Effect of admixtures on the yield stresses of cement pastes under high hydrostatic pressures

When cement-based materials are transported at a construction site, they undergo high pressures during the pumping process. The rheological properties of the materials under such high pressures are unknown, and estimating the workability of the materials after pumping is a complex problem. Among various influential factors on the rheology of concrete, this study investigated the effect of mineral and chemical admixtures on the high-pressure rheology. A rheometer was fabricated that could measure the rheological properties while maintaining a high pressure to simulate the pumping process. The effects of superplasticizer, silica fume, nanoclay, fly ash, or ground granulated blast furnace slag were investigated when mixed with two control cement pastes. The water-to-cement ratios were 0.35 and 0.50.ope

Cyberattacks: Does Physical Boundry Matter?

Information security issues are characterized with interdependence. Particularly, cyber criminals can easily cross national boundaries and exploit jurisdictional limitations between countries. Thus, whether cyber attacks are spatially autocorrelated is a strategic issue for government authorities and a tactic issue for insurance companies. Through an empirical study of cyber attacks across 62 countries during the period 2003-2007, we find little evidence on the spatial autocorrelation of cyber attacks at any week. However, after considering economic opportunity, IT infrastructure, international collaboration in enforcement and conventional crimes, we find strong evidence that cyber attacks were indeed spatially autocorrelated as they moved over time. The policy and managerial implication is that physical boundary should be an important factor in addressing strategic cyber attacks and their potential risks

Robust Likelihood-Based Survival Modeling with Microarray Data

Gene expression data can be associated with various clinical outcomes. In particular, these data can be of importance in discovering survival-associated genes for medical applications. As alternatives to traditional statistical methods, sophisticated methods and software programs have been developed to overcome the high-dimensional difficulty of microarray data. Nevertheless, new algorithms and software programs are needed to include practical functions such as the discovery of multiple sets of survival-associated genes and the incorporation of risk factors, and to use in the R environment which many statisticians are familiar with. For survival modeling with microarray data, we have developed a software program (called rbsurv) which can be used conveniently and interactively in the R environment. This program selects survival-associated genes based on the partial likelihood of the Cox model and separates training and validation sets of samples for robustness. It can discover multiple sets of genes by iterative forward selection rather than one large set of genes. It can also allow adjustment for risk factors in microarray survival modeling. This software package, the rbsurv package, can be used to discover survival-associated genes with microarray data conveniently.