Long-term mechanical performance of high fluidity fiber reinforced concrete modified by metakaolin

To clarify the long-term strength and toughness of metakaolin (MK) and steel fiber (SF) modified concrete with higher fluidity and water/binder ratio, a series of tests including slump tests, compression tests, splitting tests, digital image processing and Scanning Electron Microscope (SEM) tests were performed on MK-SF concrete cured for 7–360 days. Results reveal that the slump of fresh concrete decreased with an increase in the MK and SF replacement rates. Moreover, the impact of MK on the slump of steel fiber reinforced concrete (SFRC) was more pronounced when combined with a lower water/binder ratio, resulting in increased viscosity. At the pre-peak stress region of the strain-stress curve, the compressive strength fc, tensile strength ft, Young’s modulus Ec, elastic modulus E0, and tensile strain at peak stress εt-max of high fluidity MK-SF concrete increased with increasing MK and SF admixing ratio, regardless of curing age. Notably, the coupling effects of MK and SF became more prominent after long-term curing. Without MK incorporation, the effects of SF and curing time on the above indices were relatively implicit. At the post-peak stress region of strain-stress curves, there existed a residual stage. The inclusion of MK significantly improved the long-term residual strength and strain of SFRC. Additionally, the toughness index Mc, which represents the total area of the compressive strain-stress curve containing both the pre-peak and post-peak regions, also exhibited substantial development with curing time, primarily attributed to the incorporation of MK and SF. The coupling of MK and SF led to a transformation of the concrete failure mode from brittle to ductile. Regression analysis reveals that a linear equation adequately described the long-term relationships of fc-ft, fc-Ec, fc-E0, fc-Mc, and fc-εt-max in MK-modified SFRC. Based on the testing data, a relative strength or toughness index λ and a new generalized hyperbola model were proposed to predict the long-term mechanical behavior mentioned above. Through crack morphology and microstructure analysis, the distinct roles of MK and SF in the composite material were examined

Cerium doped copper/ZSM-5 catalysts used for the selective catalytic reduction of nitrogen oxide with ammonia

The CuCe/ZSM-5 catalysts with different cerium loadings (0, 0.5, 1.0, 1.5 and 2.0wt.%) was investigated to evaluate the correlation between structural characteristics and catalytic performance for the selective catalytic reduction (SCR) of NO by NH3. It was found that the addition of cerium increased copper dispersion and prevented its crystallization. According to the results of X-ray photoelectron spectroscopy (XPS) and temperature-programmed reduction by hydrogen (H2-TPR), copper species were enriched on the ZSM-5 grain surfaces and part of copper ions was incorporated into the cerium lattice. Addition of cerium improved the redox properties of the CuCe/ZSM-5 catalysts, owing to the higher valence of copper and mobility of lattice oxygen than those of Cu/ZSM-5 catalyst. Hence the introduction of cerium in Cu/ZSM-5 improved significantly NO conversion. On the one hand, the cerium introduction into Cu-Z enhances their low-temperature activities. 95% NO conversion is reached around 197°C for Cu-Z while the corresponding temperature value decreases to 148°C for CuCe4-Z. On the other hand, the temperature range of efficient NO reduction (95%) also extends to higher temperature when the cerium are added to Cu/ZSM-5. Among the Cu-Ce/ZSM-5 catalysts tested, the CuCe4-Z sample exhibits the highest catalytic activity with the temperature range for 90% NO removal of 148-427°C

Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks

Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On the one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose the Multi-adversarial spatiotemporal recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather predictions. Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations. Then, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we present an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.Comment: 9 pages, 6 figure

Causal relationships of neonatal jaundice, direct bilirubin and indirect bilirubin with autism spectrum disorder: A two-sample Mendelian randomization analysis

BackgroundMultiple systematic reviews and meta-analyses have examined the association between neonatal jaundice and autism spectrum disorder (ASD) risk, but their results have been inconsistent. This may be because the included observational studies could not adjust for all potential confounders. Mendelian randomization study can overcome this drawback and explore the causal relationship between the both.MethodsWe used the data of neonatal jaundice, direct bilirubin (DBIL), indirect bilirubin (IBIL), and ASD collected by genome-wide association study (GWAS) to evaluate the effects of neonatal jaundice, DBIL and IBIL on ASD by using a two-sample Mendelian randomized (MR). The inverse variance-weighted method (IVW) was the main method of MR analysis in this study. Weighted median method, MR-Egger regression and mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test were used for sensitivity analysis.ResultsThere was no evidence of an effect of neonatal jaundice (OR, 1.002, 95% CI, 0.977–1.027), DBIL (OR, 0.970, 95% CI, 0.884–1.064) and IBIL (OR, 1.074, 95% CI, 0.882–1.308) on ASD risk by IVW test. In the weighted median method, MR-Egger regression and leave-one-out analysis, the results were robust and no heterogeneity or pleiotropy was observed.ConclusionsWe found that neonatal jaundice, DBIL and IBIL were not associated with ASD in this study. However, this paper did not explore the effect of severity and duration of jaundice on ASD in different ethnic populations, which may require further research

Sparse Signal Inversion with Impulsive Noise by Dual Spectral Projected Gradient Method

We consider sparse signal inversion with impulsive noise. There are three major ingredients. The first is regularizing properties; we discuss convergence rate of regularized solutions. The second is devoted to the numerical solutions. It is challenging due to the fact that both fidelity and regularization term lack differentiability. Moreover, for ill-conditioned problems, sparsity regularization is often unstable. We propose a novel dual spectral projected gradient (DSPG) method which combines the dual problem of multiparameter regularization with spectral projection gradient method to solve the nonsmooth l1+l1 optimization functional. We show that one can overcome the nondifferentiability and instability by adding a smooth l2 regularization term to the original optimization functional. The advantage of the proposed functional is that its convex duality reduced to a constraint smooth functional. Moreover, it is stable even for ill-conditioned problems. Spectral projected gradient algorithm is used to compute the minimizers and we prove the convergence. The third is numerical simulation. Some experiments are performed, using compressed sensing and image inpainting, to demonstrate the efficiency of the proposed approach