Preparation of stereoregular block copolymer comprising isotactic PMMA and polyisobutylene blocks and its characterization

Summary A triblock copolymer of isotactic(it)-poly(methyl methacrylate) (PMMA) and polyisobutylene (PIB), it-PMMA-block-PIB-block-it-PMMA, was prepared by anionic polymerization of triphenylmethyl methacrylate initiated with , -dilithiated PIB diisobutyrate in tetrahydrofuran at -78°C, and subsequntt hydrolysis and methylation with diazomethane. Molecular weight distribution of the triblock copolymer was narrow, and the stereoregularity of the PMMA block was highly isotactic. Proton spin-lattice relaxation times of the block copolymer in acetone-d₆, which is non-solvent for PIB but dissolves the block copolymer, indicate the aggregation of the copolymer through PIB block. Stereocomplex formation between the it-block copolymer and syndiotactic(st)-PMMA-block-PIB-block-st-PMMA was also studied.     

Makeup Extraction of 3D Representation via Illumination-Aware Image Decomposition

Facial makeup enriches the beauty of not only real humans but also virtual characters; therefore, makeup for 3D facial models is highly in demand in productions. However, painting directly on 3D faces and capturing real-world makeup are costly, and extracting makeup from 2D images often struggles with shading effects and occlusions. This paper presents the first method for extracting makeup for 3D facial models from a single makeup portrait. Our method consists of the following three steps. First, we exploit the strong prior of 3D morphable models via regression-based inverse rendering to extract coarse materials such as geometry and diffuse/specular albedos that are represented in the UV space. Second, we refine the coarse materials, which may have missing pixels due to occlusions. We apply inpainting and optimization. Finally, we extract the bare skin, makeup, and an alpha matte from the diffuse albedo. Our method offers various applications for not only 3D facial models but also 2D portrait images. The extracted makeup is well-aligned in the UV space, from which we build a large-scale makeup dataset and a parametric makeup model for 3D faces. Our disentangled materials also yield robust makeup transfer and illumination-aware makeup interpolation/removal without a reference image.     

Hot water resistance of glass‐fiber and glass‐bead reinforced thermoplastics

The hot water resistance of three kinds of short glass fiber or glass bead‐reinforced plastics [polyphenyleneether (PPE), polyphenylenesulfide (PPS), and polyoxymethylene (POM)] was studied by hot water immersion testing, tensile testing and water‐hammer fatigue testing. It was found that the degradation of the strength was observed only for the reinforced plastics under hot water immersion and that the change of the tensile strength was most drastic in glass fiber‐reinforced PPS (GFPPS). Scanning electron microscope (SEM) observations of the tensile fracture surface revealed that the change in tensile strength was attributable to the deterioration of the interface between the glass fiber and the matrix resin. The results of acoustic emission analysis also supported the conclusion that the change in strength was due to the deterioration of the interface. Although the change in the tensile strength of glass fiber‐reinforced PPE (GFPPE) was small compared with that of GFPPS, debonding between the glass fiber and the matrix resin and surface cracks was observed on the surface of the GFPPE specimens.     

Data-driven modelling of turbine wake interactions and flow resistance in large wind farms

Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine and (2) by changing the overall flow resistance in the farm and thus the so-called farm blockage effect. To better predict these effects with low computational costs, we develop data-driven emulators of the ‘local’ or ‘internal’ turbine thrust coefficient $C_T^{\ast }$ as a function of turbine layout. We train the model using a multi-fidelity Gaussian process (GP) regression with a combination of low (engineering wake model) and high-fidelity (large eddy simulations) simulations of farms with different layouts and wind directions. A large set of low-fidelity data speeds up the learning process and the high-fidelity data ensures a high accuracy. The trained multi-fidelity GP model is shown to give more accurate predictions of $C_T^{\ast }$ compared to a standard (single-fidelity) GP regression applied only to a limited set of high-fidelity data. We also use the multi-fidelity GP model of $C_T^{\ast }$ with the two-scale momentum theory (Nishino & Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that the model can be used to give fast and accurate predictions of large wind farm performance under various mesoscale atmospheric conditions. This new approach could be beneficial for improving annual energy production (AEP) calculations and farm optimization in the future.