A study of Mg adsorption on Si(001) surface from first principles

First-principles calculations using density functional theory based on norm-conserving pseudopotentials have been performed to investigate the Mg adsorption on the Si(001) surface for 1/4, 1/2 and 1 monolayer coverages. For both 1/4 and 1/2 ML coverages it has been found that the most favorable site for the Mg adsorption is the cave site between two dimer rows consistent with the recent experiments. For the 1 ML coverage we have found that the most preferable configuration is when both Mg atoms on 2x1 reconstruction occupy the two shallow sites. We have found that the minimum energy configurations for 1/4 ML coverage is a 2x2 reconstruction while for the 1/2 and 1 ML coverages they are 2x1.Comment: 7 pages, 4 figure

Cs adsorption on Si(001) surface: ab initio study

First-principles calculations using density functional theory based on norm-conserving pseudopotentials have been performed to investigate the Cs adsorption on the Si(001) surface for 0.5 and 1 ML coverages. We found that the saturation coverage corresponds to 1 ML adsorption with two Cs atoms occupying the double layer model sites. While the 0.5 ML covered surface is of metallic nature, we found that 1 ML of Cs adsorption corresponds to saturation coverage and leads to a semiconducting surface. The results for the electronic behavior and surface work function suggest that adsorption of Cs takes place via polarized covalent bonding.Comment: 8 pages, 7 figure

Machine Learning Methods for Attack Detection in the Smart Grid

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semi-supervised) are employed with decision and feature level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than the attack detection algorithms which employ state vector estimation methods in the proposed attack detection framework.Comment: 14 pages, 11 Figure

Electronic Structure of a Chain-like Compound: TlSe

An ab-initio pseudopotential calculation using density functional theory within the local density approximation has been performed to investigate the electronic properties of TlSe which is of chain-like crystal geometry. The energy bands and effective masses along high symmetry directions, the density of states and valence charge density distributions cut through various planes are presented. The results have been discussed in terms of previously existing experimental and theoretical data, and comparisons with similar compounds have been made.Comment: 7 page

The quantity-quality transition in Asia

Societies in which fertility is falling and human capital investment per child increasing are experiencing a “quantity-quality transition.” Such transitions imply, over the long term, both slower rates of labor force growth and higher levels of human capital per worker. They are fundamental to economic development. Yet, these transitions are neither automatic or self-propelling. Their momentum depends on competing forces acting at both the family and the macroeconomic levels; the balance can easily tip against further transition. Family decisions about schooling are largely motivated by its private economic returns. These returns are determined in labor markets, and here the logic of supply and demand applies. When families decide to invest more deeply in their children, they collectively produce right-ward shifts in the supply of educated young labor. If other things are held fixed, the rate of return to schooling should then fall, and this, in turn, should dampen parental enthusiasm for further educational investments. Reductions in the rate of return should also weaken the case for continued reductions in fertility. Unless they are counterbalanced by other forces, such negative feedbacks would tend to bring a quantity-quality transition to a halt. The aim of this paper is to explore both the negative and positive feedbacks that have affected the quantity-quality transition in Asia. We assemble the leading hypotheses and evidence on the macroeconomic forces, both domestic and international, that could influence returns to schooling. We also examine family factors, giving particular attention to the intergenerational links that seem to have maintained the momentum of the Asian transition. Our conclusion is that negative feedbacks associated with increases in the relative supplies of educated labor have been largely offset by beneficial macroeconomic change (resulting from increases in the stock of physical capital, substantial technological change, and trade) and by powerful family-level effects that, over the generations, have continued to propel the transition