Cross-scale characteristics of backfill material using NMR and fractal theory

This paper analyzed the pore structure, quantified the pore fractal dimension, calculated the grading index(GI) of mixed aggregate, and studied the relationship among GI, pore structure, and strength to describe the cross-scale characteristics of backfill, which is made from stone powder and cemented tailing. A series of experiments were conducted on stone powder cement tailings backfill(SPCTB). The GI formulas for mixed aggregates, containing stone powder and tailings, were derived based on the Füller theory. The nuclear magnetic resonance(NMR) fractal dimensions of backfills were derived using fractal geometry principles. Compared to the mesopore and macropore fractal dimensions, the correlation between micropore fractal dimension and macro-properties in terms of NMR porosity, pore structure complexity, uniaxial compression strength(UCS), and GI is the most significant. Macropore fractal dimension is generally correlated with UCS and GI and the other properties such as the shape of mixed aggregates also have an impact on fractal dimension. However, mesopore fractal dimension has no obvious relationship with macro-properties. Finally, the relationship between GI and UCS was studied, which contributed to improving backfill’s strength and optimizing gradation.

Cross-scale Correlation of Macro-micro Evaluation Indexes for Asphalt Binder

In order to further study the reliability of macro evaluation indexes,molecular dynamics (MD) was applied to the evaluation of asphalt binder.Micro evaluation indexes (potential energy,surface free energy,solubility parameter and diffusion coefficient) of asphalt binder in different service phases (virgin,modified,aged and rejuvenated) were simulated.Combined with the variation characteristics of asphalt binder macro evaluation indexes (permeability,ductility,viscosity and softening point) in different service phases,the cross-scale correlation of macro-micro evaluation indexes was explored.The results show that the macro and micro evaluation indexes of asphalt binder have different characteristics in different service phases.The essence of the variation in the properties of asphalt binders is the difference in micro composition.In addition,there is a certain correlation between macro and micro evaluation indexes,which can be described by the gray relation theory.The cross-scale correlation of macro-micro evaluation indexes can provide a certain theoretical basis for the development of asphalt binder.

Ultra-high-temperature application of MXene: Stabilization of 2D Ti_(3)C_(2)T_(x) for cross-scale strengthening and toughening of 3D TiC

Transition metal carbide/nitride cores within MXenes make them considerably useful for ultra-high-temperature reinforcement.However,extensive research on Ti_(3)C_(2)T_(x) MXene has revealed its tendency to undergo a phase transition to TiCy at temperatures above 800℃due to high activity of a superficial Ti atomic layer.Herein,spark plasma sintering of Ti_(3)C_(2)T_(x) and TiC is performed to prevent the Ti_(3)C_(2)T_(x) phase transition at temperatures up to 1900℃through the fabrication of composites at a pressure of 50 MPa.Using a focused ion beam scanning electron microscope to separate layered substances in the composites and examining selected area diffraction spots in a transmission electron microscope enabled identification of non-phase-transitioned MXene.First-principles calculations based on density functional theory indicated the formation of strong chemical bonding interfaces between Ti_(3)C_(2)T_(x) and TiC,which imposed a stability constraint on the Ti atomic layer at the Ti_(3)C_(2)T_(x) surface.Mechanical performance tests,such as three-point bending and fracture toughness analysis,demonstrated that the addition of Ti_(3)C_(2)T_(x) can effectively improve the cross-scale strengthening and toughening of the TiC matrix,providing a new path for designing and developing two-dimensional(2D)carbides cross-scale-enhanced three-dimensional(3D)carbides with the same elements relying on a wide variety of MXenes.

Cross-scale mechanical softening of Marcellus shale induced by CO_(2)-water-rock interactions using nanoindentation and accurate grain-based modeling

Mechanical softening behaviors of shale in CO_(2)-water–rock interaction are critical for shale gas exploitation and CO_(2)sequestration.This work investigated the cross-scale mechanical softening of shale triggered by CO_(2)-water–rock interaction.Initially,the mechanical softening of shale following 30 d of exposure to CO_(2)and water was assessed at the rock-forming mineral scale using nanoindentation.The mechanical alterations of rock-forming minerals,including quartz,muscovite,chlorite,and kaolinite,were analyzed and compared.Subsequently,an accurate grain-based modeling(AGBM)was proposed to upscale the nanoindentation results.Numerical models were generated based on the real microstructure of shale derived from TESCAN integrated minerals analyzer(TIMA)digital images.Mechanical parameters of shale minerals determined by nanoindentation served as input material properties for AGBMs.Finally,numerical simulations of uniaxial compression tests were conducted to investigate the impact of mineral softening on the macroscopic Young’s modulus and uniaxial compressive strength(UCS)of shale.The results present direct evidence of shale mineral softening during CO_(2)-water–rock interaction and explore its influence on the upscale mechanical properties of shale.This paper offers a microscopic perspective for comprehending CO_(2)-water-shale interactions and contributes to the development of a cross-scale mechanical model for shale.

