Phosphorus slags, abundant industrial by-products, hold significant potential for use as supplementary cementitious materials (SCM). To explore its application, phosphorus slags with varying contents of P₂O₅ were synthesized, and their network structure, dissolution behaviour, and hydration products in cementitious systems were investigated. The results showed that the pozzolanic reactivity initially decreased and then increased with increasing P₂O₅ content from 3.11 to 8.32 wt%. This behaviour was attributed to the competition between silicate network repolymerization and distortion of aluminium polyhedra. Furthermore, the incorporation of phosphorus increased the fractions of Al_V and Al_VI in the glass network, which promoted the incongruent dissolution of Al over Si. Thermodynamic simulation predicted the formation of hydroxyapatite, as the main phosphorus-containing hydration product, which was further confirmed by XRD and NMR results, although with low crystallinity. These findings suggested new perspectives on adopting phosphorus slag as a SCM in cement and concrete production.

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The microstructure of cement paste determines the overall performance of concrete and therefore obtaining the microstructure is an essential step in concrete studies. Traditional methods to obtain the microstructure, such as scanning electron microscopy (SEM) and X-ray computed tomography (XCT), are time-consuming and expensive. Herein we propose using Denoising Diffusion Probabilistic Models (DDPM) to synthesize realistic microstructures of cement paste. A DDPM with a U-Net architecture is employed to generate high-fidelity microstructure images that closely resemble those derived from SEM. The synthesized images are subjected to comprehensive image analysis, phase segmentation, and micromechanical analysis to validate their accuracy. Findings demonstrate that DDPM-generated microstructures not only visually match the original microstructures but also exhibit similar greyscale statistics, phase assemblage, phase connectivity, and micromechanical properties. This approach offers a cost-effective and efficient alternative for generating microstructure data, facilitating advanced multiscale computational studies of cement paste properties.

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The properties of slag-rich cement paste are fundamentally associated with slag chemistry. In the present research, 10 slags covering the common chemistry range, including eight synthetic slags of CaO-SiO2-Al2O3-MgO system and two commercial slags, were adopted to evaluate the influence of slag composition on the early (7 days) and later (3 months) age properties of blended paste. Mixture containing Al2O3-rich slag performed better at 7 days as it favored the formation of ettringite and/or monosulfate. The MgO-rich slag cement paste exhibited good properties at both early and later ages, and it effectively promoted the precipitation of Mg-Al hydrotalcite-like phase. It was also noted that the Mg/Al atomic ratio of hydrotalcite-like phase was positively related to the Mg/Al atomic ratio of slag itself. Conversely, with the increasing MgO content in slag, the Al/Si atomic ratio of C-S(A)-H gel phase decreased. High Al2O3 and/or MgO contents can compensate the negative effect of reduced CaO/SiO2 ratio at early age while not at later age. Overall, attention should be paid to aluminum- and sulfur-rich slags. These two elements in slag promoted the formation of ettringite and/or monosulfate at an early age; however, this positive effect disappeared at later ages.

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This paper develops an efficient solution for super-resolution two-dimensional (2D) harmonic retrieval from multiple measurement vectors (MMV). Given the sample covariance matrix constructed from the MMV, a gridless compressed sensing approach is proposed based on the atomic norm minimization (ANM). In the approach, our key step is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (D-ANM) framework can be applied for super-resolution 2D frequency estimation. Moreover, the resulting RR-enabled D-ANM technique, termed RR-D-ANM, further allows an efficient relaxation under certain conditions, which leads to low computational complexity of the same order as the 1D case. Simulation results verify the advantages of our solutions over benchmark methods, in terms of higher computational efficiency and detectability for 2D harmonic retrieval.

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Multi-fidelity optimization methods promise a high-fidelity optimum at a cost only slightly greater than a low-fidelity optimization. This promise is seldom achieved in practice, due to the requirement that low- and high-fidelity models correlate well. In this article, we propose an efficient bi-fidelity shape optimization method for turbulent fluid-flow applications with Large-Eddy Simulation (LES) and Reynolds-averaged Navier-Stokes (RANS) as the high- and low-fidelity models within a hierarchical-Kriging surrogate modelling framework. Since the LES–RANS correlation is often poor, we use the full LES flow-field at a single point in the design space to derive a custom-tailored RANS closure model that reproduces the LES at that point. This is achieved with machine-learning techniques, specifically sparse regression to obtain high corrections of the turbulence anisotropy tensor and the production of turbulence kinetic energy as functions of the RANS mean-flow. The LES–RANS correlation is dramatically improved throughout the design-space. We demonstrate the effectivity and efficiency of our method in a proof-of-concept shape optimization of the well-known periodic-hill case. Standard RANS models perform poorly in this case, whereas our method converges to the LES-optimum with only two LES samples.

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GRACE observations revealed that rapid mass loss in the West Antarctic Ice Sheet (WAIS) abruptly paused in 2015, followed by a much lower rate of mass loss ((Formula presented.) Gt yr⁻¹) until the decommissioning of GRACE in 2017. The critical 1-year GRACE intermission data gap raises the question of whether the reduced mass loss rate persists. The Swarm gravimetry data, which have a lower resolution, show good agreement with GRACE/GRACE-FO observations during the overlapping period, i.e., high correlation (0.78) and consistent trend estimates. Swarm data efficiently bridge the GRACE/GRACE-FO data gap and reveal that WAIS has returned to the rapid mass loss state ((Formula presented.) Gt yr⁻¹) that prevailed prior to 2015 during the GRACE intermission data gap. The changes in precipitation patterns, driven by the climate cycles, further explain and confirm the dramatic shifts in the WAIS mass loss regime implied by the Swarm observations.

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This paper aims at developing low-complexity solutions for super-resolution two-dimensional (2D) harmonic retrieval via covariance reconstruction. Given the collected sample covariance, a novel gridless compressed sensing approach is designed based on the atomic norm minimization (ANM) technique. The key is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (DANM) framework can be applied for super-resolution 2D frequency estimation, at low computational complexity on the same order of the 1D case. An analysis of the complexity reduction of the proposed RR-D-ANM compared with benchmark methods is provided as well, which is verified by our simulation results @en

This paper develops efficient super-resolution spatial channel covariance estimation techniques for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under the hybrid precoding constraint. Two structure-based optimization techniques including low-rank structured covariance reconstruction and dynamic atomic norm minimization are proposed to accurately estimate the channel covariance matrix. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. The extension of this work to the higher-dimensional cases is also discussed. Simulation results verify the effectiveness of the proposed methods in hybrid mmWave massive MIMO systems.

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This paper aims at accurate channel estimation for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under practical limitations, including an arbitrary array geometry and a hybrid hardware structure. Taking on an angle-based approach, this work adopts a generalized array manifold separation approach via the Jacobi-Anger approximation, which transforms a non-ideal, non-uniform array manifold into a virtual array domain with a desired uniform geometric structure to facilitate super-resolution angle estimation and channel acquisition. Accordingly, structure-based optimization techniques are developed to estimate the channel parameters within a short sensing time. In particular, the difference in time-variation of path angles and path gains is capitalized to design a two-step scheme that can quickly sense fading channels. Theoretical results are provided on the fundamental limits of the proposed technique in terms of sample efficiency. Simulations testify the effectiveness of the proposed approaches.

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