SUMMARY: Effective visualization is important for knowledge discovery when analysing expression profile data. However, existing tools for visually integrating expression profile data with KEGG pathway maps lack extensive interactive visualization operations. KeggExp simultaneously presents the pathway map of one pathway, dendrogram and heatmap of the genes in the pathway and scatter map of one gene; and also provides interactive operations for highlighting specific genes on the pathway map, including differentially-expressed genes, co-expressed genes selected from the heatmap and user-input genes. With KeggExp, researchers, including those without programming skills, can take advantage of its interactive operations to determine key genes or pathways when analysing expression profile data. AVAILABILITY AND IMPLEMENTATION: Freely available at http://www.fgvis.com/expressvis/KeggExp/. Language: JavaScript, python; Libraries: D3.js, Rxjs, Angular, Django, Django rest frame work, Scipy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we design, analyze, and evaluate a novel automated container terminal (ACT), namely, the rail-shuttle-based ACT. To analyze the performance, one detailed simulation system is built based on parametric design ideas. Two different microscopic rail-shuttle-based ACT simulation models, in which either straddle carriers (SCs) or automated guided vehicles with lifting devices (lift-AGVs) are employed, are formulated. Simulation experiments are carried out to obtain a reasonable equipment configuration for the proposed ACT. The results indicate that the performance of conventional ACTs can be improved if equipped with rail shuttles, and the performance of the rail-shuttle-based ACT with SCs exceeds that with lift-AGVs.

With the flourishing of IoV(Internet of Vehicles) technology, location based services need to handle the positional coordinates streaming in continuously from large numbers of vehicles. Across the hundreds of thousands of kilometers of roads, with tens of millions of vehicles on them, it is a significant performance challenge to determine in real time where vehicles are; how quickly and where they are headed; and when, where, and how much congestion can be expected to build as a result. The high volume of data and the rate at which the mappings must be performed require high computational efficiency and avoidance of storage accesses where possible. This paper introduces a Hadoop based approach for handling such large volumes of information. The paper describes a couple of simple adjustments to methods available in JTS and Java AWT that provide efficient mapping between vehicle positions and road segments, shows the importance of secondary sort in achieving the needed computational throughput, and establishes the significant performance benefit to be achieved from in-memory processing. An evaluation using RAF, a lightweight in-memory computing framework, shows the mapping is 20X+ faster than Hadoop approach to achieve results for real-time operation.