Other miscellaneous projects

Project members: T Byrum, A Swain, B J Klein and W F Fagan

Status: Publicly available as a CRAN package

Website: https://cran.r-project.org/web/packages/einet/index.html

Project members: J Demers, A Swain, T Byrum, S Bewick and W F Fagan

Status: Publicly available as a CRAN package

Website: https://cran.r-project.org/web/packages/mosqcontrol/index.html

Project members: Levi Fussell, Kirtus Leyba, Jessica A Lee, and Anshuman Swain

Status: Publicly available as a github repository; This project came out of a working group as a part of the Santa Fe Institute (SFI) CSSS 2019

Website: github.com/anshuman21111/htg_robotics

Algorithms that implement information exchange and the optimization of computational processes find a particularly interesting use case in distributed, asynchronous systems with constraints on communication and computational capabilities. There are many instances where such challenges must be addressed, such as the consensus of values in networks with tenuous or even adversarial connections, or the coverage of an environment by mobile autonomous vehicles. It has been shown that many distributed tasks can be unified as the optimization of a specialized form of a more fundamental cost function.

The coordination and collective behavior of simplistic robotic agents comes with the potential for robust, affordable, and adaptable systems. These challenges fall under the active research field of swarm robotics. Past work has shown that probabilistic models can be adapted to useful control schemes. For example, a swarm might effectively transport objects larger than any single member. While not generally thought of as swarm robots, the related self-organizing particle systems have been shown to effectively optimize various cost functions to achieve a desired behavior even in completely distributed and asynchronous settings. Such behaviors include but are not limited to compression and expansion, leader election, and universal coating.

These previous approaches are well suited to individual tasks, or being abstracted to larger related task classes. The design of an ideal system of a quickly adapting and robust robotic swarm that generalizes to multiple task classes is still a seemingly daunting problem. For some tasks, tools have been developed to adapt the behavior of the swarm to be specialized for specific environments. The ant inspired central-place foraging algorithm (CPFA) has been evolved with a genetic algorithm (GA) such that a robotic swarm performs with parameters suited to a specific environment, resource distribution, or environmental noise level. The process of evolving entirely new behaviors instead of parameters of those behaviors is a difficult challenge, and whether such a method can be successful is still an open question. Biology-inspired optimization of machine behavior has been applied to other systems outside of robotics. Neural networks have successfully been evolved using genetic algorithms in the well-known NEAT project. Furthermore, it has been shown that neural topologies could be effectively evolved in an approach that is agnostic to the weights of the neural networks. Genetic algorithms that operate in a structured space have had success in certain problem cases.  A major challenge in this project is to design an effective evolutionary algorithm that operates in real-time and accounts for the distributed nature of the robot team.

This project investigates the question of how a robot population with limited computational and communication capabilities can simultaneously optimize their individual performance and share effective behaviors with other robots. This challenge has many applications in robotics, such as pattern formation in swarms, rendezvous algorithms in localization and mapping, and convergence and consensus algorithms in distributed and noisy environments. We look to biology for inspiration to design a system that has several potentially useful traits. The robots are behaviorally heterogeneous, opening up the possibility of implicit task allocation in multi-task problems. The robots also optimize themselves via simulated evolution. Our approach must account for the fact that the robot population does not grow or decrease, which are important tools in biological evolution. Additionally, the system has redundancies because genetic patterns are mixed into the population, which may improve redundancy to robot failures or sensor error. In this work, we seek to explore such scenarios using inspiration from the biological phenomenon of horizontal gene transfer. Horizontal gene transfer (HGT) refers to the sharing of genetic material between organisms that are not in a parent–offspring relationship (where transmission of genes from parent to offspring is termed "vertical transmission").

Project members: Collin Schwantes, Jeff Demers, Sharon Bewick, Anshuman Swain, and William F Fagan

Status: Web app online

Website: collin-schwantes.shinyapps.io/MosquitoApp/

This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling timeperiod, and the optimal time to apply a treatment or multiple treatments.

Usable app below. Link to application: https://collin-schwantes.shinyapps.io/MosquitoApp/

Project members: Merveille Koissi Savi, Bhartendu Pandey, Anshuman Swain, Jeongki Lim

Status: Work in progress; This project came out of a working group as a part of the Santa Fe Institute (SFI) CSSS 2019

In the last two decades, tremendous efforts have been made by governments and private investors in West African (WA) endemic regions to reduce malaria incidence rate. Due to the high rates of urbanization in the region and known abstract associations between urbanization and malaria disease epidemiology, we suspect a change in the disease pattern. However, the association between urbanization and the spatio-temporal pattern of malaria is insufficiently documented.  Ghana is located in the endemic WA region and is rapidly urbanizing. This study aims to assess the influence of urbanization on malaria incidences in Ghana and to inform current and future decision-making. We used self-reported malaria cases time-series dataset (2015-2018)  from the District Health Information Management System aggregated by sex and age groups, at the district level. We then applied a series of aspatial and spatial quantitative analysis methods on the dataset. Our results show significant heterogeneity in malaria incidences across time and space. We find that the number of malaria cases is increasing by an average rate of 3,061 incidences per month. Our results show that for each district, self-reported cases are highest for children aged under-five for most districts in the country. In contrast, we find that in large urban centers such as Kumasi and Greater Accra Metropolitan Region, females aged between 20 and 34 had the highest cases. Our results show a statistically significant correlation between degree of urbanization, measured using satellite and census data, and self-reported total cases, and total cases for age group 20 to 34. Additionally, we find that in urban areas inter-age and inter-age-sex disparities in self-reported cases are lower. This points to the greater efficiency of urban areas in serving health care needs of the population. In contrast, as the population of Ghana urbanizes, it also suggests that healthcare systems in urban areas may be more stressed in the future unless urban healthcare infrastructure also expands in the future. In summary, our study stresses that cities in Ghana will need to adapt to the changing social and physical environments to address the increasing complexities in malaria disease dynamics.