Recent advances in AI-based video synthesis and character generation technologies have opened new possibilities for content production. However, as AI-generated humanlike characters become increasingly realistic, they often evoke discomfort and rejection—a phenomenon known as the "uncanny valley." While technical realism has progressed rapidly, it alone does not guarantee emotional engagement or viewer acceptance. This study explores how emotional design, particularly as conceptualized by Donald Norman in visceral, behavioral, and reflective dimensions, serves as a strategic response to the uncanny valley in AI-generated content. Through case analyses—such as anthropomorphized animal shorts, interactive AI chatbots, and the reanimation of historical figures—this paper demonstrates how emotional design fosters trust, immersion, and affective resonance. Furthermore, it discusses the ethical implications of emotional manipulation and authenticity in AI-human interactions. By examining how emotional design restructures user perception beyond visual fidelity, this study offers a reinterpretation of the uncanny valley from an affective and aesthetic perspective.

We present a 1.1mm emission map of the OMC1 region observed with AzTEC, a new large-format array composed of 144 silicon-nitride micromesh bolometers, that was in use at the James Clerk Maxwell Telescope (JCMT). These AzTEC observations reveal dozens of cloud cores and a tail of filaments in a manner that is almost identical to the submillimeter continuum emission of the entire OMC1 region at 450 and 850 μm. We perform Fourier analysis of the image with a modified periodogram and the density power spectrum, which provides the distribution of the length scale of the structures, is determined. The expected value of the periodogram converges to the resulting power spectrum in the mean squared sense. The present analysis reveals that the power spectrum steepens at relatively smaller scales. At larger scales, the spectrum flattens and the power law becomes shallower. The power spectra of the 1.1mm emission show clear deviations from a single power law. We find that at least three components of power law might be fitted to the calculated power spectrum of the 1.1mm emission. The slope of the best fit power law, γ≈−2.7 is similar to those values found in numerical simulations. The effects of beam size and the noise spectrum on the shape and slope of the power spectrum are also included in the present analysis. The slope of the power law changes significantly at higher spatial frequency as the beam size increases.

The purpose of the present study is to investigate what the memory representation of L2 text is like based on the Causal Network Model. In order to do that, 8 stories were read in English by Korean students and recalled in Korean. Their recall was analysed in terms of the number of causal connections each sentence has as specified in the model. And then it was compared with the results of Kim (2001) where Korean students read and recalled the same stories in Korean. The overall amount of recall was not different between L1 and L2 texts, but the pattern of recall showed differences in terms of the causal structure proposed by Causal Network Model. While the recall of L1 text was nicely accounted for by the number of causal connections specified in the model, the recall of individual goal statements in L2 text did not reflect the causal structure. Interesting was the finding that the more important goal among the two goal statements was recalled better for L2 than for L1 text.