Multi-scale Characteristics Research Articles

In vivo co-registered hybrid-contrast imaging by successive photoacoustic tomography and magnetic resonance imaging

Magnetic resonance imaging (MRI) and photoacoustic tomography (PAT) offer two distinct image contrasts. To integrate these two modalities, we present a comprehensive hardware-software solution for the successive acquisition and co-registration of PAT and MRI images in in vivo animal studies. Based on commercial PAT and MRI scanners, our solution includes a 3D-printed dual-modality imaging bed, a 3-D spatial image co-registration algorithm with dual-modality markers, and a robust modality switching protocol for in vivo imaging studies. Using the proposed solution, we successfully demonstrated co-registered hybrid-contrast PAT-MRI imaging that simultaneously displays multi-scale anatomical, functional and molecular characteristics on healthy and cancerous living mice. Week-long longitudinal dual-modality imaging of tumor development reveals information on size, border, vascular pattern, blood oxygenation, and molecular probe metabolism of the tumor micro-environment at the same time. The proposed methodology holds promise for a wide range of pre-clinical research applications that benefit from the PAT-MRI dual-modality image contrast.

Fusion-based color and depth image segmentation method for rocks on conveyor belt

Semi-autogenous mill (SAG) feeding process requires accurate and reliable monitoring and controlling techniques for multi-sized rock distribution to stabilize crushing productivity. This study presents a color and depth (RGB-D) image fusion algorithm for the segmentation of multiscale rock images from the conveyor belt. The algorithm combines the multiscale characteristics of the color image and depth image, considers the extraction of large particles from the depth image and the rich texture details for segmentation of the color image, divides the segmentation regions according to different particle sizes, and finally obtains the contour of each particle after region-based superpixels merging. The proposed method outperforms any single sensor image segmentation method proposed in direct comparison, thereby attaining the closest to ground truth performance. The comparison results also show that the accuracy and adaptability of the algorithm are significantly improved, and this algorithm can therefore be used in rock-like material segmentation with multiple size distribution on conveyors.

Institutional and technical history of requirements‐based strategic armor ceramics basic research leading up to the multiscale material by design materials in extreme dynamic environments (MEDE) program. Part II: Dynamic effects on the physics and mechanisms of advanced ceramics such as boron carbide

AbstractThis paper follows a historical background on requirements‐based strategic armor research, leading to the materials in extreme dynamic environments program presented in Part I, now focusing on the developed technical aspects and state‐of‐the‐art. It starts with some background on dynamic testing techniques and a structural ceramics review. Then, selected armor ceramics research results and relevant single‐grain anisotropic crystal physics, microstructure, and defect mechanics mechanisms: Including pivotal armor ceramics research results prior to the adoption of the strategic research objective (SRO). Next, multiscale characteristics, crystal physics, planar features, anisotropy, and relevant mechanisms will be described. The historic progression/evolution of multiscale lightweight armor ceramics research results will be summarized, including multiscale dynamic deformation and damage characteristics. The focus of the following sections will be on the role of defects, quasi‐plasticity, and anisotropic crystal physics properties, including preexisting single grain synthesis and process‐induced planar features (aka twins) and planar deformation features (PDF); for example, nano‐amorphization in boron carbide. A new model for boron carbide processing planar features will be discussed. A schematic diagram illustrating the hypothetical formation of PDFs in a dynamic event is also presented. An expended canonical equation is introduced, suggesting possible strategies for boron carbide research using the canonical figures of merit approach. Finally, we highlight the efficacy of the materials by design process and approach in a multiscale framework for the simultaneous experimental and theoretical research trajectories guided by the accepted canonical equation.

Characterization method of core pore structure based on truncated Gaussian and its application in shale cores

The core pore structure is mainly described by fractal theory. Conventional single fractal models cannot express the multi-peak distribution characteristics of coal rock, shale, carbonate rock, and other cores. A multifractal model has numerous dimensions and discontinuous functions, making the model parameter selection difficult. Based on the capillary bundle theory and the truncated Gaussian distribution model, a model for characterizing the multi-peak distribution structure of cores is developed in this study. The model's accuracy is verified using the tested NMR experimental data of shale bimodal distribution and the cited coal trimodal distribution. This study further analyzes the influence of tortuosity and tortuosity fractal dimensions on this model. The results show that tortuosity and tortuosity fractal dimensions are conducive to improving the accuracy of the multi-peak pore structure model. The structure model, which can characterize the multi-scale and multi-peak distribution characteristics of shale pore diameter, provides a new concept for describing the pore structure of porous media.

