Dependence of quantum-Hall conductance on the edge-state equilibration position in a bipolar graphene sheet

By using four-terminal configurations, we investigated the dependence of longitudinal and diagonal resistances of a graphene p-n interface on the quantum-Hall edge-state equilibration position. The resistance of a p-n device in our four-terminal scheme is asymmetric with respect to the zero point where the filling factor ($\nu$) of the entire graphene vanishes. This resistance asymmetry is caused by the chiral-direction-dependent change of the equilibration position and leads to a deeper insight into the equilibration process of the quantum-Hall edge states in a bipolar graphene system.Comment: 5 pages, 4 figures, will be published in PR

Design and Implementation of an Intranet Security and Access Control System in Ubi-Com

Currently, most enterprise intranet systems process user information for security and access authentication purposes. However, this information is often captured by unauthorized users who may edit, modify, delete or otherwise corrupt this data. In addition, corruption can result from inaccurate communication protocols in the web browser. Therefore, a method is needed to prevent unauthorized or erroneous access and modification of data through the intranet. This paper proposes an efficient security procedure that incorporates a new model that allows flexible web security access control in securing information over the intranet in UC. The proposed web security access control system improves the intranet data and access security by using encryption and decryption techniques. It further improves the security access control by providing authentication corresponding to different security page levels relevant to public ownership and information sensitivity between different enterprise departments. This approach reduces processing time and prevents information leakage and corruption caused by mistakes that occur as a result of communication protocol errors between client PC's or mail security methods

Thermoelectric Transport of Massive Dirac Fermions in Bilayer Graphene

Thermoelectric power (TEP) is measured in bilayer graphene for various temperatures and charge-carrier densities. At low temperatures, measured TEP well follows the semiclassical Mott formula with a hyperbolic dispersion relation. TEP for a high carrier density shows a linear temperature dependence, which demonstrates a weak electron-phonon interaction in the bilayer graphene. For a low carrier density, a deviation from the Mott relation is observed at high temperatures and is attributed to the low Fermi temperature in the bilayer graphene. Oscillating TEP and the Nernst effect for varying carrier density, observed in a high magnetic field, are qualitatively explained by the two dimensionality of the system.Comment: published versio

Difference in the color stability of direct and indirect resin composites

Indirect resin composites are generally regarded to have better color stability than direct resin composites since they possess higher conversion degree. OBJECTIVE: The present study aimed at comparing the changes in color (ΔE) and color coordinates (ΔL, Δa and Δb) of one direct (Estelite Sigma: 16 shades) and 2 indirect resin composites (BelleGlass NG: 16 shades; Sinfony: 26 shades) after thermocycling. MATERIAL AND METHODS: Resins were packed into a mold and light cured; post-curing was performed on indirect resins. Changes in color and color coordinates of 1-mm-thick specimens were determined after 5,000 cycles of thermocycling on a spectrophotometer. RESULTS: ΔE values were in the range of 0.3 to 1.2 units for direct resins, and 0.3 to 1.5 units for indirect resins, which were clinically acceptable (Δ

Influence of HEMA content on the mechanical and bonding properties of experimental HEMA-added glass ionomer cements

The purpose of this study was to determine the influence of incrementally added uncured HEMA in experimental HEMA-added glass ionomer cement (HAGICs) on the mechanical and shear bond strength (SBS) of these materials. Increasing contents of uncured HEMA (10-50 wt.%) were added to a commercial glass ionomer cement liquid (Fuji II, GC, Japan), and the compressive and diametral tensile strengths of the resulting HAGICs were measured. The SBS to non-precious alloy, precious alloy, enamel and dentin was also determined after these surfaces were subjected to either airborne-particle abrasion (Aa) or SiC abrasive paper grinding (Sp). Both strength properties of the HAGICs first increased and then decreased as the HEMA content increased, with a maximum value obtained when the HEMA content was 20% for the compressive strength and 40% for the tensile strength. The SBS was influenced by the HEMA content, the surface treatment, and the type of bonding surface (

miTarget: microRNA target gene prediction using a support vector machine

BACKGROUND: MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes. RESULTS: We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program. CONCLUSION: miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples