In this paper we present our experiences from a decade of plug-in development in the jABC framework, that is characterized by rigorous application of simplicity principles in two dimensions. First, the scope of the plug-in development is clearly defined: The jABC readily provides a sophisticated graphical user interface, which has been tailored to working with all kinds of directed graphs. Within this scope, plug-in development can deliberately focus on the actual functionality, like providing semantics to graphs, without having to deal with tedious but semantically irrelevant issues like user interfaces. Second, plug-in functionality can be itself conveniently modeled as a workflow within the jABC. We illustrate our approach by means of two mature plug-ins: Genesys, a plug-in that adds arbitrary code generator functionality to the jABC, and PROPHETS, a plug-in that eases user-level definition of workflows by completing model sketches by means of synthesis capabilities, so that they become complete and executable. We summarize our experience so far and derive general design principles for "lightweight plug-in development", that we are going to realize in the next generation of the jABC, which will be implemented itself as a collection of Eclipse plug-ins.

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Background: Carbon nanotubes (CNTs) are novel materials with considerable potential in many areas related to nanomedicine. However, a major limitation in the development of CNT-based therapeutic nanomaterials is a lack of reliable and reproducible data describing their chemical and structural composition. Knowledge of properties including purity, structural quality, dispersion state, and concentration are essential before CNTs see widespread use in in vitro and in vivo experiments. In this work, we describe the characterization of several commercially available and two in-house-produced CNT samples and discuss the physicochemical profiles that will support their use in nanomedicine. Methods: Eighteen single-walled and multi-walled CNT raw materials were characterized using established analytical techniques. Solid CNT powders were analyzed for purity and structural quality using thermogravimetric analysis and Raman spectroscopy. Extinction coefficients for each CNT sample were determined by ultraviolet-visible near infrared absorption spectroscopy. Standard curves for each CNT sample were generated in the 0-5 µg/mL concentration range for dispersions prepared in 1,2-dichlorobenzene. Results: Raman spectroscopy and thermogravimetric analysis results demonstrated that CNT purity and overall quality differed substantially between samples and manufacturer sources, and were not always in agreement with purity levels claimed by suppliers. Absorbance values for individual dispersions were found to have significant variation between individual single-walled CNTs and multi-walled CNTs and sources supplying the same type of CNT. Significant differences (P , 0.01) in extinction coefficients were observed between and within single-walled CNTs (24.9-53.1 mL·cm ). The results described here suggest a considerable role for impurities and structural inhomogeneities within individual CNT preparations and the resulting spectroscopic properties of their dispersions. Conclusion: Raw CNT materials require thorough analytical workup before they can be used as nanoexcipients. This applies especially to the determination of CNT purity, structure, and concentration. The results presented here clearly demonstrate that extinction coefficients must be determined for individual CNT preparations prior to their use.

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This paper presents research using simulation to explore the sensitivity of the network lifetime of a wireless sensor network (WSN) under the constraint to maintain a chosen coverage percentage when different aspects of the node model are included. Specifically, we begin with a simple sensor node that can transition between an Awake mode and a Sleep mode, dependent on meeting the coverage constraint with a simple battery model that expends energy when the node is in the Awake mode. We then compare this network behavior to when the battery model includes battery recovery behavior. We conclude that while the difference between the behaviors is small, they are significant enough to warrant the inclusion of a more sophisticated battery model when modeling wireless sensor networks.