A 3-DOF piezoelectric robot with continuous walking gait aiming at cross-scale smooth motion

Piezoelectric robots play important roles in the field of micromanipulation, but it is difficult for them to generate steady precision motion at any moment. In order to eliminate the changing inertial force and improve the motion smoothness, this work proposes a piezoelectric robot with continuous walking gait inspired by ants. The idea is verified with theoretical models and numerical simulation, and the performances are evaluated with experiments. The robot is proven to have the ability to generate 3-DOF(dgeree of freedom) continuous smooth motions with constant speeds. The maximum and minimum smooth velocities have a difference of six orders of magnitude, realizing cross-scale velocity control. Besides, the motion resolution reaches several nanometers with the unlimited workspace, so the cross-scale displacement control can be also obtained. Furthermore, with great robustness against varying loads, the stable actuation capability of the robot is more than 22 times of the self-weight. To sum up,the proposed robot generates cross-scale smooth motion in both aspects of displacement and velocity, so it has good prospects in the applications requiring steady precision motion. The design philosophy and research methods in this work can be valuable references for further advances of micromanipulation robots.

Multi-scale observation and cross-scale mechanistic modeling on terrestrial ecosystem carbon cycle

To predict global climate change and to implement the Kyoto Protocol for stabilizing atmospheric greenhouse gases concentrations require quantifying spatio-temporal variations in the terrestrial carbon sink accurately. During the past decade multi-scale ecological experiment and observation networks have been established using various new technologies (e.g. controlled environmental facilities, eddy covariance techniques and quantitative remote sensing), and have obtained a large amount of data about terrestrial ecosystem carbon cycle. However, uncertainties in the magnitude and spatio-temporal variations of the terrestrial carbon sink and in understanding the underlying mechanisms have not been reduced significantly. One of the major reasons is that the observations and experiments were conducted at individual scales independently, but it is the interactions of factors and processes at different scales that determine the dynamics of the terrestrial carbon sink. Since experiments and observations are always conducted at specific scales, to understand cross-scale interactions requires mechanistic analysis that is best to be achieved by mechanistic modeling. However, mechanistic ecosystem models are mainly based on data from single-scale experiments and observations and hence have no capacity to simulate mechanistic cross-scale interconnection and interactions of ecosystem processes. New-generation mechanistic ecosystem models based on new ecological theoretical framework are needed to quantify the mechanisms from micro-level fast eco-physiological responses to macro-level slow acclimation in the pattern and structure in disturbed ecosystems. Multi-scale data-model fusion is a recently emerging approach to assimilate multi-scale observational data into mechanistic, dynamic modeling, in which the structure and parameters of mechanistic models for simulating cross-scale interactions are optimized using multi-scale observational data. The models are validated and evaluated at different spatial and temporal scales and real-time observational data are assimilated continuously into dynamic modeling for predicting and forecasting ecosystem changes realistically. in summary, a breakthrough in terrestrial carbon sink research requires using approaches of multi-scale observations and cross-scale modeling to understand and quantify interconnections and interactions among ecosystem processes at different scales and their controls over ecosystem carbon cycle.

Texture evolution prediction of 2219 aluminum alloy sheet under hydro-bulging using cross-scale numerical modeling

A simultaneous prediction of macroscopic deformation and microstructure evolution is critical for un-derstanding the deformation mechanism of components.In this work,the hydro-bulging process of 2219 aluminum alloy sheet was investigated using cross-scale numerical modeling,in which the macroscopic finite element method(FEM)and crystal plasticity finite element method(CPFEM)were combined.The calculated texture evolution exhibits good agreement with the experimental results,and the stress er-ror between the two scales is generally small.The effects of different strain states on texture evolution and slip mode are further discussed.As the strain ratioηincreases,the volume fractions of the initial Rotated Copper texture component andγ-Fiber texture component decrease significantly,which tend to be stabilized at P texture component.The initial Rotated Cube texture component is inclined to rotate towards the Cube texture component,while the volume fraction of this orientation is relatively stable.The lower strain ratio can considerably enhance the activity of more equivalent slip systems,promoting a more uniform strain distribution over grains.The difficulty of grain deformation changes as the lat-tice rotates.The grain with easy-to-deform orientation can gradually rotate to a stable orientation during plastic deformation,which has a lower Schmid factor.

