Multi-scale simulation of capillary pores and gel pores in Portland cement paste

The microstructures of Portland cement paste (water to cement ratio is 0.4, curing time is from 1 day to 28 days) are simulated based on the numerical cement hydration model, HUMOSTRUC3D (van Breugel, 1991; Koenders, 1997; Ye, 2003). The nanostructures of inner and outer C-S-H are simulated by the packing of monosized (5 nm) spheres. The pore structures (capillary pores and gel pores) of Portland cement paste are established by upgrading the simulated nanostructures of C-S-H to the simulated microstructures of Portland cement paste. The pore size distribution of Portland cement paste is simulated by using the image segmentation method (Shapiro and Stockman, 2001) to analyse the simulated pore structures of Portland cement paste. The simulation results indicate that the pore size distribution of the simulated capillary pores of Portland cement paste at the age of 1 day to 28 days is in a good agreement with the pore size distribution determined by scanning electron microscopy (SEM). The pore size distribution of the simulated gel pores of Portland cement paste (interlayer gel pores of outer C-S-H and gel pores of inner C-S-H are not included) is validated by the pore size distribution obtained by mercury intrusion porosimetry (MIP). The pores with pore size of 20 nm to 100 nm occupy very small volume fraction in the simulated Portland cement paste at each curing time (0.69% to 1.38%). This is consistent with the experimental results obtained by nuclear magnetic resonance (NMR)

Multilabel Consensus Classification

In the era of big data, a large amount of noisy and incomplete data can be collected from multiple sources for prediction tasks. Combining multiple models or data sources helps to counteract the effects of low data quality and the bias of any single model or data source, and thus can improve the robustness and the performance of predictive models. Out of privacy, storage and bandwidth considerations, in certain circumstances one has to combine the predictions from multiple models or data sources to obtain the final predictions without accessing the raw data. Consensus-based prediction combination algorithms are effective for such situations. However, current research on prediction combination focuses on the single label setting, where an instance can have one and only one label. Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time. Direct applications of existing prediction combination methods to multilabel settings can lead to degenerated performance. In this paper, we address the challenges of combining predictions from multiple multilabel classifiers and propose two novel algorithms, MLCM-r (MultiLabel Consensus Maximization for ranking) and MLCM-a (MLCM for microAUC). These algorithms can capture label correlations that are common in multilabel classifications, and optimize corresponding performance metrics. Experimental results on popular multilabel classification tasks verify the theoretical analysis and effectiveness of the proposed methods

Transport of titanium dioxide nanoparticles in saturated porous media under various solution chemistry conditions

Because of its wide applications, nanosized titanium dioxide may become a potential environmental risk to soil and groundwater system. It is therefore important to improve current understanding of the environmental fate and transport of titanium oxides nanoparticles (TONPs). In this work, the effect of solution chemistry (i.e., pH, ionic strength, and natural organic matter (NOM) concentration) on the deposition and transport of TONPs in saturated porous media was examined in detail. Laboratory columns packed with acid-cleaned quartz sand were used in the experiment as porous media. Transport experiments were conducted with various chemistry combinations, including four ionic strengths, three pH levels, and two NOM concentrations. The results showed that TONP mobility increased with increasing solution pH, but decreased with increasing solution ionic strength. It is also found that the presence of NOM in the system enhanced the mobility of TONPs in the saturated porous media. The Derjaguin–Landau–Verwey–Overbeek (DLVO) theory was used to justify the mobility trends observed in the experimental data. Predictions from the theory agreed excellently with the experimental data