The multi-scale mechanical properties of calcium-silicate-hydrate

The mechanical properties of calcium-silicate-hydrates (C–S–H) directly affect the performance of cement-based materials. C–S–H spans ranging from nanometers to micrometers, and has typical multi-scale characteristics which are outlined in this review. The advantages and disadvantages of different methods for quantifying multi-scale mechanical properties of C–S–H are firstly highlighted. The C–S–H micromechanical properties obtained from nanoindentation are discussed in details including the comparison between the properties of C–S–H obtained cement hydration and chemical synthesis. The influence factors of the mechanical properties of C–S–H are then illustrated. The Ca/Si and incorporation of Al are considered to affect the mechanical properties by modifying the chemical composition and structure of C–S–H. The effects of other impurity phases inevitably mixed with C–S–H on its mechanical properties are also revealed. The multi-scale toughening strategy of C–S–H relies on its enhancement on the structural ordering and interface connection.

Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning.

The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie.

Review of research on evaluating the ecological security of cultivated land

Cultivated land provides fundamental land-related resources, and its ecological security is, thus, an important means of protecting it. The ecological security of cultivated land has emerged as an important and challenging area of research in recent years. In this study, we summarize the progress in research on the evaluation of the ecological security of cultivated land through visual analysis. We review the concepts, characteristics, driving factors, scales and methods of evaluation, technologies, and simulations used in the relevant literature. The results show that while the relevant concept has been preliminarily established, research on the ecological security of cultivated land remains in its infancy, and comprehensive work on the subject is lacking. The Prevalent research has mainly focused on analyzing the current situation, but lacks a dynamic analysis of the driving mechanism of the ecological security of cultivated land based on simulations. This has made it difficult to understand the spatiotemporal mechanism of the ecological security of cultivated land. Future research in the area should discuss the complex driving mechanism of interactions between the social economy system and the ecological system and focus on an integrated model to assess its dynamic spatial and multi-scale characteristics of ecological security of cultivated land because this can inform the theory of protecting cultivated land and the design of plans for land use to mitigate global climate change.

Combined Forecasting Model of Precipitation Based on the CEEMD-ELM-FFOA Coupling Model

Precipitation prediction is an important technical mean for flood and drought disaster early warning, rational utilization, and the development of water resources. Complementary ensemble empirical mode decomposition (CEEMD) can effectively reduce mode aliasing and white noise interference; extreme learning machines (ELM) can predict non-stationary data quickly and easily; and the fruit fly optimization algorithm (FFOA) has better local optimization ability. According to the multi-scale and non-stationary characteristics of precipitation time series, a new prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), extreme learning machine (ELM), and the fruit fly optimization algorithm (FFOA) is proposed. The monthly precipitation data measured in Zhengzhou City from 1951 to 2020 was taken as an example to conduct a prediction experiment and compared with three prediction models: ELM, EMD-HHT, and CEEMD-ELM. The research results show that the sum of annual precipitation predicted by the CEEMD-ELM-FFOA model is 577.33 mm, which is higher than the measured value of 572.53 mm with an error of 4.80 mm. The average absolute error is 0.81 and the average relative error is 1.39%. The prediction value of the CEEMD-ELM-FFOA model can closely follow the changing trend of precipitation, which shows a better prediction effect than the other three models and can be used for regional precipitation prediction.

Coupling effect of P, Ni micro-alloying and thermomechanical processing on microstructure evolution and high formability of Al-Mg-Si-Cu-Zn-Fe alloys

The coupling effect of P, Ni micro-alloying and thermomechanical processing on the mechanical properties and microstructure evolution of a novel Al-Mg-Si-Cu-Zn-Fe alloy was systematically studied. The results show that, both the P-added and Ni-added alloys exhibit significantly improved formability and strengths due to the changed distribution of multi-scale particles and its effect on recrystallization microstructure. However, when the Ni content reaches 0.1 wt%, unbreakable AlNiFe phase tends to be formed, inducing a strength loss. The formation mechanism of multi-scale particles distribution characteristics and its effects on deformation and recrystallization microstructure of the alloys were deeply discussed. Additionally, the high formability mechanism of P-added and Ni-added alloys have been also put forward. A better understanding of them provides novel visions for the development of Al alloys with high formability.

