Initial release of RCNA and CollaboratinoViz

<p>Network analysis of research collaborations based on UAMS's ORSP's grant database</p

Systematic Investigation of Isoindigo-Based Polymeric Field-Effect Transistors: Design Strategy and Impact of Polymer Symmetry and Backbone Curvature

Ten isoindigo-based polymers were synthesized, and their photophysical and electrochemical properties and device performances were systematically investigated. The HOMO levels of the polymers were tuned by introducing different donor units, yet all polymers exhibited <i>p</i>-type semiconducting properties. The hole mobilities of these polymers with centrosymmetric donor units exceeded 0.3 cm² V^–1 s^–1, and the maximum reached 1.06 cm² V^–1 s^–1. Because of their low-lying HOMO levels, these copolymers also showed good stability upon moisture. AFM and GIXD analyses revealed that polymers with different symmetry and backbone curvature were distinct in lamellar packing and crystallinity. DFT calculations were employed to help us propose the possible packing model. Based on these results, we propose a design strategy, called “molecular docking”, to understand the interpolymer π–π stacking. We also found that polymer symmetry and backbone curvature affect interchain “molecular docking” of isoindigo-based polymers in film, ultimately leading to different device performance. Finally, our design strategy maybe applicable to other reported systems, thus representing a new concept to design conjugated polymers for field-effect transistors

A visualization of collaboration recommendations.

<p>A visualization of collaboration recommendations.</p

Temporal evolution of an investigator in the research collaboration network at UAMS.

<p>Temporal evolution of an investigator in the research collaboration network at UAMS.</p

Visualizing “centrality leaders”: Figure 3 (a) demonstrates a visualization of the identified “centrality leaders” and their relative “importance” to the network based on UAMS’s 2012 research collaboration network; and Figure 3 (b) zooms in to one of the centrality leaders and shows her immediate collaborative relationships.

<p>Visualizing “centrality leaders”: <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111928#pone-0111928-g003" target="_blank">Figure 3</a> (a) demonstrates a visualization of the identified “centrality leaders” and their relative “importance” to the network based on UAMS’s 2012 research collaboration network; and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111928#pone-0111928-g003" target="_blank">Figure 3</a> (b) zooms in to one of the centrality leaders and shows her immediate collaborative relationships.</p

Using transparency to highlight areas of interests while preserving the context (e.g., a particular investigator’s direct collaborators).

<p>Using transparency to highlight areas of interests while preserving the context (e.g., a particular investigator’s direct collaborators).</p

Mining Twitter to Assess the Public Perception of the “Internet of Things”

<div><p>Social media analysis has shown tremendous potential to understand public's opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public’s attitude towards the IoT. As anticipated, our analysis indicates that the public's perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public.</p></div

Public's sentiment on leisure, social, health and money when discussing Internet of Things.

<p>The y-axis is the percent of all the words in the text.</p

The perplexity score continues to increase as the number of topics increases.

<p>The perplexity score continues to increase as the number of topics increases.</p

Trends of topics learned through a LDA model related to Internet of Things on Twitter since 2009.

<p>The y-axis is the probability distribution (between 0 and 1) of the topic of interest.</p