specimen using the chemical vapor infiltration (CVI) process, which could reflect the real microstructure and surface morphology characteristics of the material. A BP neural network was used to predict the multiscale stiffness, considering the influence of the porous structure on the macroscopic stiffness of the material. The inversion process of the constituent elastic parameters was established using the trust-region algorithm combined with an improved error function. The inversion results showed that this method could accurately invert the CMC constituent elastic parameters with excellent robustness and anti-noise performance. Under four different degrees of deviation in the initial iteration conditions, the inversion error of all parameters was within 1%, and the maximum inversion error was only 2.16% under a 10% high noise level. [ABSTRACT FROM AUTHOR]

Sustainable Development Goal 11.7 (SDG 11.7) aims to promote the improvement of urban public spaces. However, the localization process of SDG 11.7 mainly relies on a bottom-up problem-solving approach, which fails to fully encompass the connotation of SDG 11.7. Additionally, existing evaluations primarily focus on a single scale, neglecting the impact of scale issues. These limitations can lead to imbalanced development or misallocation of responsibilities when guiding governments at different levels in promoting the sustainable development of public spaces. Therefore, this article introduces a multi-scale assessment model of SDG 11.7. It employs a top-down problem-solving approach to construct a sustainable development indicator framework, setting appropriate sustainable development indicators for various levels of government based on the connotation of SDG 11.7, and generates city-scale results by integrating three scales: apartment complexes, street blocks, and counties. Testing this model in Xi'an, China, revealed that it adequately captures four key aspects of SDG 11.7—safety, inclusiveness, accessibility, and greenness—through 11 indicators. The evaluation outcomes at the apartment complex, street block, and county levels effectively guide future development directions for various levels of government. Ultimately, the synthesis of these scales reveals the spatial pattern of SDG 11.7 at the city scale and identifies focal areas for development. Overall, this exploratory model demonstrates high accuracy and robustness, providing a comprehensive understanding of the essence of SDG 11.7. It also alleviates challenges posed by scale issues, offering decision support for monitoring SDG 11.7 across different levels of government in Chinese cities and promoting the process of sustainable development. [ABSTRACT FROM AUTHOR]

Natural plant fibers are hierarchical structures with multi-level microstructures. With advances in composite material science, these fibers have been widely used in various polymer products. Therefore, it is crucial to quantitatively understand the relationship between their microstructures and mechanical behavior. This paper utilizes the Mori-Tanaka micromechanics model, viscoelasticity theory, and Zakian's inversion method to study the impact of plant fiber microstructure on the viscoelastic behavior of multiscale structures. At the microscopic scale, the macromolecular polymer (matrix) and cellulose (fiber) are first homogenized. The second homogenization involves the cell wall microstructure, and the third homogenization considers the porosity of the cell wall and lumen to predict the effective modulus of fiber bundles. By applying the principle of elasticviscoelastic correspondence, the viscoelastic mechanical parameters of plant fibers are calculated. The study examines the effects of cellulose crystallinity and lumen porosity on the structural stiffness and viscoelastic properties of fibers, identifying these factors as key influences on the mechanical behavior of plant fibers. Given their significant economic potential, the feasibility of using tobacco plant fibers as bio-based materials is also explored. [ABSTRACT FROM AUTHOR]

Inspired by the success of large language models (LLMs), we develop a long-context generative model for genomes. Our multiscale transformer model, megaDNA, is pre-trained on unannotated bacteriophage genomes with nucleotide-level tokenization. We demonstrate the foundational capabilities of our model including the prediction of essential genes, genetic variant effects, regulatory element activity and taxonomy of unannotated sequences. Furthermore, it generates de novo sequences up to 96 K base pairs, which contain potential regulatory elements and annotated proteins with phage-related functions. MegaDNA, a long-context genomic language model, generates DNA sequences up to 96 K base pairs with annotated proteins and potential regulatory elements. It predicts essential genes, genetic variant effects, and regulatory element activity. [ABSTRACT FROM AUTHOR]