The modelling and application of cross-scale human behavior in realizing the shop-floor digital twin[version 1;peer review:1 approved with reservations,1 not approved]

The digital twin shop-floor has received much attention from the manufacturing industry as it is an important way to upgrade the shop-floor digitally and intelligently.As a key part of the shop-floor,humans'high autonomy and uncertainty leads to the difficulty in digital twin modeling of human behavior.Therefore,the modeling system for cross-scale human behavior in digital twin shop-floors was developed,powered by the data fusion of macro-behavior and micro-behavior virtual models.Shop-floor human macro-behavior mainly refers to the role of the human and their real-time position.Shop-floor micro-behavior mainly refers to real-time human limb posture and production behavior at their workstation.In this study,we reviewed and summarized a set of theoretical systems for cross-scale human behavior modeling in digital twin shop-floors.Based on this theoretical system,we then reviewed modeling theory and technology from macro-behavior and micro-behavior aspects to analyze the research status of shop-floor human behavior modeling.Lastly,we discuss and offer opinion on the application of cross-scale human behavior modeling in digital twin shop-floors.Cross-scale human behavior modeling is the key for realizing closed-loop interactive drive of human behavior in digital twin shop-floors.

UMTransNet:结合U-Net和多尺度感知Transformer的图像拼接定位方法

当前基于深度学习的图像拼接定位方法大多只关注深层次特征,且感受野有限,忽略了浅层次特征,影响图像拼接定位的准确性。针对上述问题,文中提出一种结合改进U-Net和多尺度多视角Transformer的图像拼接定位网络UMTransNet。改进U-Net模型的编码器,将编码器中的最大池化层替换成卷积层,防止浅层次特征的流失;将多尺度多视角Transformer嵌入到U-Net的跳跃连接中,Transformer的输出特征与U-Net的上采样特征进行有效融合,实现深层次特征与浅层次特征的平衡,从而提高图像拼接定位的准确性。通过可视化检测结果图显示,所提方法在定位拼接篡改区域方面表现得更加出色。

A new cross-scaling method to deal with the porous flow problem

The finite volume method （FVM） and the lattice Boltzmann method （LBM） are coupled with each other to construct a new cross-scaling method to deal with the porous flow problem. To check the effectiveness of our developed cross-scaling LBM-FVM, the above mentioned problem is also solved by the well known LBM-LBM. Based on the data checking of the published data and the results of LBM-FVM and LBM-LBM, good agreement is observed.

Mechanisms for Cross-Scaling, Flexibility and Social Learning in Building Resilience to Sea Level Rise: Case Study of Hampton Roads, Virginia

This case study examines how the Hampton Roads Sea Level Rise Preparedness and Resilience Intergovernmental Planning Pilot Project, a whole-of-government and whole-of-community approach to planning for and adapting to sea level rise, addressed coastal resilience in a southeastern Virginia watershed that spans multiple jurisdictions. Meeting the challenge of sea level rise requires that actors across multiple sectors—citizens, community organizations, industry and government—understand the risks and work together to make critical decisions regarding adaptation strategies and actions. The case study area includes Little Creek Amphibious Base, which is bordered by the cities of Norfolk and Virginia Beach. Adaptation responses to sea level rise by the military base and the local governments will impact each other and the residents of the area, but no cooperative agreements are in place for a joint or collaborative response. This case study examines public and private infrastructure at risk, the infrastructure interdependencies, and mechanisms for providing collaborative solutions. Engagement of area residents and other stakeholders is also integral to the process of adaptation, which includes educating about sea level rise risk and provides a mechanism for social learning that enables stakeholders to participate in critical adaptation decisions. The case study demonstrates a method to improve resiliency in the case study area and inform a regional, multi-sectoral response to sea level rise adaptation strategies.