Multi-scale spectral-spatial dual-transformer network for hyperspectral image classification

ABSTRACT Deep learning methods have shown great advantages in hyperspectral image (HSI) classification tasks. In particular, convolutional neural network (CNN)-based methods for HSI classification have made great progress. However, it is difficult for CNN to process long-range spatial and spectral information. Recently, a novel deep learning model called transformer has demonstrated its potential to replace CNN in various classification tasks with its amazing performance. In this letter, a multi-scale spectral-spatial dual-transformer network (MS3DT) is proposed to deeply consider the spectral-spatial features via transformer. Specifically, MS3DT consists of a feature pyramid network (FPN), a spectral transformer subnetwork (SPECT) and a spatial transformer subnetwork (SPAT). To utilize the complementary multi-scale characteristics, we introduce FPN to capture shallow-to-deep and spectral-spatial features. To improve the representational capacity in spatial and spectral domains, SPECT is exploited to extract long-range spectral correlation over local spectral features, and SPAT is designed to explore the contextual information for better refinement. Therefore, MS3DT is able to adaptively recalibrate the nonlinear interdependence of shallow-to-deep spectral-spatial features by merging the two subnetworks. Experiments on two widely used HSI datasets show that the MS3DT can outperform the state-of-the-art (SOTA) algorithm. The source codes will be available at https://github.com/RsAI-lab/MSSSDT.

Evaluation method of underground water storage space and thermal reservoir model in abandoned mine

A large number of mines are closed or abandoned every year in China. Geothermal utilization is one of the important ways to efficiently reuse underground resources in abandoned mines. How to calculate the volume and distribution of underground water storage space is the key to accurately evaluate the sustainable geothermal production in abandoned mines. In this paper, according to the multi-scale characteristics of the underground space in abandoned mine, the flow and heat transfer equations in the multi-scale space are sorted out systematically, and the calculation methods of different secondary space volumes are derived in detail. Taking Jiahe abandoned mine as the background, the volume and distribution of underground secondary space are calculated, and three heat storage evaluation models considering different water storage spaces are established by using COMSOL. The simulation results show that there are great differences among different models, and the results of the equivalent porous media model considering the multi-scale space are most consistent with the reality. Sensitivity analyses of key parameters model results indicated that the heat production is closely related to not only the recharge flow rate but also the recharge temperature and operating time. Furthermore, the energy saving and emission reduction benefits of geothermal utilization in abandoned mines are calculated, the results show that geothermal utilization of abandoned mines can effectively reduce energy consumption and CO2 emissions, and it has great economic benefits.

Numerical Investigation of the Multiscale Characteristics of Elastoplasticity in Microheterogeneous Continuum Media Using a Simple Hollow Sphere Model

In this paper, we investigate the fundamental characteristics of plasticity in microheterogeneous continuum materials. Plasticity in such materials is a multiscale phenomenon with contrasting characteristics at different scales. A hollow sphere model, including a matrix embedding an oblate spheroidal pore, is considered as a sample representative elementary volume (REV) of a microheterogeneous material where, at the local level (microscale), a simple Drucker–Prager plasticity model is assumed for the matrix obeying the classical continuum theory of plasticity. The plasticity model is Lode angle independent with associated plastic flow and no hardening. We perform finite element numerical simulations on the REV to investigate the characteristics of the homogenized plasticity of the REV. It is seen that the homogenized plasticity shows complex characteristics such as Lode angle dependency, presence of cap, hardening, and nonassociativity. Well-established as these aspects are in the literature, they are often presumed in the plasticity models of heterogeneous materials in an ad hoc form. In contrast, we present here a systematic and lucid way to capture these aspects as emergent phenomena of heterogeneous microstructures. This has crucial implications in guiding the theoretical constitutive developments for elastoplastic materials toward adopting a multiscale approach that properly considers the effect of microstructure and naturally captures these aspects without the need for ad hoc assumptions. A fundamental assumption in the classical theory of plasticity is the plastic flow rule postulate, which states that the direction of plastic strain increment is governed only by the current state variables, independent of the loading increment direction. We show, through a stress probing exercise in 3D principal stress space, that the flow rule, assumed for the microscale plasticity, is not valid for the macroscopic plasticity. Indeed, the homogenized plastic flow direction in heterogeneous materials is governed by the current state variables and loading increment direction. Finally, we show that the plastic response is also a function of the stress path taken prior to loading. As such, nonclassical characteristics are observed for the plasticity of microheterogeneous materials. These nonclassical characteristics for the simple REV considered in this work (the hollow sphere model) align with some of the results observed in experiments or only reported for microdiscrete materials; herein, we show they are also valid for a broader range of materials endowed with microstructure, i.e., microcontinuous materials, and, as such, the fundamental assumptions in the classical theory of plasticity require revision before being applicable to the plastic behavior of heterogeneous materials.

Changes in the structure of polysaccharides under different extraction methods

Changes in the structure of polysaccharides under different extraction methods

DFEN: Dual Feature Enhancement Network for Remote Sensing Image Caption

The remote sensing image caption can acquire ground objects and the semantic relationships between different ground objects. Existing remote sensing image caption algorithms do not acquire enough ground object information from remote-sensing images, resulting in inaccurate captions. As a result, this paper proposes a codec-based Dual Feature Enhancement Network (“DFEN”) to enhance ground object information from both image and text levels. We build the Image-Enhancement module at the image level using the multiscale characteristics of remote sensing images. Furthermore, more discriminative image context features are obtained through the Image-Enhancement module. The hierarchical attention mechanism aggregates multi-level features and supplements the ground object information ignored due to large-scale differences. At the text level, we use the image’s potential visual features to guide the Text-Enhance module, resulting in text guidance features that correctly focus on the information of the ground objects. Experiment results show that the DFEN model can enhance ground object information from images and text. Specifically, the BLEU-1 index increased by 8.6% in UCM-caption, 2.3% in Sydney-caption, and 5.1% in RSICD. The DFEN model has promoted the exploration of advanced semantics of remote sensing images and facilitated the development of remote sensing image caption.

Study on Multi-Scale Characteristics and Influencing Factors of Trade-Offs and Synergies between Ecosystem Services in Jiangxi Province

The trade-offs and synergies reveal the profit and loss relationship between ecosystem services, which is of great significance to the sustainable development of natural resources. The ecosystem services in Jiangxi Province, such as net primary productivity (NPP), soil conservation (SC) and water yield (WY) during 2000–2020, were estimated in this study. The correlation coefficient was adopted to analyze the trade-offs and synergies between the three ecosystem services by static space correlation and dynamic space correlation from such perspectives as Watershed, county and grid. Moreover, the influence of the three ecosystem services and the relations between them were explored from four aspects: landform, NDVI, accumulated temperature and precipitation. The results showed that the ecological environment quality in Jiangxi Province was improved and that the distribution of ecosystem services had significant regional characteristics. In the static analysis, ecosystem services at all scales were remarkably synergistic, and synergies weakened rapidly and even turned into trade-offs as the scale decreased. In the dynamic analysis, ecosystem services at all scales were mainly synergistic; the proportion of significant samples was much lower than that in the static analysis, the degree of trade-offs/synergies decreased with the decrease in scale, and the decrease was smaller than that in the static analysis. The major constraints for SC were landform and NDVI. The main constraint for WY was precipitation, and that for NPP was NDVI. Affected by various factors, NPP and SC were stably synergistic, NPP and WY were in a stable trade-off relationship, and the relationship between SC and WY was unstable. The trade-offs and synergies changed with factors and zoning.

Computational fluid dynamics study of hydrogen production by sorption enhanced steam ethanol reforming process in fluidized bed

As a renewable resource, hydrogen plays a pivotal role in the energy transition of the current world. A promising route for hydrogen production is the sorption enhanced steam ethanol reforming (SE-SER), which is characterized by the complex multi-scale characteristics. This paper aims to explore the hydrodynamic (e.g., gas and solid flux, solid dispersion and velocity) and thermodynamic (e.g., particle temperature and heat transfer coefficient) characteristics on the micro-level together with the effect of operating parameters on the SE-SER performance. Accordingly, the reactive multiphase particle-in-cell (MP-PIC) model has been developed under the Lagrangian manner for studying the SE-SER process in the bubbling fluidized bed. In this model, it is assumed that uniform temperature appears inside the particle, and particles with similar properties are packed into a parcel to reduce computational cost. This model has been well validated with experimental data. The results indicate that along the bed height, the solid temperature decreases while the heat transfer coefficient increases. H2 and CO2 have similar distribution in the bed, mainly accumulating in the upper part of the reactor. Enlarging the ratio of H2O to C2H6O in the gas mixture promotes the yields of H2 and CO2. The relationships between key particle variables are explored. It is found that increasing the slip velocity enlarges the particle heat transfer coefficient, and large particle Reynolds number mainly appears with small solid volume fraction. The explored results provide valuable insights into deeply understanding complex multiphase flow and optimizing actual operation of the SE-SER field.

