Learning skilled behaviors requires intensive practice over days, months, or years. Behavioral hallmarks of practice include exploratory variation and long-term improvements, both of which can be impacted by circadian processes. During weeks of vocal practice, the juvenile male zebra finch transforms highly variable and simple song into a stable and precise copy of an adult tutor's complex song. Song variability and performance in juvenile finches also exhibit circadian structure that could influence this long-term learning process. In fact, one influential study reported juvenile song regresses towards immature performance overnight, while another suggested a more complex pattern of overnight change. However, neither of these studies thoroughly examined how circadian patterns of variability may structure the production of more or less mature songs. Here we relate the circadian dynamics of song maturation to circadian patterns of song variation, leveraging a combination of data-driven approaches. In particular we analyze juvenile singing in learned feature space that supports both data-driven measures of song maturity and generative developmental models of song production. These models reveal that circadian fluctuations in variability lead to especially regressive morning variants even without overall overnight regression, and highlight the utility of data-driven generative models for untangling these contributions.

Airborne particulate matter (PM) is a well-known cause of cardiovascular disease and mortality and has also been associated with respiratory disease, low birth weight, lung cancer, and other adverse health outcomes. However, our understanding of the relative toxicity of specific PM types-mixtures with different proportions of particles of various sizes, shapes, and compositions-is less well understood. Observations from space, in conjunction with data from surface monitors and chemical transport models, offer a practical means of generating frequent, high spatial resolution maps of PM concentrations in major population centers around the world. The Multi-Angle Imager for Aerosols (MAIA) investigation, currently in development, is motivated by the scientific and societal benefits of linking PM types with adverse health outcomes and the value of satellite observations for addressing this objective.

This paper deals with the implementation of an online platform for Responsible Research and Innovation (RRI), which provides anticipatory and deliberative tools to address societal expectations with regard to resrarch and innovation and mobilises information technology tools for inclusive, responsive and sustainable research. In particular, we have developed a Forum, which is a Web-based application and service for stakeholders (members of the RRI community) to deliberate and discuss about RRI. Different interaction modalities are supported by the Forum, such as: `Caucus Process', Suggestion Board, Partnership Initiative, etc. A special attention is devoted to the Citizen Jury and Tacit Knowledge modules that represent innovative features of our Forum. The outcomes show that our online Forum represents an effective solution to address RRI issues by adopting e-Government and knowledge management approaches. This work was carried out as part of EU FP7 Responsibility project.

This paper is concerned with the practical application of control to a pneumatically actuated Stewart-Gough platform with 6 Degrees of Freedom (DOF). Two control approaches for motion control of the platform are presented using a classical control technique and a modern control technique. The control schemes experimental results are compared showing that either approach is suitable but with LQG giving slightly better performance.

Scientific research becomes increasingly reliant on collaborative effort among multiple institutions and interdisciplinary consortia, through sharing scientific experiments and data and collaboration on analysis of data and results. Besides information sharing, there is a growing need to create a framework to enable collaborative, cross-disciplinary research, which can facilitate a research community into a default mode of collaboration, including: i) enabling scientific researchers to dynamically interact with others, collaboratively author, annotate, review, comment on others' data, and discuss their research; ii) providing a dynamic collaboration environment by harnessing collective contributions from a group of researchers; and iii) facilitating broader participation of people into research projects. While traditional data management and integration systems put much focus on passively integrating existing data, the collaboration among data providers and users is quite limited. In this paper, we present a Web-based collaborative platform to dynamically integrate information for scientific research. Besides data integration, the system provides essential collaboration capabilities to boost user participation and collaboration. The system will not only enable users as active information contributors to bring significant new values to the data, but also provide an environment for scientific researchers to do collaborative research in a large research community.

Scientific research in many fields is increasingly a collaborative effort across multiple institutions and disciplines. Scientific researchers need not only an effective system to manage their data, results, and the experiments that generate the results, but also a platform to integrate, share and search these across multiple institutions. Therefore, researchers are able to reuse experiments, pool expertise and validate approaches. In this paper, we present Sci- Port, a system of experiment management and integration for collaborative scientific research. SciPort’s architecture uses i) a general transformation-based data model to represent and link experiment processes; ii) hierarchical data classification across multiple institutions according to research programs’ goals and organization; iii) metadatacentric representation that concisely captures the context of experiments; and iv) virtual data integration through centralized metadata integration. The system is built for open source, and the metadata-based representation and integration provides a unified framework and tool set to manage and share experiments for scientific research communities.