Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer’s disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer’s Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 month, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in the both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction.

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This study investigates the diffusion of mobile telecommunications in Korea. The aim is to compare the performance of logistic model of diffusion with that of a time series autoregressive moving average model and to identify factors that determine the diffusion adoption process. Empirical results are based on annual data on cellular mobile subscribers covering 1984 to 2002. Results suggest that, the diffusion speed increases with per capita GDP, but it decreases with the number of main telephone lines in operation.

In a bid to reduce greenhouse gas emissions, several countries worldwide are implementing policies to promote electric vehicles (EVs). However, contrary to expectations, the diffusion speed of EVs has been rather slow in South Korea. This study analyzes consumer preferences for the technological and environmental attributes of EVs and derives policy and environmental implications to promote market diffusion of EVs in South Korea. We conduct a choice‐based conjoint survey of 1,008 consumers in South Korea and estimate the consumer utility function using a mixed logit model considering consumer heterogeneity. Based on the consumer utility function, we analyze consumers' willingness‐to‐pay (WTP) for EV attributes such as driving range, charging method, charging time, autonomous driving function, carbon dioxide (CO2) reduction rate, and purchase price. The results indicate that the current low acceptance of EVs is due to their relatively high price and lack of a battery charging technology that satisfies consumers' expectations of the charging method and time. One interesting finding is that Korean consumers have a relatively higher WTP for the CO2 reduction rate of EVs than consumers in other countries; however, they do not consider CO2 reduction over other technological attributes when choosing EVs. This implies that the rate of CO2 reduction of EVs is not an important factor for South Korean consumers when buying EVs. We also calculate the effect of CO2 reduction with the market penetration of EVs and find that CO2 reduction through the diffusion of EVs depends on the country's electricity generation mix.