Unimolecular Micelles from Layered Amphiphilic Dendrimer-Like Block Copolymers

In this report, we synthesized layered amphiphilic dendrimer-like block copolymers containing a polystyrene core and poly­(<i>p</i>-<i>tert</i>-butoxystyrene)/poly­(<i>p</i>-hydroxylstyrene) shell (coded G4-P<i>t</i>BOS/G4-PHOS). The synthetic method is easy involving anionic polymerization, epoxidation, ring-opening reaction and hydrolysis reaction. The hydrolyzed G4-P<i>t</i>BOS was soluble in alkaline water and behaved as unimolecular micelle, as demonstrated by the results of DLS, cryo- and normal TEM, and pyrene entrapping experiment. The stability of the unimolecular micelles was investigated via ζ-potential measurements

Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

<div><p>Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.</p></div

RBM Structure.

<p>RBM Structure.</p

BackPropagation Through Time for RNN.

<p>BackPropagation Through Time for RNN.</p

Comparison of traffic congestion prediction performance with different data aggregation levels.

<p>Comparison of traffic congestion prediction performance with different data aggregation levels.</p

Sensitivity analysis of congestion evolution prediction performance with various speed thresholds.

<p>Sensitivity analysis of congestion evolution prediction performance with various speed thresholds.</p

Predicted Network Congestion Evolution Patterns on May 09, 2014 with Varying Times of Day.

<p>(a) Spatial Distribution of Congestion from 5AM to 6AM; (b) Spatial Distribution of Congestion from 9AM to 10AM; (c) Spatial Distribution of Congestion from 5PM to 6PM; (d) Spatial Distribution of Congestion from 11PM to 12PM (Red line indicates congested traffic condition; green line indicated uncongested traffic condition).</p

Temporal distribution for number of congested links on May 9, 2014.

<p>Temporal distribution for number of congested links on May 9, 2014.</p

Link Parameters.

<p>Link Parameters.</p

State Space Neural Network Structure.

<p>State Space Neural Network Structure.</p