The quantification of interphase properties between metals and their corresponding hydrides is crucial for modeling the thermodynamics and kinetics of the hydrogenation processes in solid-state hydrogen storage materials. In particular, interphase boundary energies assume a pivotal role in determining the kinetics of nucleation, growth, and coarsening of hydrides, alongside accompanying morphological evolution during hydrogenation. The total interphase energy arises from both chemical bonding and mechanical strains in these solid-state systems. Since these contributions are usually coupled, it is challenging to distinguish via conventional computational approaches. Here, a comprehensive atomistic modeling methodology is developed to decouple chemical and mechanical energy contributions using first-principles calculations, of which feasibility is demonstrated by quantifying chemical and elastic strain energies of key interfaces within the FeTi metal-hydride system. Derived materials parameters are then employed for mesoscopic micromechanical analysis, predicting crystallographic orientations in line with experimental observations. The multiscale approach outlined verifies the importance of the chemo-mechanical interplay in the morphological evolution of growing hydride phases, and can be generalized to investigate other systems. In addition, it can streamline the design of atomistic models for the quantitative evaluation of interphase properties between dissimilar phases and allow for efficient predictions of their preferred phase boundary orientations. [ABSTRACT FROM AUTHOR]

Numerous threats affect aquatic ecosystems at different biological organizational levels from individuals to ecosystems. Stresses occurring on the metabolism and physiological functions of individuals can have repercussions on the individual behavior, its ability to survive and reproduce, also known as the individual fitness, which may then influence the demography and spatial distribution of populations, and ultimately modify trophic flows and ecosystem functioning. In a context of a globally changing environment, predicting the life history traits and fitness of individuals can be relevantly performed with the association of laboratory experiments with Dynamic Energy Budget (DEB) theory, while modeling species interactions have proven to be an efficient tool to understand aquatic food webs using mass-balanced models such as linear inverse models (LIMs) or Chance and Necessity (CaN) models. However, while predictive results obtained on individuals can be provided with a thorough mechanistic interpretation, the propagation of the effects is most often limited to the closest biological hierarchical level, i.e., the population, and rarely to the food-web level. Furthermore, there is a need to understand how to avoid misleading approaches and interpretations due to the simplicity of experiments. For the moment, no clear methodology has stood out yet to do so. In this study, we provide a new methodology based on a combination of models (i.e., DEB, LIM, and CaN) aiming at upscaling information from laboratory experiments on individuals to ecosystems to address multiple ecological issues. This framework has a potential to enhance our understanding of higher-scale consequences of the effect of stressors measured at the sub-individual scale. This combination of models was chosen for the convergence of their framework but also their ability to consider a substantial portion of the projected uncertainty. The description of this methodology can help experimenters and modelers to jointly address a specific question involving upscaling from individual to ecosystem, proposes approaches, and gives tips on the pitfalls to avoid along the upscaling process. [ABSTRACT FROM AUTHOR]

The application of carbon fibers reinforced carbon matrix (C/C) composite can solve the local wear of metallic finger seals effectively. However, the performance of C/C composite finger seal is complex and variable, which further decreases the sealing performance and life. Therefore, a method of multi-scale modeling and mechanical performance analysis for plain woven C/C composite finger seals was conducted. The circumferential finger beams of C/C composite were modeled by multi-scale structural analysis and weaving simulation. The radial static and dynamic stiffness characteristics of finger beams were investigated. The results showed that the radial static stiffness of the finger beam with three layers was about 3 times that with single layer. The radial stiffness of circumferential finger beams presented a periodic distribution pattern with a period of 90°. The radial dynamic stiffness of C/C composite finger beams increased with the excitation displacement amplitude and rotor speed. But the magnitude and fluctuation degree of dynamic stiffness were greater than those of static stiffness. A large difference in radial stiffness will lead to local wear and hysteretic leakage. This study lays a foundation for the analysis and optimization of the hysteresis and wear characteristics of C/C composite finger seals. [ABSTRACT FROM AUTHOR]

