This study is performed to determine the characteristics of fabric sounds and the mechanical properties to predict their auditory comfort. In order to obtain quantitative information about emotional changes evoked by fabric sounds, physiological signals are acquired and analyzed when each fabric sound is presented to participants. Physiological parameters employed in this study are electroencephalogram and autonomic nervous system activities, including photoplethysmogram (PPG), skin conductance level (SCL), and the ratio of low frequency to high frequency (LF/HF) from the power spectrum of heart rate variability. As sound parameters, the pressure level of total sound (LPT), loudness(z), and sharpness(z) are calculated. Mechanical properties of the fabrics are measured with the KES-FB system. Bending hysteresis and weight. reflect negative sensations, influencing slow alpha, LF/HF, and SCL. Among sound parameters, LPT, loudness(z), and sharpness(z) influence negative sensations, causing the increment of SCL and LF/HF with the decrement of slow alpha and PPG.

In order to provide a fabric sound evaluation system for designing auditory-sensible fabrics, sound parameters are obtained, including the level pressure of total sound (LPT), sound color factors (Δ L and Δ f ), three coefficients (ARC, ARF, ARE) of autoregressive models based on the fast fourier transform spectrum, loudness(Z) and sharpness(Z) from Zwicker's psychoacoustic models, and mechanical properties from KES values for wool suiting fabrics. As psychophysical characteristics, subjective sensations of softness, loudness, sharpness, clearness, roughness, highness, and pleasantness of the fabric sounds are evaluated by the free modulus magnitude estimation. Tropical wool has the lowest loudness(Z) and the highest Δ L value among the fabrics. Melton, a thicker and heavier woolen, shows a trend similar to saxony and flannel for sound parameters. Wool suiting fabrics have higher scores for loudness and highness rather than clearness and pleasantness, except for tropical wool, which has the highest scores for pleasantness among the fabrics. Using the modified stepwise regression of Kawabata, all sensations are predicted by both sound parameters and mechanical properties. The sound sensation of wool suiting fabrics is related mainly to tensile, surface, and shear properties in mechanical measurements and with autoregressive coefficients as sound parameters.Fabric sounds have recently attracted both researchers and manufacturers of textiles and apparel. Fabric sound affects the sensorial comfort of clothing [ I ]. Because consumers are interested in the sensorial qualities of fabrics, auditory-sensible textile products are expected to broaden the market now more than at any other time. The tactile and visual aesthetic performance of textiles is already commonly understood, but auditory-sensible textiles are in the early stages of development and production. There have been only a few reports on fabric sound: Fukuhara reported that two edges of a micro-slit in a trilobal-shaped cross section of a polyester fiber imitate silk-scrooping [4]. To develop textile products with auditory comfort for the sensory satisfaction of consumers, however, the physical characteristics of fabrics that can be measured should be identified for application in the manufacturing process. In previous studies [2, 31, we discussed the relationship between the physical characteristics of fabric sounds and the mechanical properties of the fabrics. In addition, we observed some physiological responses to the rubbing sounds of several fabrics, including silk and polyester, in order to determine the relationship between objective physical measurements and human physiological attributes [13]. An approach by Pollard [9] proposed that complex sounds such as music may be determined by following three levels of sound analysis-physical, psycho-physical, and feature analysis. According to this suggestion, fabric sound can be, subjected to psycho-physical and feature analysis as well as physical analysis, indicating the usefulness of h...

Colors from naturally dyed fabrics recently have attracted both consumers and manufacturers in fashion markets. Even though color attributes of the fabrics have been partially observed in some literature, a data base of colors for natural colorants in fabrics needs to be established and the colors to be characterized according to systematic color notations and tones in order to relate the traditional natural colors to contemporary color communication systems. Therefore, a study was performed to investigate color characteristics for a given large set of natural colorants-dyed fabrics based on the Munsell color notations, to analyze their tones with relation to the notation such as hue, value, and chroma, and finally to identify the effects of mordanting, an important coloring auxiliary, on the colorimetric properties of the fabrics. As a result, the dominant hue for a total of 350 naturally dyed fabrics was yellowish families followed by reddish and purplish ones in the Munsell notation owing to the use of leaves and plant as usual natural dyestuff, which confirms the limit of color hues of the fabrics. Color value for most of naturally dyed fabrics was generally higher whereas the chroma was lower, which means that most of colors for naturally dyed fabrics tended to be bright and weak shaded. Grayish, light grayish, and soft tones were the main tones of natural colorant-dyed fabrics. All of hue, value, and chroma were found as being influenced by mordanting in that more particularly; iron mordanting was likely to cause the decrease of both value and chroma for most of naturally dyed fabrics. These results could provide a systematic color data for naturally dyed fashion fabrics and suggest a future direction of color development for them.

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This paper studies the causal relationship between Bitcoin and other investment assets. We first test Granger causality and then calculate transfer entropy as an information-theoretic approach. Unlike the Granger causality test, we discover that transfer entropy clearly identifies causal interdependency between Bitcoin and other assets, including gold, stocks, and the U.S. dollar. However, for symbolic transfer entropy, the dynamic rise–fall pattern in return series shows an asymmetric information flow from other assets to Bitcoin. Our results imply that the Bitcoin market actively interacts with major asset markets, and its long-term equilibrium, as a nascent market, gradually synchronizes with that of other investment assets.

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