Initial investigation into the use of GGBS-MgO in soil stabilisation

Portland cement (PC) is the most widely used binder for ground improvement. However, there are significant environmental impacts associated with its production in terms of high energy consumption and CO2 emissions. Hence, the use of industrial by-products materials or new low-carbon footprint alternative cements has been encouraged. Ground granulated blastfurnace slag (GGBS), a by-product of the steel industry, has been successfully used for such an application, usually activated with an alkali such as lime or PC. In this study the use of MgO as a novel activator for GGBS in ground improvement of soft soils is addressed and its performance was compared to the above two conventional activators as well as PC alone. The GGBS:activator ratio used in this study was 9:1. A range of tests was performed at three curing periods (7, 28 and 90 days), including unconfined compressive strength (UCS), permeability and microstructure analysis. The results show that the MgO performed as the most efficient activator yielding the highest strength and the lowest permeability indicating a very high stabilisation efficiency of the system. © 2012 American Society of Civil Engineers.

"Plateau"-related summary statistics are uninformative for comparing working memory models.

Performance on visual working memory tasks decreases as more items need to be remembered. Over the past decade, a debate has unfolded between proponents of slot models and slotless models of this phenomenon (Ma, Husain, Bays (Nature Neuroscience 17, 347-356, 2014). Zhang and Luck (Nature 453, (7192), 233-235, 2008) and Anderson, Vogel, and Awh (Attention, Perception, Psychophys 74, (5), 891-910, 2011) noticed that as more items need to be remembered, "memory noise" seems to first increase and then reach a "stable plateau." They argued that three summary statistics characterizing this plateau are consistent with slot models, but not with slotless models. Here, we assess the validity of their methods. We generated synthetic data both from a leading slot model and from a recent slotless model and quantified model evidence using log Bayes factors. We found that the summary statistics provided at most 0.15 % of the expected model evidence in the raw data. In a model recovery analysis, a total of more than a million trials were required to achieve 99 % correct recovery when models were compared on the basis of summary statistics, whereas fewer than 1,000 trials were sufficient when raw data were used. Therefore, at realistic numbers of trials, plateau-related summary statistics are highly unreliable for model comparison. Applying the same analyses to subject data from Anderson et al. (Attention, Perception, Psychophys 74, (5), 891-910, 2011), we found that the evidence in the summary statistics was at most 0.12 % of the evidence in the raw data and far too weak to warrant any conclusions. The evidence in the raw data, in fact, strongly favored the slotless model. These findings call into question claims about working memory that are based on summary statistics.

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.

Simulations of autoignition and laminar premixed flames in methane/air mixtures diluted with hot products

This article considers constant-pressure autoignition and freely propagating premixed flames of cold methane/air mixtures mixed with equilibrium hot products at high enough dilution levels to burn within the moderate to intense low oxygen dilution (MILD) combustion regime. The analysis is meant to provide further insight on MILD regime boundaries and to identify the effect of hot products speciation. As the mass fraction of hot products in the reactants mixture increases, autoignition occurs earlier. Species profiles show that the products/reactants mixture approximately equilibrates to a new state over a quick transient well before the main autoignition event, but as dilution becomes very high, this equilibration transient becomes more prominent and eventually merges with the primary ignition event. The dilution level at which these two reactive zones merge corresponds well with that marking the transition into the MILD regime, as defined according to conventional criteria. Similarly, premixed flame simulations at high dilutions show evidence of significant reactions involving intermediate species prior to the flame front. Since the premixed flame governing equations system demands that the species and temperature gradients be zero at the "cold" boundary, flame speed cannot be calculated above a certain dilution level. Up to this point, which again agrees reasonably well with the transition into the MILD regime according to convention, the laminar burning velocity was found to increase with hot product dilution while flame thickness remained largely unchanged. Some comments on the MILD combustion regime boundary definition for gas turbine applications are included. Copyright © Taylor & Francis Group, LLC.