A 3D OPENFOAM BASED FINITE VOLUME SOLVER FOR INCOMPRESSIBLE OLDROYD-B MODEL WITH INFINITY RELAXATION TIME.

In this paper, we develop a Finite Volume solver for a 3D incompressible Oldroyd-B model with infinity relaxation time. The Finite Volume solver is implemented by using a leading open-source computational mechanics software OpenFOAM. We have imposed the divergence free condition as a constraint on the momentum equation to derive a pressure equation and a predictor-corrector procedure is applied when solving the velocity field. Both stability analysis and numerical experiments are given to show the robustness and accuracy of our algorithm. Two concrete examples on a cubical domain and a dumbbell are computed and illustrated.

Synthesis of Atomically Thin Boron Films on Copper Foils.

Two-dimensional boron materials have recently attracted extensive theoretical interest because of their exceptional structural complexity and remarkable physical and chemical properties. However, such 2D boron monolayers have still not been synthesized. In this report, the synthesis of atomically thin 2D γ-boron films on copper foils is achieved by chemical vapor deposition using a mixture of pure boron and boron oxide powders as the boron source and hydrogen gas as the carrier gas. Strikingly, the optical band gap of the boron film was measured to be around 2.25 eV, which is close to the value (2.07 eV) determined by first-principles calculations, suggesting that the γ-B28 monolayer is a fascinating direct band gap semiconductor. Furthermore, a strong photoluminescence emission band was observed at approximately 626 nm, which is again due to the direct band gap. This study could pave the way for applications of two-dimensional boron materials in electronic and photonic devices.

Video-based 3D reconstruction, laparoscope localization and deformation recovery for abdominal minimally invasive surgery: a survey.

BACKGROUND: The intra-operative three-dimensional (3D) structure of tissue organs and laparoscope motion are the basis for many tasks in computer-assisted surgery (CAS), such as safe surgical navigation and registration of pre-operative and intra-operative data for soft tissues. METHODS: This article provides a literature review on laparoscopic video-based intra-operative techniques of 3D surface reconstruction, laparoscope localization and tissue deformation recovery for abdominal minimally invasive surgery (MIS). RESULTS: This article introduces a classification scheme based on the motions of a laparoscope and the motions of tissues. In each category, comprehensive discussion is provided on the evolution of both classic and state-of-the-art methods. CONCLUSIONS: Video-based approaches have many advantages, such as providing intra-operative information without introducing extra hardware to the current surgical platform. However, an extensive discussion on this important topic is still lacking. This survey paper is therefore beneficial for researchers in this field. Copyright © 2015 John Wiley & Sons, Ltd.

Fast and large-area growth of uniform MoS2 monolayers on molybdenum foils.

A controllable synthesis of two-dimensional crystal monolayers in a large area is a prerequisite for potential applications, but the growth of transition metal dichalcogenide monolayers in a large area with spatial homogeneity remains a great challenge. Here we report a novel and efficient method to fabricate large-scale MoS2 monolayers by direct sulfurization of pre-annealed molybdenum foil surfaces with large grain boundaries of more than 50 µm in size at elevated temperatures. Continuous MoS2 monolayers can be formed uniformly by sulfurizing the Mo foils in sulfur vapor at 600 °C within 1 min. At a lower temperature even down to 500 °C, uniform MoS2 monolayers can still be obtained but in a much longer sulfurizing duration. It is demonstrated that the formed monolayers can be nondestructively transferred onto arbitrary substrates by removing the Mo foil using diluted ferric chloride solution and can be successfully fabricated into photodetectors. The results show a novel avenue to efficiently fabricate two-dimensional crystals in a large area in a highly controllable way and should have great potential for the development of large-scale applications of two-dimensional crystals in electrophotonic systems.

A novel approach to nodule feature optimization on thin section thoracic CT.

RATIONALE AND OBJECTIVES: An analysis for the optimum selection of image features in feature domain to represent lung nodules was performed, with implementation into a classification module of a computer-aided diagnosis system. MATERIALS AND METHODS: Forty-two regions of interest obtained from 38 cases with effective diameters of 3 to 8.5 mm were used. On the basis of image characteristics and dimensionality, 11 features were computed. Nonparametric correlation coefficients, multiple regression analysis, and principal-component analysis were used to map the relation between the represented features from four radiologists and the computed features. An artificial neural network was used for the classification of benign and malignant nodules to test the hypothesis obtained from the mapping analysis. RESULTS: Correlation coefficients ranging from 0.2693 to 0.5178 were obtained between the radiologists' annotations and the computed features. Of the 11 features used, three were found to be redundant when both nodule and non-nodule cases were used, and five were found redundant when nodule or non-nodule cases were used. Combination of analysis from correlation coefficients, regression analysis, principal-component analysis, and the artificial neural network resulted in the selection of optimum features to achieve F-test values of 0.821 and 0.643 for malignant and benign nodules, respectively. CONCLUSION: This study demonstrates that for the optimum selection of features, each feature should be analyzed individually and collectively to evaluate the impact on the computer-aided diagnosis system on the basis of its class representation. This methodology will ultimately aid in improving the generalization capability of a classification module for early lung cancer diagnosis.