Energy multi-scale method to analyze the scale effect of soil particles

As a common geological material, soil is a key aspect of construction engineering. Soil has typical multi-scale characteristics, but current multi-scale methods analyze these characteristics only in regard to geometric space. More exploration of the coupling influence mechanism of the basic properties of particles on the microstructure and macroscopic properties of soil is needed. This study analyzed the influence of geometric scale and mineral composition on the surface energy of particles at the microscopic level for development of the energy multi-scale method. Experiments were performed to determine the influence of mineral composition and particle size on the plasticity index (Ip) of the soil, and experimental results are discussed and interpreted quantitatively using the energy multi-scale method. The conclusions derived from this work are as follows: 1) the mineral composition and particle size of the soil can cause interface and surface effects; 2) the comprehensive ratio of micro-force to weight (CRFW) of the particles can be determined using the energy multi-scale method and quantitatively reflects the influence of particle size and mineral composition on the microscopic properties of the soil; and 3) the energy multi-scale method explains the mechanism of the plasticity index of soil and has allowed identification of a new division of soil plasticity. When the CRFW was used as the control index, the plasticity index of the three materials was practically the same, even if the mineral composition and particle sizes of the three materials were different.

Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction

The short-term forecast of photovoltaic (PV) power is crucial for the security and economics of power system operations. However, the fluctuation characteristics of the PV power, which are closely related to the meteorological factors, introduce inaccuracies in its forecast. Towards this end, the paper studies the effects of clustering analysis at long time scale and data reconstruction technique at short time scale on capturing PV power fluctuation characteristics. A short-term PV power forecasts method based on multi-scale fluctuation characteristics extraction (MFCE), which employs a path analysis to identify the relevance of meteorological factors with PV power at long time scale and a phase space reconstruction to analyze PV power fluctuation characteristics at short time scale, is proposed in this paper. The proposed MFCE methodology deploys a widely-used extreme gradient boosting (XGBoost) model to output the forecasting results. Both the effectiveness and accuracy of the proposed methodology are verified by using the real data under the conditions of sunny and cloudy days of four seasons compared to traditional methodologies.

Micro-/nano-scale interface structure and mechanical characteristics of the Al/Ti composite bar produced by a novel continuously-extruded technique

In this work, a novel extrusion die for embedding the continuous Ti wire into the aluminum alloy is developed, and the extruded composite bar is obtained. The multi-scale interface structure, elemental diffusion, and mechanical characteristics are investigated by different material characterization methods. The three-dimensional (3D) amorphous regions are produced on the well-bonded Al/Ti interface. The formation of a nanocrystalline Al layer is accompanied by the precipitation of Mg-/Si-enriched clusters. With an increase in the annealing time, the average grain size of TiAl3 increases, while the dislocation density of the TiAl3 layer decreases. A nanoscale TiAl3 monolayer is exhibited between the Al/TiAl3 and Kirkendall interfaces, whereas two types of nanoscale TiAl3 grains are detected on the Ti/TiAl3 interface. The Si-enriched clusters are aggregated at the Ti/TiAl3 interface, generating a nanoscale layer. Local inhibition behaviors of large-sized Mg-enriched clusters on the growth of TiAl3 are observed at the Al/TiAl3 interface after annealing for 48 h. Not only are massive misfit dislocations and distorted lattices detected on the 3D interface, but also locally ordered transition zones are noticed. The extruded composite bar exhibits a superior strength-ductility balance, which provides a new strategy for the development of aluminum profiles.

MPARN: multi-scale path attention residual network for fault diagnosis of rotating machines

Abstract Multi-scale convolutional neural network structures consisting of parallel convolution paths with different kernel sizes have been developed to extract features from multiple temporal scales and applied for fault diagnosis of rotating machines. However, when the extracted features are used to the same extent regardless of the temporal scale inside the network, good diagnostic performance may not be guaranteed due to the influence of the features of certain temporal scale less related to faults. Considering this issue, this paper presents a novel architecture called a multi-scale path attention residual network to further enhance the feature representational ability of a multi-scale structure. Multi-scale path attention residual network adopts a path attention module after a multi-scale dilated convolution layer, assigning different weights to features from different convolution paths. In addition, the network is composed of a stacked multi-scale attention residual block structure to continuously extract meaningful multi-scale characteristics and relationships between scales. The effectiveness of the proposed method is verified by examining its application to a helical gearbox vibration dataset and a permanent magnet synchronous motor current dataset. The results show that the proposed multi-scale path attention residual network can improve the feature learning ability of the multi-scale structure and achieve better fault diagnosis performance.