Traffic density, congestion externalities, and urbanization in China

<p>Although there is an abundant regional literature analyzing traffic congestion, only a few studies have explored extending such analysis with spatial effects. This study uses a dynamic spatial Durbin model and city-level panel data for the period 2003–14 to investigate the spatial spillover effects of traffic congestion on urbanization in China. The results show that there is an inverted ‘U’-shaped relationship between urbanization and traffic density in local and neighbouring cities, and congestion effects have appeared. In the short and long run, the spatial effects of traffic congestion have become an important force restricting the effective promotion of urbanization in China.</p

Visualization 1.mp4

Visualization 1 for the total 80 remapped spectral videos

Visualization 2.mp4

Visualization 2 for the original 80 spatio-spectral gray subimages captured at video rate

Detectability of Features from Neuronal Responses to Natural Images and Random Stimuli

<div><p>(A) Probability distribution of feature contrast in a natural ensemble (or, equivalently, its matched random ensemble). For simplicity, only the positive side (feature contrast >0) is shown. Gray shading: feature contrasts near zero (<<i>T</i>₀, here <i>T</i>₀<i>=</i> 0.007, “feature absent”); black shading: high feature contrasts (><i>T</i>₁, here <i>T</i>₁ = 0.04, “feature present”).</p> <p>(B) Conditional probability distributions of responses evoked by natural images (upper) and random stimuli (lower). Solid lines: response distributions when the feature was present in stimulus (black shading in [A]); dashed lines: distributions when the feature was absent (gray shading in [A]).</p> <p>(C) Feature detectability in natural images versus that in matched random stimuli, for the same population of cells shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#pbio-0030342-g003" target="_blank">Figure 3</a>B. Detectability was measured as the percentage of trials in which stimuli were correctly classified as “feature present” or “feature absent” (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#s4" target="_blank">Materials and Methods</a>).</p></div

Feature Sensitivity of Complex Cells in Response to Natural Images and Random Stimuli

<div><p>(A) Contrast-response functions for both preferred features (insets above) of a complex cell. Curves: fits of data with quadratic functions.</p> <p>(B) Gain of contrast-response function (in spikes/s per unit feature contrast) for natural ensemble versus that for contrast-matched random ensemble. For this population of cells, the gain was significantly higher for the natural than for the random ensemble (<i>n</i> = 24, from 14 cells; <i>p</i> < 10⁻⁴, Wilcoxon signed rank test).</p></div

Media3.mp4

Visualization 3 for one of the original 80 spatio-spectral gray subimages captured at video rate

Matching of Feature Contrasts in Natural and Random Ensembles

<div><p>(A) Example images in the natural (upper row) and the random (lower row) ensembles, which were matched frame by frame for both global and feature contrasts.</p> <p>(B) Contrasts of a preferred feature of a complex cell (inset at center) in each frame of the natural (squares) and random (circles) ensembles in (A). F.C. denotes feature contrast.</p> <p>(C) Distributions of feature contrasts in the natural (left) and random (middle) ensembles, and the distribution of the difference in feature contrast between the two ensembles (right).</p></div

Difference in Feature Sensitivity between the Responses to Natural and Random Stimuli as a Function of <i>F</i>₁<i>/F</i>₀

<p>Each symbol represents data from one cell. For complex cells with two significant eigenvectors, the sensitivity difference was averaged between the two eigenvectors. Dashed line: linear fit.</p

Corrosion Behavior of Metallic Materials in Acidic-Functionalized Ionic Liquids

This paper describes the influence of temperature, water content, and anionic type of acidic-functionalized ionic liquids (ILs), 1-(4-sulfobutyl)-3-methylimidazolium hydrogen sulfate ([BsMIM]­[HSO₄]) and 1-(4-sulfobutyl)-3-methylimidazolium toluenesulfonate ([BsMIM]­[OTs]), on the corrosion behavior of Fe, Ni, and 304 stainless steel (304SS). Electrochemical methods including electrochemical impedance spectroscopy (EIS) and Tafel plots were used to investigate it. Also, scanning electron microscopy (SEM) was used to characterize the nature of the corrosion morphology. The obtained electrochemical results indicated that increasing temperature accelerates the corrosion, while decreasing IL concentration retards the corrosion. The corrosion process is controlled by charge transfer. Moreover, the bisulfate anion (HSO₄^–) has an effect on the corrosion rate more significantly than the <i>p</i>-toluenesulfonate anion (OTs^–) does. The SEM spectrum showed that the corrosion situation of Fe is more serious than Ni and 304SS performed in IL-based solutions, especially in [BsMIM]­[HSO₄]. Also, the protective layer formed on the 304SS surface is more uniform. On the basis of these consistent finds, the corrosion mechanism is assumed

Feature Sensitivity of Simple Cells in Response to Natural Images and Random Stimuli

<div><p>(A) Contrast-response function for the preferred feature (inset above) of a simple cell. Curves: fits of data with quadratic functions (for positive feature contrasts only).</p> <p>(B) Gain of contrast-response function, as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#pbio-0030342-g003" target="_blank">Figure 3</a>B (<i>n</i> = 14, from 14 cells).</p></div