In smart classroom environments, accurately recognizing students' facial expressions is crucial for teachers to efficiently assess students' learning states, timely adjust teaching strategies, and enhance teaching quality and effectiveness. In this paper, we propose a student facial expression recognition model based on multi-scale and deep fine-grained feature attention enhancement (SFER-MDFAE) to address the issues of inaccurate facial feature extraction and poor robustness of facial expression recognition in smart classroom scenarios. Firstly, we construct a novel multi-scale dual-pooling feature aggregation module to capture and fuse facial information at different scales, thereby obtaining a comprehensive representation of key facial features; secondly, we design a key region-oriented attention mechanism to focus more on the nuances of facial expressions, further enhancing the representation of multi-scale deep fine-grained feature; finally, the fusion of multi-scale and deep fine-grained attention-enhanced features is used to obtain richer and more accurate facial key information and realize accurate facial expression recognition. The experimental results demonstrate that the proposed SFER-MDFAE outperforms the existing state-of-the-art methods, achieving an accuracy of 76.18% on FER2013, 92.75% on FERPlus, 92.93% on RAF-DB, 67.86% on AffectNet, and 93.74% on the real smart classroom facial expression dataset (SCFED). These results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Advancements in satellite and aerial imagery technology have made it easier to obtain high-resolution remote sensing images, leading to widespread research and applications in various fields. Remote sensing image semantic segmentation is a crucial task that provides semantic and localization information for target objects. In addition to the large-scale variation issues common in most semantic segmentation datasets, aerial images present unique challenges, including high background complexity and imbalanced foreground–background ratios. However, general semantic segmentation methods primarily address scale variations in natural scenes and often neglect the specific challenges in remote sensing images, such as inadequate foreground modeling. In this paper, we present a foreground-aware remote sensing semantic segmentation model. The model introduces a multi-scale convolutional attention mechanism and utilizes a feature pyramid network architecture to extract multi-scale features, addressing the multi-scale problem. Additionally, we introduce a Foreground–Scene Relation Module to mitigate false alarms. The model enhances the foreground features by modeling the relationship between the foreground and the scene. In the loss function, a Soft Focal Loss is employed to focus on foreground samples during training, alleviating the foreground–background imbalance issue. Experimental results indicate that our proposed method outperforms current state-of-the-art general semantic segmentation methods and transformer-based methods on the LS dataset benchmark. [ABSTRACT FROM AUTHOR]

Chemical vapor deposition (CVD) is a crucial technique in the preparation of high-quality thin films and coatings, and is widely used in various industries including semiconductor, optics, and nuclear fuel, due to its operation simplicity and high growth rate. The complexity of the CVD process arises from numerous parameters, such as precursor chemistry, temperature, pressure, gas flow dynamics, and substrate characteristics. These multiscale parameters make the optimization of the CVD process a challenging task. Numerical simulations are widely used to model and analyze the CVD complex systems, and can be divided into nanoscale, mesoscale, and macroscale methods. Numerical simulation is aimed at optimizing the CVD process, but the inter-scale parameters still need to be extracted in modeling processes. However, multiscale coupling modeling becomes a powerful method to solve these challenges by providing a comprehensive framework that integrates phenomena occurring at different scales. This review presents an overview of the CVD process, the common critical parameters, and an in-depth analysis of CVD models in different scales. Then various multiscale models are discussed. This review highlights the models in different scales, integrates these models into multiscale frameworks, discusses typical multiscale coupling CVD models applied in practice, and summarizes the parameters that can transfer information between different scales. Finally, the schemes of multiscale coupling are given as a prospective view. By offering a comprehensive view of the current state of multiscale CVD models, this review aims to bridge the gap between theory and practice, and provide insights that could lead to a more efficient and precise control of the CVD process. [ABSTRACT FROM AUTHOR]

