Theoretical investigation on axial cyclic performance of monopile in sands using interface constitutive models

Cyclic loads generated by environmental factors,such as winds,waves,and trains,will likely lead to performance degradation in pile foundations,resulting in issues like permanent displacement accumulation and bearing capacity attenuation.This paper presents a semi-analytical solution for predicting the axial cyclic behavior of piles in sands.The solution relies on two enhanced nonlinear load-transfer models considering stress-strain hysteresis and cyclic degradation in the pile-soil interaction.Model parameters are calibrated through cyclic shear tests of the sand-steel interface and laboratory geotechnical testing of sands.A novel aspect involves the meticulous formulation of the shaft loadtransfer function using an interface constitutive model,which inherently inherits the interface model’s advantages,such as capturing hysteresis,hardening,degradation,and particle breakage.The semi-analytical solution is computed numerically using the matrix displacement method,and the calculated values are validated through model tests performed on non-displacement and displacement piles in sands.The results demonstrate that the predicted values show excellent agreement with the measured values for both the static and cyclic responses of piles in sands.The displacement pile response,including factors such as bearing capacity,mobilized shaft resistance,and convergence rate of permanent settlement,exhibit improvements compared to non-displacement piles attributed to the soil squeezing effect.This methodology presents an innovative analytical framework,allowing for integrating cyclic interface models into the theoretical investigation of pile responses.

Editorial

Founded only six years ago, the National Center for Nanoscience and Technology （NCNST） has developed rapidly withimportant achievements. We invited experts of NCNST in this field to introduce the research developments by NCNST and

Attentional Ptycho-Tomography(APT)for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Noninvasive X-ray imaging of nanoscale three-dimensional objects,such as integrated circuits(ICs),generally requires two types of scanning:ptychographic,which is translational and returns estimates of the complex electromagnetic field through the IC;combined with a tomographic scan,which collects these complex field projections from multiple angles.Here,we present Attentional Ptycho-Tomography(APT),an approach to drastically reduce the amount of angular scanning,and thus the total acquisition time.APT is machine learning-based,utilizing axial self-Attention for Ptycho-Tomographic reconstruction.APT is trained to obtain accurate reconstructions of the ICs,despite the incompleteness of the measurements.The training process includes regularizing priors in the form of typical patterns found in IC interiors,and the physics of X-ray propagation through the IC.We show that APT with×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique(SART)with the original set of angles.When using the same set of reduced angles,then APT also outperforms Filtered Back Projection(FBP),Simultaneous Iterative Reconstruction Technique(SIRT)and SART.The time needed to compute the reconstruction is also reduced,because the trained neural network is a forward operation,unlike the iterative nature of these alternatives.Our experiments show that,without loss in quality,for a 4.48×93.2×3.92µm^(3) IC(≃6×10^(8) voxels),APT reduces the total data acquisition and computation time from 67.96 h to 38 min.We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging,including materials science and biological imaging.

Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning(RAPID)

X-ray ptychographic tomography is a nondestructive method for three dimensional(3D)imaging with nanometer-sized resolvable features.The size of the volume that can be imaged is almost arbitrary,limited only by the penetration depth and the available scanning time.Here we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned priors.The proposed 3D reconstruction method“RAPID”relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground truth.It is then trained to reproduce equal fidelity from much fewer angles.After training,it performs with similar fidelity on the hitherto unexamined portions of the object,previously not shown during training,with a limited set of acquisitions.In our experimental demonstration,the nominal number of angles was 349 and the reduced number of angles was 21,resulting in a×140 aggregate speedup over a volume of 4.48×93.18×3.92μm^(3) and with(14 nm)^(3) feature size,i.e.-10^(8) voxels.RAPID’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic way.We found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.

Compartmentalized immune responses and the local microbiota determine mucosal and systemic immunity against SARS-CoV-2

The clinical manifestations of SARS-CoV-2 infection,which is the cause of the coronavirus disease 2019(COVID-19)pandemic,are highly variable and range from asymptomatic carriage or mild symptoms to severe disease involving different organ systems.However,the specific factors influencing individual clinical outcomes remain unclear.Thus,to characterize the versatile interplay of mucosal and systemic immune responses with the local microbiome and the viral load,as well as its impact on the course of the disease,Smith et al.performed integrated analyses of nasopharyngeal swabs and plasma samples from COVID-19 patients with varying degrees of illness severity.They observed that spike-specific neutralizing antibodies were heterogeneous between paired plasma samples and nasopharyngeal swabs from individual SARS-CoV-2 patients,suggesting a tissuedependent regulation of humoral immune responses.

Intracellular in situ labeling of Ti02 nanoparticles for fluorescence microscopy detection

Titanium dioxide （TiO2） nanoparticles are produced for many different purposes, including development of therapeutic and diagnostic nanoparticles for cancer detection and treatment, drug delivery, induction of DNA double-strand breaks, and imaging of specific cells and subcellular structures. Currently, the use of optical microscopy, an imaging technique most accessible to biology and medical patholog36 to detect TiO2 nanoparticles in cells and tissues ex vivo is limited with low detection limits, while more sensitive imaging methods （transmission electron microscopy, X-ray fluorescence microscop~ etc.） have low throughput and technical and operational complications. Herein, we describe two in situ post- treatment labeling approaches to stain TiO2 nanoparticles taken up by the cells. The first approach utilizes fluorescent biotin and fluorescent streptavidin to label the nanoparticles before and after cellular uptake; the second approach is based on the copper-catalyzed azide-alkyne cycloaddition, the so-called Click chemistry, for labeling and detection of azide-conjugated TiO2 nanoparticles with alkyne- conjugated fluorescent dyes such as Alexa Fluor 488. To confirm that optical fluorescence signals of these nanoparticles match the distribution of the Ti element, we used synchrotron X-ray fluorescence microscopy （XFM） at the Advanced Photon Source at Argonne National Laboratory. Titanium-specific XFM showed excellent overlap with the location of optical fluorescence detected by confocal microscopy. Therefore, future experiments with TiO2 nanoparticles may safely rely on confocal microscopy after in situ nanoparticle labeling using approaches described here.