This poster presents a uniform method for translating an arbitrary nondeterministic nite automaton (NFA) into a deterministic mass action chemical reaction network (CRN) that simulates it. The CRN receives its input as a continuous time signal consisting of concentrations of chemical species that vary to represent the NFA's input string in a natural way. The CRN exploits the inherent parallelism of chemical kinetics to simulate the NFA in real time, and its size is linear in the number of states of the NFA. The compiler of Chen et al.

Introduction: Multi-scale methods are used to study phenomena that occur over multiple temporal or spatial scales and observations at different scales often require distinct experimental protocols. A problem in quantitative physiology is to develop the methodology for simultaneous modeling and assessment of data that arise from multiple, multi-scale experimental frameworks.

As more of the world's cities suffer from congestion, pollution, and energy exploitation, urban mobility remains one of the toughest challenges that cities face as the process of population growth and urbanization continues. So far, the most common approach for urban mobility characterization focuses on vehicle's spatial and temporal positions. However, urban mobility is a multidimensional characteristic of the city life, experienced as tangled layers of interconnected infrastructures and information networks around people and their needs in a spatio-­‐emporal frame.

A detailed analysis of the adverse events associated with the surgical robots (reported to the FDA) indicates that despite the increased number of robotic procedures and their greater utilization, the rate of adverse events has remained relatively steady over the last 14 years. Even though current surgical robots are designed with safety mechanisms in mind, in practice several significant challenges exist in enabling timely and accurate detection and mitigation of incidents during surgery.

The presentation materials cover results obtained for the two above-mentioned projects.  The poster presents material on an algebraic approach to modeling systems with both continuous and discrete behavior.  The framework is based on process algebra, which was developed for discrete systems, and features the development of a tree-based semantic model, called generalized synchronization trees, that uniformly captures a very general notions of time.

NoQueue is a real-time offloading framework design to remove server side queueing delay. It predicts workload execution time for each client using time series prediction. With the knowledge of offloading period and execution time, the planner is able to predict all future queueing events. The planner adjusts the future offloading time of clients to avoid queueing. As a result, it introduces jitter of offloading interval but improve end-to-end delay performance.

Starting from a database of 100’s of real patient electrogram records, we describe how to develop and use a large in-silico cohort consisting of 10,000+ heart models to improve the planning and execution of a clinical trial (CT) for implantable cardioverter defibrillators (ICDs). We illustrate our approach by applying it retrospectively to a real CT that compares two discrimination algorithms (DA) within ICDs for the detection of potentially fatal cardiac arrhythmias. The CT posited that one algorithm would be better than the other but the results of the trial