This project advances the scientific knowledge on design methods for improving the resilience of civil infrastructures to disruptions. To improve resilience, critical services in civil infrastructure sectors must utilize new diagnostic tools and control algorithms that ensure survivability in the presence of both security attacks and random faults, and also include the models of incentives of human decision makers in the design process.

In a team decision problem, a team of agents interacts with a system; each agent receives a measurement that is probabilistically related to the state of the system; each agent uses its measurement to choose an action; and the team incurs a cost that is a function of the state of the system and the actions of the agents; the objective is to design the rules that the agents use to choose their actions in order to minimize the expected cost. Despite their simplicity, team decision problems are a fundamental building block of decentralized control, and the

Wide-area management of terrestrial scale infrastructures often involves human operators, who are sandwiched between physical-world systems and cyber- assets.  These Management Coupled Cyber-Physical Infrastructures (MCCPIs) are subject to diverse threats that can propagate across network elements.  In this research effort, a layered network modeling paradigm for MCCPIs is developed, and threats to cyber, physical, and human assets are modeled at several resolutions.

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.

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.

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

Cyber-physical systems (CPS) have become increasingly prevalent in appli- cations including health care, energy, and transportation. The tight coupling between cyber and physical components of CPS implies that cyber attacks can degrade the safety, availability, and performance of physical components. The cyber components also introduce multiple entry points to the CPS, lowering the cost of attacks compared to purely physical systems.