In the present work, a full range of compositions of xBi(2/3)MoO4-(1 -x)BiVO4 (0.0 ≤ x ≤ 1.0) was prepared by the solid state reaction method. All the ceramic compositions could be readily densified to below 850 °C. As the x value increased, the monoclinic scheelite structure continuously changed to a tetragonal structure at x = 0.10, which means the ferroelastic phase transition temperature was lowered to near room temperature. In the compositional range 0.50 ≤ x < 0.70, a novel ordered scheelite phase was formed, most likely through A-site vacancy ordering. For compositions x ≥ 0.70, a composite two-phase region consisting of the ordered scheelite and Bi(2/3)MoO4 phases was formed. High microwave permittivity around 75 and Qf values around 8000 GHz could be obtained in the compositions near the phase boundaries between monoclinic and tetragonal scheelite phases. The intrinsic microwave dielectric properties were extrapolated from the far infrared reflectivity spectra, and it was found that the polarization was dominated by the Bi-O stretches when x ≤ 0.10.

Layout of the interior works during building construction is time-consuming and error-prone. Given the cost involved, both for initial layout and for later rework where errors occur, researchers have sought to automate the layout task. Some have adopted marker-less augmented reality (AR) methods using heads-up displays or cameras, and others have proposed robots capable of marking out the works. The former encumber the workers, the latter are expensive to set up and are sensitive to site conditions, and neither has yet achieved the required accuracy. In this work, we propose a more efficient approach to project relevant information from a Building Information Model (BIM) onto the construction surface, directly augmenting the construction site with the design information. This is done using a portable system consisting of a 2D laser scanner, an angled adjustable projector, and a camera. The system localizes itself within the already built outer walls using the laser scanner and the BIM model using a method derived from robotic mapping; it calibrates the projection correction parameters (keystone correction) using image analysis; and it projects the information with the angled projector. Testing results showed that the localization was accurate within a few millimeters and less than three degrees, and the final projected image's error was approximately one centimeter. Initial calibration requires less than one minute and does not require specialist skills. The system automates the layout task, preserves accuracy, and can provide rich model information on any interior surface. Note to Practitioners-Measuring and marking up of the interior works during building construction is time-consuming and error-prone. Layout must be repeated for each trade: partitions, false ceilings, mechanical, electrical and plumbing systems, flooring and furnishings all require accurate marking of locations on floors, walls and ceiling surfaces. Current automation is limited to the use of robotic total stations, but these only locate specific predetermined points. We propose a simple solution for automated layout, in which images from a building information model (BIM) are projected directly onto the work surface. The system projects any desired informationdrawings, images, etc.onto the work surface (floor, walls or ceiling) in the correct location, scale and orientation. The prototype apparatus consists of a laser range scanner, a projector, and a camera. Projection of the work instructions directly onto the work surface is accurate and immediate. It saves the time required for workers to interpret R. Sacks is with the Division of Structural Engineering and Construction Management, Faculty of Civil and Environmental Engineering, Technion -and then mark up the dimensional information and it avoids the human error involved. The prototype is limited to environments in which computer projection is practical and currently requires planar surfaces. ). L. Ma is with the School of Art, Design and Architecture, University of Huddersfield, Huddersf...

In this article, three parts of work have been done. First, silver nano-particle dispersion had been obtained by liquid chemical reduction method with Ag+ concentration as 2.7mol/l and UV-vis, SEM were used to characterize the silver nano-particles. Then, the dispersion was purified by solvent deposit method for three times with acetone acting as the deposit agent and water-based gravure ink was obtained after adding water, resin, and other additives. The silver content and viscosity of the ink were measured by TG and rheometer. Finally, the ink was used to fabricate transparent conductive film (TCF) with PET as the substrate. The transmissivity, adhesion, conductivity, and the edge sharpness were measured. The results show that ration of silver nanoplates in the dispersion synthesized can reach to 70%. From TG curve, the silver content of the ink is wt. 49%. Viscosity of the gravure ink is 129mPa•s. The transmissivity of the film is around 80% and the calculated resistivity is 1.53×10-4Ω.cm.

SVM is a novel machine learning technique developed on empirical risk minimization principle. SVM has many advantages in solving small sample size, nonlinear and high dimensional pattern recognition problem. Based on the study of SVM, this paper discusses its application in the supply chain partner selection that provides a reference for enterprise to select the partner.