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To address sustainability issues by facilitating the use of high-volume fly ash (HVFA) concrete in industry, this paper investigates the early age hydration properties of HVFA binders in concrete and the correlation between hydration properties and compressive strengths of the cement pastes. A new method of calculating the chemically bound water of HVFA binders was used and validated. Fly ash (FA) types used in this study were sourced from Indonesia and Australia for comparison. The water to binder (w/b) ratio was 0.4 and FA replacement levels were 40%, 50% and 60% by weight. Isothermal calorimetry tests were conducted to study the heat of hydration which was further converted to the adiabatic temperature rise. Thermo-gravimetric analysis (TGA) was employed to explore the chemically bound water (WB) of the binders. The results showed that Australian FA pastes had higher heat of hydration, adiabatic temperature rise, WB and compressive strength compared to Indonesian FA pastes. The new method of calculating chemically bound water can be successfully applied to HVFA binders. Linear correlation could be found between the WB and compressive strength.

In genetics, we often encounter a large number of highly correlated test statistics. The most famous conservative bound for multiple comparison is Bonferroni's bound, which is suitable when the test statistics are independent but not when the test statistics are highly correlated. This article proposes a new conservative bound that is easily calculated without multiple integration and is a good approximation when the test statistics are highly correlated. The performance of the proposed method is evaluated by simulation and real data analysis.

The minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean square error (MMSE) estimation especially for non-Gaussian situations. There is an important performance bound on the MEE estimation, namely the W-S lower bound, which is computed as the conditional entropy of the unknown variable given observation. Though it has been known in the literature for a considerable time, up to now there is little study on this performance bound. In this paper, we reexamine the W-S lower bound. Some basic properties of the W-S lower bound are presented, and the characterization of Gaussian distribution using the W-S lower bound is investigated.