Digital Divide and Growth Gap: A Cumulative Relationship

IT, growth gap, cumulative relationship

Relationships of self-identified cold tolerance and cold-induced vasodilatation in the finger

Thermal environments in daily life, such as occupational cold exposure and the use of heating facilities and warm clothing, affect acclimatization to both cold and heat. Also, cold tolerance can be cognized by self-identified evaluation. Thermal life-style during daily life might be one of the factors which affect cold-induced vasodilatation (CIVD) when different degrees of thermal stimuli are considered. Therefore, this study investigated whether or not CIVD response is related to self-identified cold and heat tolerances which is attributable to thermal life-style

Commuting Toeplitz operators on the Segal–Bargmann space

AbstractConsider two Toeplitz operators Tg, Tf on the Segal–Bargmann space over the complex plane. Let us assume that g is a radial function and both operators commute. Under certain growth condition at infinity of f and g we show that f must be radial, as well. We give a counterexample of this fact in case of bounded Toeplitz operators but a fast growing radial symbol g. In this case the vanishing commutator [Tg,Tf]=0 does not imply the radial dependence of f. Finally, we consider Toeplitz operators on the Segal–Bargmann space over Cn and n>1, where the commuting property of Toeplitz operators can be realized more easily

Long-Term Deflection Prediction from Computer Vision-Measured Data History for High-Speed Railway Bridges

Management of the vertical long-term deflection of a high-speed railway bridge is a crucial factor to guarantee traffic safety and passenger comfort. Therefore, there have been efforts to predict the vertical deflection of a railway bridge based on physics-based models representing various influential factors to vertical deflection such as concrete creep and shrinkage. However, it is not an easy task because the vertical deflection of a railway bridge generally involves several sources of uncertainty. This paper proposes a probabilistic method that employs a Gaussian process to construct a model to predict the vertical deflection of a railway bridge based on actual vision-based measurement and temperature. To deal with the sources of uncertainty which may cause prediction errors, a Gaussian process is modeled with multiple kernels and hyperparameters. Once the hyperparameters are identified through the Gaussian process regression using training data, the proposed method provides a 95% prediction interval as well as a predictive mean about the vertical deflection of the bridge. The proposed method is applied to an arch bridge under operation for high-speed trains in South Korea. The analysis results obtained from the proposed method show good agreement with the actual measurement data on the vertical deflection of the example bridge, and the prediction results can be utilized for decision-making on railway bridge maintenance