This study employs a microstructure-based finite element modeling approach to understand the mechanical behavior of asphalt mixtures across different length scales. Specifically, this work aims to develop a multi-scale modeling approach employing representative volume elements (RVEs) of optimal size; this is a key issue in asphalt modeling for high-fidelity fracture modeling of heterogeneous asphalt mixtures. To determine the optimal RVE size, a convergence analysis of homogenized elastic properties is conducted using two types of RVEs, one made with polydisperse spherical inclusions, and another made with polydisperse truncated cylindrical inclusions, each aligned with the American Association of State Highway and Transportation Official's maximum density gradation curve for a 12.5 mm Nominal Maximum Aggregate Size (NMAS). The minimum RVE lengths for this NMAS were found to be in the range of 32–34 mm. After the optimal RVE size for each inclusion shape is obtained, computational models of heterogeneous Indirect Tensile Asphalt Cracking Test samples are then generated. These models include the components of viscoelastic mastic, linear elastic aggregates, and cohesive zone modeling to simulate the rate-dependent failure evolution from micro- to macro-cracking. Examination of load-displacement responses at multiple loading rates shows that both heterogeneous models replicate experimentally measured data satisfactorily. Through micro- and macro-level analyses, this study enhances our understanding of the composition-performance relationships in asphalt pavement materials. The procedure proposed in this study allows us to identify the optimal RVE sizes that preserve computational efficiency without significantly compromising their ability to capture the asphalt material behavior under specific operational conditions. [ABSTRACT FROM AUTHOR]

When implementing outside-the-window (OTW) visuals in aviation tactical simulators, maintaining terrain image color consistency is critical for enhancing pilot immersion and focus. However, due to various environmental factors, inconsistent image colors in terrain can cause visual confusion and diminish realism. To address these issues, a color correction technique based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is proposed. The proposed WGAN-GP model utilizes multi-scale feature extraction and Wasserstein distance to effectively measure and adjust the color distribution difference between the input image and the reference image. This approach can preserve the texture and structural characteristics of the image while maintaining color consistency. In particular, by converting Bands 2, 3, and 4 of the BigEarthNet-S2 dataset into RGB images as the reference image and preprocessing the reference image to serve as the input image, it is demonstrated that the proposed WGAN-GP model can handle large-scale remote sensing images containing various lighting conditions and color differences. The experimental results showed that the proposed WGAN-GP model outperformed traditional methods, such as histogram matching and color transfer, and was effective in reflecting the style of the reference image to the target image while maintaining the structural elements of the target image during the training process. Quantitative analysis demonstrated that the mid-stage model achieved a PSNR of 28.93 dB and an SSIM of 0.7116, which significantly outperforms traditional methods. Furthermore, the LPIPS score was reduced to 0.3978, indicating improved perceptual similarity. This approach can contribute to improving the visual elements of the simulator to enhance pilot immersion and has the potential to significantly reduce time and costs compared to the manual methods currently used by the Republic of Korea Air Force. [ABSTRACT FROM AUTHOR]

This paper examines the multi-scale super-resolution challenge of digital elevation models in remote sensing. A dual-domain multi-scale attention fusion network is proposed, which reconstructs digital elevation image details step-by-step using cascading sub-networks. This model incorporates components like the wavelet guidance and separation module, multi-scale attention fusion blocks, dilated convolutional inception module, and edge enhancement module to improve feature extraction and fusion capabilities. A new loss function is designed to enhance the model's robustness and stability. Experiments indicate that the proposed model outperforms 15 benchmark models in PSNR, RMSE, MAE, R M S E s l o p e , and R M S E a s p e c t metrics. In HMA data, The proposed model's PSNR increases by 0.89 dB (~1.81%), and RMSE decreases by 1.22 m (~8.6%) compared to a state-of-the-art model. Compared to EDEM, which has the best elevation index, R M S E s l o p e decreases by 0.79 ° (~16%). Additionally, the effectiveness and contribution of each DDMAFN component were verified through ablation experiments. Finally, on the SRTM dataset, The proposed model demonstrates superior performance even with interpolated degradation. [ABSTRACT FROM AUTHOR]

