1,720,991 research outputs found
DYVERSE: From formal verification to biologically-inspired real-time self-organizing systems
No full textTo predict the future of scientific thought and technological advance is a challenging venture, but it seems probable that progress lies in the multi-disciplinary approach. Multi-disciplinary research is a manner of sharing ideas across specialized areas in order to find answers to new technological challenges. The complexity of today’s technological applications means that automated and semiautomated processes have become proportionately more complicated. With consumers demanding more from automated services, the necessity for safety-critical and resilient systems – that is, systems capable of preserving stability and recovering from shock – becomes more pressing. The challenge is to accurately test the performance of complex systems, and, if necessary, to modify their behavior to meet desired specifications. But complexity is not only on the outside. The most safety-critical, resilient, and robust system of all is the human body. Could the lessons learned in engineering be applied to healthcare? Surprisingly, from the mathematical perspective, there are common features and dynamical behaviors in the synchronization of swarm satellites and, for example, the self-organization of cells in living organisms. Behind the surface appearance of each system are underlying patterns and points of similarity. Typically, they are highly nonlinear systems, and combine continuous and discrete, smooth and abrupt dynamics. Their combined dynamics can be interpreted as a hybrid dynamical system. DYVERSE is a computational-dynamical framework for the modeling, analysis and control of complex control systems under the framework of hybrid systems, and stands for the DYnamically-driven VERification of Systems with Energy considerations. DYVERSE methodology aims to bring together formal computational tools, dynamical systems theory and control engineering methodologies to advance the understanding of systems interconnected in a non-regular and nontrivial manner
DYVERSE: From formal verification to biologically-inspired real-time self-organizing systems
No full textTo predict the future of scientific thought and technological advance is a challenging venture, but it seems probable that progress lies in the multi-disciplinary approach. Multi-disciplinary research is a manner of sharing ideas across specialized areas in order to find answers to new technological challenges. The complexity of today’s technological applications means that automated and semiautomated processes have become proportionately more complicated. With consumers demanding more from automated services, the necessity for safety-critical and resilient systems – that is, systems capable of preserving stability and recovering from shock – becomes more pressing. The challenge is to accurately test the performance of complex systems, and, if necessary, to modify their behavior to meet desired specifications. But complexity is not only on the outside. The most safety-critical, resilient, and robust system of all is the human body. Could the lessons learned in engineering be applied to healthcare? Surprisingly, from the mathematical perspective, there are common features and dynamical behaviors in the synchronization of swarm satellites and, for example, the self-organization of cells in living organisms. Behind the surface appearance of each system are underlying patterns and points of similarity. Typically, they are highly nonlinear systems, and combine continuous and discrete, smooth and abrupt dynamics. Their combined dynamics can be interpreted as a hybrid dynamical system. DYVERSE is a computational-dynamical framework for the modeling, analysis and control of complex control systems under the framework of hybrid systems, and stands for the DYnamically-driven VERification of Systems with Energy considerations. DYVERSE methodology aims to bring together formal computational tools, dynamical systems theory and control engineering methodologies to advance the understanding of systems interconnected in a non-regular and nontrivial manner
Synergistic Sharing of Data and Tools to Enable Team Science
Get PDFOur goals are to compare and contrast examples of workflow efforts in the context of team science that have been heavily funded by governments (i.e. infectious disease genomics) with efforts that have been supported more by enthusiastic individual scientists (i.e., paleontology). We present lessons learned from these examples in terms of interoperability, agility, data sharing, metrics for scientific impact, and openness. In conclusion, we make suggestions for progress in synergistic sharing to enable team scienc
Synergistic sharing of data and tools to enable team science
Get PDFOur goals are to compare and contrast examples of workflow efforts in the context of team science that have been heavily funded by governments (i.e., infectious disease genomics) with efforts that have been supported more by enthusiastic individual scientists (i.e., paleontology). We present lessons learned from these examples in terms of interoperability, agility, data sharing, metrics for scientific impact, and openness. In conclusion, we make suggestions for progress in synergistic sharing to enable team science
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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