Feasibility of Using High-Alkali Natural Pozzolans and Reclaimed Fly Ash as Alternative Supplementary Cementitious Materials in Concrete

Due to the shortage of supplementary cementitious materials (SCMs) in the concrete industry, it is necessary to seek a new alternative. This study evaluates the feasibility of high-alkali SCMs, which were not recommended previously due to potentially exacerbating alkali-silica reaction (ASR) in concrete. The study addresses theSCMs’ ASR mitigation performance, evaluated by ASTM C1567, C1293, and AASHTO T380. Other concrete durability tests, including ASTM C596 drying shrinkage test, ASTM C1012 sulfate resistance test, and ASTM C1202 rapid chloride ions penetration test, were also conducted in this study. The ASTM C311 strength activity index test, thermal gravimetric analysis (TGA), and ASTM C1897 R3 test assessed SCMs’ pozzolanic reactivity. The pore solution analysis of cement paste with SCMs was conducted to determine the correlation between SCMs’ total and available alkali. Based on the results of this study, high-alkali SCMs have the strong potential to be alternative SCMs to the concrete industry

Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions

High-dimensional Partial Differential Equations (PDEs) are a popular mathematical modelling tool, with applications ranging from finance to computational chemistry. However, standard numerical techniques for solving these PDEs are typically affected by the curse of dimensionality. In this work, we tackle this challenge while focusing on stationary diffusion equations defined over a high-dimensional domain with periodic boundary conditions. Inspired by recent progress in sparse function approximation in high dimensions, we propose a new method called compressive Fourier collocation. Combining ideas from compressive sensing and spectral collocation, our method replaces the use of structured collocation grids with Monte Carlo sampling and employs sparse recovery techniques, such as orthogonal matching pursuit and $\ell^1$ minimization, to approximate the Fourier coefficients of the PDE solution. We conduct a rigorous theoretical analysis showing that the approximation error of the proposed method is comparable with the best $s$-term approximation (with respect to the Fourier basis) to the solution. Using the recently introduced framework of random sampling in bounded Riesz systems, our analysis shows that the compressive Fourier collocation method mitigates the curse of dimensionality with respect to the number of collocation points under sufficient conditions on the regularity of the diffusion coefficient. We also present numerical experiments that illustrate the accuracy and stability of the method for the approximation of sparse and compressible solutions.Comment: 33 pages, 9 figure

Lund University Website Evaluation: Focus on homepage and English research pages

The present universities have their own websites to achieve academic goals, and for this reason, the process of maintaining a high quality and effective website is vital for a university to strengthen its unpredictable creativity and entrepreneurialism. The aim of this study was to develop and validate university website usability, quality and performance, especially focuses on English homepage and research pages. In addition, we will develop a model of how to evaluate university website. Specific objectives were to identify major usability issues and provide a foundation for future development work. The web evaluation methods adopted during the study fall into three major classes: usability testing, user feedback and usage data. Results indicated that English information on website is incomplete, layout and design of the English homepage need to be improved, and the quality of the English research pages varied dramatically. Some web pages were of high standard, enabling quick access to current research and reinforcing the university’s brand as a high quality university conducting world’s leading research. Other web pages were a usability disaster, giving poor user satisfaction and negatively affecting the credibility of the university. The study recommends making an improvement in content, design, layout and new technology, it is necessary to work closely with the faculties and institutes on a ‘case by case’ basis, and finally to improve site performance

Molecular dynamics-driven global tetra-atomic potential energy surfaces: Application to the AlF dimer

In this work, we present a general machine learning approach for full-dimensional potential energy surfaces for tetra-atomic systems. Our method employs an active learning scheme trained on {\it ab initio} points, which size grows based on the accuracy required. The training points are selected based on molecular dynamics simulations, choosing the most suitable configurations for different collision energy and mapping the most relevant part of the potential energy landscape of the system. The present approach does not require long-range information and is entirely general. As an example, we provide the full-dimensional AlF-AlF potential energy surface, requiring $\lesssim 0.1\%$ of the configurations to be calculated {\it ab initio}. Furthermore, we analyze the general properties of the AlF-AlF system, finding key difference with other reported results on CaF or bi-alkali dimers

Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection

Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly. As a result, various automatic methods have been proposed to construct CSKG with larger semantic coverage. However, these unsupervised approaches introduce spurious noise that can lower the quality of the resulting CSKG, which cannot be tackled easily by existing denoising algorithms due to the unique characteristics of nodes and structures in CSKGs. To address this issue, we propose Gold (Global and Local-aware Denoising), a denoising framework for CSKGs that incorporates entity semantic information, global rules, and local structural information from the CSKG. Experiment results demonstrate that Gold outperforms all baseline methods in noise detection tasks on synthetic noisy CSKG benchmarks. Furthermore, we show that denoising a real-world CSKG is effective and even benefits the downstream zero-shot commonsense question-answering task.Comment: Accepted to EMNLP findings 202