基于跨尺度相似先验的遥感图像时空融合算法

遥感卫星图像在空间分辨率和时间分辨率之间权衡导致图像序列的时空矛盾.时空图像融合提供了一个生成高空间分辨率和高时间分辨率图像的解决方案,以满足各种地球观测应用.基于稀疏表示的时空融合算法通过联合训练字典和稀疏编码表示建立高低空间分辨率图像之间的关系,为物候变化、类型变化等各种情况提供了统一的融合框架.然而,多源遥感图像来自于不同的传感器,高低空间分辨率图像之间关系模型暗含有传感器映射关系,导致模型设备依赖.针对该问题,本文提出将多源遥感图像时空融合过程分解为传感器偏差校正和时空融合两个子问题,即设备依赖部分和设备无关部分.传感器偏差校正部分可以作为时空融合预处理模块,提高融合精度,并且使得后续的融合模型更加具有普适性.当高低空间分辨率图像空间分辨率差异较大时,“高低空间分辨率图像稀疏系数一致”的假设带来的融合误差非常突出.针对该问题,本文提出基于跨尺度相似先验的遥感图像时空融合算法,利用跨尺度相似块构建稀疏结构先验的正则项,优化稀疏表示的目标函数,并构建中间尺度图像,降低跨尺度相似块的二义性.本文分别使用3组典型场景的实验数据集与其他算法进行对比,实验结果表明,在BOREAS数据集上,与次优的指标相比,本文算法的结构相似度(Structural SIMilarity,SSIM)提高了4.2%,光谱角(Spectral Angle Mapper,SAM)提高了4.6%;在CIA数据集上,与次优的指标相比,本文算法的SSIM提高了2.7%,SAM提高了12.8%;在LGC数据集上,与次优的指标相比,本文算法的SSIM提高了7.1%,SAM提高了16.3%;证明本文算法在空间和光谱特性上表现出优秀的特性.

微纳尺度结构动力学专刊序

围绕微纳尺度结构跨尺度动力学建模、动力学分析、动力学控制、动力学设计与测量方法等研究主题,本专刊介绍了微纳尺度结构动力学与控制领域的一些最新研究成果.

改进3D-Octave卷积的高光谱图像分类方法

高光谱图像数据具有维度高、数据稀疏、空间光谱信息丰富等特点,针对空谱联合分类模型中高光谱图像卷积操作处理大片相同类别像素区域时会存在计算的空间冗余,3D卷积对深层空间纹理特征提取不充分,串行注意力机制结构不能充分考虑空谱相关性的问题,该文提出了改进的3D-Octave卷积高光谱图像分类模型。首先改进的3D-Octave卷积模块将输入的高光谱图像数据划分为高频特征图和低频特征图,减少空间信息冗余,提取多尺度的空间光谱特征,结合跨层融合策略,加强对浅层空间纹理特征和光谱特征的提取;随后利用2D卷积提取深层空间纹理特征并进行光谱特征融合;最后使用三维注意力机制跨纬度交互实现对有效特征的关注和激活,增强网络模型的性能和鲁棒性。结果表明,由于充分提取有效空谱联合特征,在印第安松树林(Indian Pines,IP)数据集的训练集比例为10%的条件下,OA,Kappa和AA分别为99.32%,99.13%和99.15%;在帕维亚大学(Pavia University,PU)数据集的训练集比例为3%的条件下,OA,Kappa和AA分别为99.61%,99.44%和99.08%。与5个主流分类模型进行对比,获得了更高的分类精度。

生物热-力-电生理耦合学

由于温度、机械力、生化等多种影响因子对机体的生物学行为产生重要影响,生物体在多物理场下保持动态平衡是其正常活动的关键条件.研究生物热-力-电生理耦合行为,旨在交叉融合多个学科,建立生物传热学、生物力学、神经生理学等数理模型,系统开展跨尺度热-力-电生理耦合行为研究,并将成果应用于生物医学领域,对理解生物体的正常功能以及重大疾病的病理机制和有效诊治具有重要意义.其核心思想在于发现关键科学问题、识别交叉学科,并集成学科优势,推动理论创新和技术创新,为生命医学领域带来新突破.该文以剑桥大学和DNA双螺旋结构的发现为背景,以大脑、皮肤、牙齿等为例,介绍生物热-力-电生理耦合学这一新兴交叉学科的起源和内涵.