Network is an essential tool today, and the Intrusion Detection System (IDS) can ensure the safe operation. However, with the explosive growth of data, current methods are increasingly struggling as they often detect based on a single scale, leading to the oversight of potential features in the extensive traffic data, which may result in degraded performance. In this work, we propose a novel detection model utilizing multi-scale transformer namely IDS-MTran. In essence, the collaboration of multi-scale traffic features broads the pattern coverage of intrusion detection. Firstly, we employ convolution operators with various kernels to generate multi-scale features. Secondly, to enhance the representation of features and the interaction between branches, we propose Patching with Pooling (PwP) to serve as a bridge. Next, we design multi-scale transformer-based backbone to model the features at diverse scales, extracting potential intrusion trails. Finally, to fully capitalize these multi-scale branches, we propose the Cross Feature Enrichment (CFE) to integrate and enrich features, and then output the results. Sufficient experiments show that compared with other models, the proposed method can distinguish different attack types more effectively. Specifically, the accuracy on three common datasets NSL-KDD, CIC-DDoS 2019 and UNSW-NB15 has all exceeded 99%, which is more accurate and stable. [ABSTRACT FROM AUTHOR]

Classifying emission reduction zones on different scales has important implications for the ecological protection and high‐quality development of the Yellow River Basin. Based on remote sensing data and a light‐carbon conversion model, carbon footprints at provincial, municipal, and county scales in the Yellow River Basin are measured. The spatiotemporal evolution critical paths of carbon footprints at the three spatial scales are compared and classified into different zones using spatiotemporal evolution analysis methods. The conclusions are as follows: (1) The carbon footprint increased over the years. The spatial distributions of carbon footprints at the three scales are not only consistent but also different. The study of carbon footprints at the county scale is more conducive to the summary of the spatiotemporal evolution and the formulation of detailed emission reduction schemes. (2) Four provinces, 48 cities, and 373 counties are designated as a "core protected zone"; three provinces, 29 cities, and 177 counties are designated as a "strictly governed zone"; one province, 12 cities, and 47 counties are designated as a "key restricted zone"; four cities and 39 counties are designated as an "alert diffusion zone." (3) The agglomeration expansion trend and the spillover effect of high‐carbon footprint units at the county scale are more obvious. Further enhancement of the path‐locking characteristics of the carbon footprints of counties will make governance more difficult. Effective governance of carbon footprint at the county scale is of urgent concern. These results provide scientific evidence for multiscale carbon emission control and zoning policy formulation in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Metamaterials are a type of groundbreaking engineered materials with unique properties not found in natural substances. Lattice metamaterials, which have a periodic lattice cell structure, possess exceptional attributes such as a negative Poisson's ratio, high stiffness-to-weight ratios, and outstanding energy dissipation capabilities. This review provides a comprehensive examination of lattice metamaterials. It covers their various structures and fabrication methods. The review emphasizes the crucial role of homogenization methods and multi-scale modeling in assessing metamaterial properties. It also highlights the advancement of topology optimization through advanced computational techniques, such as finite element analysis simulations and machine learning algorithms. [ABSTRACT FROM AUTHOR]

Contact interface is essential for the dynamic response of the bolted structures. To accurately predict the dynamic characteristics of bolted joint structures, a fractal extension of the segmented scale model, i.e., the JK model, is proposed in this paper to comprehensively analyze the dynamic contact performance of engineering surfaces and revisit the multi-scale model based on the concept of asperities. The influence of asperity geometry, dimensionless material properties, and the elastic, elastoplastic, and full plastic mechanical models of a single asperity is established considering the asperity–substrate interaction. Then, a segmented scale contact model of rough surfaces is proposed based on the island distribution function in a strict sense. The mechanical contact process of determining rough surfaces is divided into small-scale, medium-scale, and large-scale stages. Moreover, cross-scale boundary conditions, i.e., al1′, al2′, and al3′, are provided through strict mathematical deduction. The results show that the real contact area and contact stiffness are positively correlated with fractal dimension and negatively correlated with fractal roughness. On a small scale, the contact damping decreases with an increase in load. In meso-scale and large-scale stages, the contact damping increases with the load. Finally, the reliability of the proposed model is verified by setting up three groups of modal vibration experiments. [ABSTRACT FROM AUTHOR]

