Understanding Emergent Dynamism of Covid-19 Pandemic in a City.

Predicting the evolution of a pandemic requires precise understanding of the pathogen and disease progression, the susceptible population group, means of transmission, and possible control mechanisms. It has been a significant challenge as Covid-19 virus (SARS-CoV-2 family) is not well understood yet; the entire human population is susceptible, and the virus transmits easily through airborne particles. Given its size and connectedness, it is not feasible to test the entire population and to isolate the infected individuals. Moreover, rapid and continuous mutation of virus open up the possibility of reinfection. As a result, the evolution of pandemic is not uniform and in-step throughout the world but is significantly influenced by local characteristics pertaining to people, places, dominant virus strain, extent of vaccination, and adherence to pandemic control interventions. Traditional macro-modelling techniques, such as variations of SEIR models, provide only a coarse-grained, 'lumped up' understanding of the pandemic which is not enough for exploring and understanding possible fine-grained factors that are effective for controlling the Covid-19 pandemic. This paper explores the problem space from a system theoretic perspective and presents a fine-grained city digital twin as an in-silico experimentation aid to understand the complex interplay of factors that influence infection spread and also help in controlling the Covid-19 pandemic. Our focus is not to speculate the possibility of the next wave or how the next wave may look like. Instead, we systematically seek answers to questions such as: what are indicators should we consider for a future wave? What are the parameters that may influence those indicators? When and why should they be tweaked (in terms of interventions) to control unacceptable situations? We validate our approach on the second and third waves of Covid-19 pandemic in Pune city.

An Agent-Based Digital Twin for Exploring Localized Non-pharmaceutical Interventions to Control COVID-19 Pandemic.

The COVID-19 epidemic created, at the time of writing the paper, highly unusual and uncertain socio-economic conditions. The world economy was severely impacted and business-as-usual activities severely disrupted. The situation presented the necessity to make a trade-off between individual health and safety on one hand and socio-economic progress on the other. Based on the current understanding of the epidemiological characteristics of COVID-19, a broad set of control measures has emerged along dimensions such as restricting people's movements, high-volume testing, contract tracing, use of face masks, and enforcement of social-distancing. However, these interventions have their own limitations and varying level of efficacy depending on factors such as the population density and the socio-economic characteristics of the area. To help tailor the intervention, we develop a configurable, fine-grained agent-based simulation model that serves as a virtual representation, i.e., a digital twin of a diverse and heterogeneous area such as a city. In this paper, to illustrate our techniques, we focus our attention on the Indian city of Pune in the western state of Maharashtra. We use the digital twin to simulate various what-if scenarios of interest to (1) predict the spread of the virus; (2) understand the effectiveness of candidate interventions; and (3) predict the consequences of introduction of interventions possibly leading to trade-offs between public health, citizen comfort, and economy. Our model is configured for the specific city of interest and used as an in-silico experimentation aid to predict the trajectory of active infections, mortality rate, load on hospital, and quarantine facility centers for the candidate interventions. The key contributions of this paper are: (1) a novel agent-based model that seamlessly captures people, place, and movement characteristics of the city, COVID-19 virus characteristics, and primitive set of candidate interventions, and (2) a simulation-driven approach to determine the exact intervention that needs to be applied under a given set of circumstances. Although the analysis presented in the paper is highly specific to COVID-19, our tools are generic enough to serve as a template for modeling the impact of future pandemics and formulating bespoke intervention strategies.

Flight mechanics of a tailless articulated wing aircraft.

This paper investigates the flight mechanics of a micro aerial vehicle without a vertical tail in an effort to reverse-engineer the agility of avian flight. The key to stability and control of such a tailless aircraft lies in the ability to control the incidence angles and dihedral angles of both wings independently. The dihedral angles can be varied symmetrically on both wings to control aircraft speed independently of the angle of attack and flight path angle, while asymmetric dihedral can be used to control yaw in the absence of a vertical stabilizer. It is shown that wing dihedral angles alone can effectively regulate sideslip during rapid turns and generate a wide range of equilibrium turn rates while maintaining a constant flight speed and regulating sideslip. Numerical continuation and bifurcation analysis are used to compute trim states and assess their stability. This paper lays the foundation for design and stability analysis of a flapping wing aircraft that can switch rapidly from flapping to gliding flight for agile manoeuvring in a constrained environment.

Synthesis of robust tunable oscillators using mitogen activated protein kinase cascades.

Mitogen activated protein kinase (MAPK) cascade is evolutionally preserved in all eukaryotic cells, and regulates various cellular activities such as gene expression, mitosis, differentiation, and apoptosis. Recently, Bashor et al. have shown that Ste5 scaffold protein can be used to reshape the MAPK cascade through engineered feedback loops, and have used heuristic tuning mechanisms to synthesize the feedback. A problem of interest is to determine whether information regarding the underlying biochemical reactions can be used to synthesize robust feedback that will ensure that the resultant circuit has the desired properties. In this paper, we consider the problem of engineering feedback in MAPK cascade to synthesize an oscillator of the desired frequency. Our approach builds on the MAPK cascade model derived by Chikarmane et al. who have exploited the existence of a Hopf bifurcation point in the Markevich model of the MAPK cascade to synthesize the exciting kinase as a function of the doubly phosphorylated protein. We show how the [Formula: see text]-control theory can be used for a robust synthesis of the oscillator and present the simulation results.