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Power systems serve social communities that consist of residential, commercial, and industrial customers. As a result, the disaster resilience of a power system should account for social community resilience. The social behavior and psychological features of all stakeholders involved in a disaster influence the level of power system preparedness, mitigation, recovery, adaptability, and resilience. Hence, there is a need to consider the social community's effect on the power system and the dependence between them in determining a power system's resilient to human-made and natural hazards. The social community, such as a county, city, or state, consists of various stakeholders, e.g., social consumers, social prosumers, and utilities. In this paper, we develop a multi-dimensional output-oriented method to measure resilience. The three key ideas for measuring power system resilience are the multi-dimensionality, output-oriented, and degraded functionality aspects of the power system. To this end, we develop an artificial society based on neuroscience, social science, and psychological theories to model the behavior of consumers and prosumers and the interdependence between power system resilience, comsumer and prosumer well-being, and community capital. Both mental health and physical health are used as metrics of well-being, while the level of cooperation is used to measure community capital resilience. 

Experimental evidence has demonstrated the ability of transient pulses of electric fields to alter mammalian cell behavior. Strategies with these pulsed electric fields (PEFs) have been developed for clinical applications in cancer therapeutics, in-vivo decellularization, and tissue regeneration. Successful implementation of these strategies involve understanding how PEFs impact the cellular structures and, hence, cell behavior. The caveat, however, is that the PEF parameter space (i.e., comprising different pulse widths, amplitudes, number of pulses) is large, and design of experiments to explore all possible combinations of pulse parameters is prohibitive from a cost and time standpoint. In this study, a scaling law based on the Ising model is introduced to understand the impact of PEFs on the outer cell lipid membrane so that an understanding developed in one PEF pulse regime may be extended to another. Combining non-Markovian Monte Carlo techniques to determine density-of-states with a novel non-equilibrium thermodynamic framework based on the principle of steepest entropy ascent, the applicability of this scaling model to predict the behavior of both thermally quenched and electrically perturbed lipid membranes is demonstrated. A comparison of the predictions made by the steepest-entropy-ascent quantum thermodynamic (SEAQT) framework to experimental data is performed to validate the robustness of this computational methodology and the resulting scaling law