In a recently published work, we introduced local Legendre polynomial fitting-based permutation entropy (LPPE) as a new complexity measure for quantifying disorder or randomness in time series. LPPE benefits from the ordinal pattern (OP) concept and incorporates a natural, aliasing-free multiscaling effect by design. The current work extends our previous study by investigating LPPE's capability to assess fatigue levels using both synthetic and real surface electromyography (sEMG) signals. Real sEMG signals were recorded during biceps brachii fatiguing exercise maintained at 70% of maximal voluntary contraction (MVC) until exhaustion and were divided into four consecutive temporal segments reflecting sequential stages of exhaustion. As fatigue levels rise, LPPE values can increase or decrease significantly depending on the selection of embedding dimensions. Our analysis reveals two key insights. First, using LPPE with limited embedding dimensions shows consistency with the literature. Specifically, fatigue induces a decrease in sEMG complexity measures. This observation is supported by a comparison with the existing multiscale permutation entropy (MPE) variant, that is, the refined composite downsampling (rcDPE). Second, given a fixed OP length, higher embedding dimensions increase LPPE's sensitivity to low-frequency components, which are notably present under fatigue conditions. Consequently, specific higher embedding dimensions appear to enhance the discrimination of fatigue levels. Thus, LPPE, as the only MPE variant that allows a practical exploration of higher embedding dimensions, offers a new perspective on fatigue's impact on sEMG complexity, complementing existing MPE approaches. [ABSTRACT FROM AUTHOR]

The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design. [ABSTRACT FROM AUTHOR]

Understanding wood combustion has become increasingly critical as fire safety engineering moves toward a performance‐based approach to building design. Although different kinetic models have been developed for wood burning, chemical kinetics remains a significant challenge for accurate prediction. This work has developed a novel multiscale model by implementing kinetic parameters calculated from molecular dynamics simulations using reactive forcefield into a kinetic model of wood burning. The calculated kinetic parameters of the main components in wood, namely, cellulose, hemicellulose, and lignin, are first utilized to model microscale thermogravimetric experiments for validation. Subsequently, the mesoscale and full‐scale fire tests have been simulated by the multiscale model. Furthermore, the fire properties of various wood species at different heat fluxes have been predicted and compared with results from the cone calorimeter test. Our multiscale model outperforms existing kinetic models in predicting wood combustion and can effectively discern the influence of chemical components on fire properties. [ABSTRACT FROM AUTHOR]

Strip steel surface defect detection has become a crucial step in ensuring the quality of strip steel production. To address the issues of low detection accuracy and long detection times in strip steel surface defect detection algorithms caused by varying defect sizes and blurred images during acquisition, this paper proposes a lightweight strip steel surface defect detection network, YOLO-SDS, based on an improved YOLOv8. Firstly, StarNet is utilized to replace the backbone network of YOLOv8, achieving lightweight optimization while maintaining accuracy. Secondly, a lightweight module DWR is introduced into the neck and combined with the C2f feature extraction module to enhance the model's multi-scale feature extraction capability. Finally, an occlusion-aware attention mechanism SEAM is incorporated into the detection head, enabling the model to better capture and process features of occluded objects, thus improving performance in complex scenarios. Experimental results on the open-source NEU-DET dataset show that the improved model reduces parameters by 34.4% compared with the original YOLOv8 algorithm while increasing average detection accuracy by 1.5%. And it shows good generalization performance on the deepPCB dataset. Compared with other defect detection models, YOLO-SDS offers significant advantages in terms of parameter count and detection speed. Additionally, ablation experiments validate the effectiveness of each module. [ABSTRACT FROM AUTHOR]