Arts: An Interactive medium throughout utilizing abstraction, simplicity and harmony

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Preparation of [C60]Fullerene Nanowhisker-gold Nanoparticle Composites and Reduction of 4-Nitrophenol through Catalysis

A gold nanoparticle solution was prepared by adding sodium borohydride (NaBH4), trisodium citrate dihydrate (C6H5Na3O7⋅2H2O), cetyltrimethyl ammonium bromide (CTAB,(C16H33)N(CH3)3Br), ascorbic acid (C6H8O6), and potassium tetrachloroaurate(III)(KAuCl4) to distilled water and stirring the solution for 15 min. [C60]fullerene nanowhisker-gold nanoparticle composites were synthesized using C60-saturated toluene, the gold nanoparticle solution, and isopropyl alcohol by liquid-liquid interfacial precipitation (LLIP). The product of the nanocomposites was characterized by X-ray diffraction, scanning electron microscopy, Raman spectroscopy, transmission electron microscopy, and solid-state 13C-nuclear magnetic resonance spectroscopy. The catalytic activity of the [C60]fullerene nanowhisker-gold nanoparticle composites was confirmed in 4-nitrophenol reduction by UV-vis spectroscopy

Aesthetic approach to green transportation planning in tourism with design factors

Green transportation technology has become a new paradigm in transportation engineeringfields over the whole world. Most attention, however, has been paid to mitigate greenhousegases or energy consumption, seldom reported pro-environment design in aesthetic aspect. Itis well known that a proper design of train enhances traveler satisfaction and contributes todemand increase in public transportation. Hence, aesthetic factors should be consideredsignificantly. However, a difficult problem for reflecting aesthetic aspect is that there is noclear design standard for railway to reflect aesthetic features. This paper aims to suggestaesthetic factors to be guidance of rail transit planning in tourist attractions. We begin bydefining a term ‘aesthetic in railway system’ based on literature and empirical review. In thispaper, the definition is divided into two complementary views: (I) Sight-seeing mechanismfrom inside to outside. (II) Vehicle exterior design harmonized with surrounding environment.Based on the definitions, design factors are suggested: window size, speed, routes, type oftrack, color, and size of train system. Each factor is explained with its standard. A result of evaluating rail transit with the factors shows that wireless tram is the most suitable transit for tourism. Limitations and improvements of the study are also suggested.Keywords: Green transportation technology; Public transportation; Aesthetic factor; Touris

Efficient Continuous Manifold Learning for Time Series Modeling

Modeling non-Euclidean data is drawing attention along with the unprecedented successes of deep neural networks in diverse fields. In particular, symmetric positive definite (SPD) matrix is being actively studied in computer vision, signal processing, and medical image analysis, thanks to its ability to learn appropriate statistical representations. However, due to its strong constraints, it remains challenging for optimization problems or inefficient computation costs, especially, within a deep learning framework. In this paper, we propose to exploit a diffeomorphism mapping between Riemannian manifolds and a Cholesky space, by which it becomes feasible not only to efficiently solve optimization problems but also to reduce computation costs greatly. Further, in order for dynamics modeling in time series data, we devise a continuous manifold learning method by integrating a manifold ordinary differential equation and a gated recurrent neural network in a systematic manner. It is noteworthy that because of the nice parameterization of matrices in a Cholesky space, it is straightforward to train our proposed network with Riemannian geometric metrics equipped. We demonstrate through experiments that the proposed model can be efficiently and reliably trained as well as outperform existing manifold methods and state-of-the-art methods in two classification tasks: action recognition and sleep staging classification