固体发动机药柱低温点火开裂失效的跨尺度分析

为了准确预测推进剂装药在低温点火过程中是否发生开裂,提出了一种全局⁃局部单向收缩耦合的跨尺度分析方法。针对高能硝酸酯增塑聚醚(NEPE)推进剂,开展低温中应变率单轴拉伸试验,获取了推进剂的典型失效模式。结果表明,基于发展的推进剂非线性粘弹性本构模型,实现了固体发动机药柱低温点火的宏观结构分析,获取了推进剂药柱结构危险点的位置;同时建立了考虑颗粒与基体界面脱湿和颗粒断裂的细观颗粒填充模型,进一步将宏观结构分析结果作用于相应的细观代表性体积单元(RVE)上。最后,建立推进剂细观失效准则,表明在低温点火条件下药柱结构完整性满足要求。收缩跨尺度分析方法可作为预测药柱在低温点火过程中开裂行为的有效手段。

基于多尺度时空特征和篡改概率改善换脸检测的跨库性能

目前大多DeepFake换脸检测算法过于依赖局部特征,尽管库内检测性能尚佳,但容易出现过拟合,导致跨库检测性能不理想,即泛化性能不够好。有鉴于此,文中提出一种基于多尺度时空特征和篡改概率的换脸视频检测算法,目的是利用假脸视频中广泛存在的帧间时域不连续性缺陷来解决现有检测算法在跨库、跨伪造方式和视频压缩时性能明显下降的问题,改善泛化检测能力。该算法包括3个模块:为检测假脸视频在时域上留下的不连续痕迹,设计了一个多尺度时空特征提取模块;为自适应计算多尺度时空特征之间的时空域关联性,设计了一个三维双注意力机制模块;为预测随机选取的像素点的篡改概率和构造监督掩膜,设计了一个辅助监督模块。将所提出的算法在FF++、DFD、DFDC、CDF等公开大型标准数据库中进行实验,并与基线算法和近期发布的同类算法进行对比。结果显示:文中算法在保持库内平均检测性能优良的同时,跨库检测和抗视频压缩时的综合性能最好,跨伪造方法检测时的综合性能中等偏上。实验结果验证了文中算法的有效性。

基于多尺度对比度增强和跨维度交互注意力机制的红外与可见光图像融合

针对目前红外与可见光图像融合存在特征提取不足、融合图像目标区域不显著、细节信息缺失等问题,提出了一种多尺度对比度增强和跨维度交互注意力机制的红外与可见光图像融合方法。首先,设计了多尺度对比度增强模块,以增强目标区域强度信息利于互补信息的融合;其次,采用密集连接块进行特征提取,减少信息损失最大限度利用信息;接着,设计了一种跨维度交互注意力机制,有助于捕捉关键信息,从而提升网络性能;最后,设计了从融合图像到源图像的分解网络使融合图像包含更多的场景细节和更丰富的纹理细节。在TNO数据集上对提出的融合框架进行了评估实验,实验结果表明本文方法所得融合图像目标区域显著,细节纹理丰富,具有更优的融合性能和更强的泛化能力,主观性能和客观评价优于其他对比方法。

基于改进YOLOv5-ResNet的海上舰船SAR图像快速检测

在恶劣天气和海浪等自然因素的影响下,基于可见光数据进行舰船目标监测等手段往往难以有效开展,需要借助主动式微波成像卫星合成孔径雷达(SAR)进行图像解译。为了解决深度学习在处理数据集较小图像上无法准确提取特征及数据相似度较高的问题,基于YOLOv5-ResNet提出了一种跨尺度融合机制,重新定义损失函数。研究表明,识别SAR舰船目标的准确率有一定的提升:识别单目标舰船检测最高准确度达到93%,同比YOLOv5提升4%,比YOLOv5-ResNet50提升20%;在近岸舰船目标检测上,有效降低了由于数据集质量不佳、模型训练方法不当等造成误差率的非必要上升。

体电子显微学前沿

电子显微成像技术的快速发展使得对完整细胞、组织乃至整个机体进行高分辨三维结构解析研究成为可能,这些可进行大尺度生物样品三维结构研究的电子显微成像技术统称为体电子显微学技术(volume electron microscopy,vEM)。近年来,v EM在研究尺度、分辨率、吞吐量和易用性等方面发展迅速,在整个生命科学领域的应用呈爆炸式增长,该技术因此被《自然》(Nature)评为2023年最值得关注的七项前沿技术之一。然而,vEM相关技术的发展和应用在国内起步较晚,亟待进一步推广。本综述涵盖了vEM的发展历程、技术分类、样品制备、数据收集、图像处理等全方位的内容,便于生命科学、医学等领域研究人员去了解、学习、应用和进一步发展该技术。