41 research outputs found
Mobile Human Ad Hoc Networks: A Communication Engineering Viewpoint on Interhuman Airborne Pathogen Transmission
Pathogens such as viruses and bacteria play a vital role in human life, since
they cause infectious diseases which can lead to epidemics. Recent coronavirus
disease 2019 epidemic has shown that taking effective prevention measures such
as wearing masks are important to reduce the human deaths and side effects of
the epidemic. It is therefore requisite to accurately model the spread of
infectious diseases whose one of the most crucial routes of transmission is
airborne transmission. The transmission models in the literature are proposed
independently from each other, at different scales and by the researchers from
various disciplines. Thus, there is a need to merge all these research
attempts. To this end, we propose a communication engineering approach that
melts different disciplines such as epidemiology, biology, medicine, and fluid
dynamics in the same pot to model airborne pathogen transmission among humans.
In this approach, we introduce the concept of mobile human ad hoc networks
(MoHANETs). This concept exploits the similarity of airborne
transmission-driven human groups with mobile ad hoc networks and uses molecular
communication as the enabling paradigm. The aim of this article is to present a
unified framework using communication engineering, and to highlight future
research directions for modeling the spread of infectious diseases among humans
through airborne pathogen transmission. In this article, we first review the
airborne pathogen transmission mechanisms. Then, the MoHANET is given with a
layered structure. In these layers, the infectious human emitting
pathogen-laden droplets through air and the exposed human to these droplets are
considered as the transmitter and receiver, respectively. Moreover, the
experimental methods for the proposed approach are reviewed and discussed.Comment: 7 pages, 6 figures, submitted to IEEE Communications Magazine Feature
Topic on Nano-Networking for Nano-, Micro-, and Macro-Scale Application
A Stochastic Biofilm Disruption Model based on Quorum Sensing Mimickers
Quorum sensing (QS) mimickers can be used as an effective tool to disrupt
biofilms which consist of communicating bacteria and extracellular polymeric
substances. In this paper, a stochastic biofilm disruption model based on the
usage of QS mimickers is proposed. A chemical reaction network (CRN) involving
four different states is employed to model the biological processes during the
biofilm formation and its disruption via QS mimickers. In addition, a
state-based stochastic simulation algorithm is proposed to simulate this CRN.
The proposed model is validated by the in vitro experimental results of
Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS
mimicker. Our results show that there is an uncertainty in state transitions
due to the effect of the randomness in the CRN. In addition to the QS
activation threshold, the presented work demonstrates that there are underlying
two more thresholds for the disruption of EPS and bacteria, which provides a
realistic modeling for biofilm disruption with QS mimickers.Comment: Accepted for publication in IEEE Transactions on Molecular,
Biological, and Multi-Scale Communication
Stochastic Modeling of Biofilm Formation with Bacterial Quorum Sensing
Bacteria generally live in complicated structures called biofilms, consisting
of communicating bacterial colonies and extracellular polymeric substance
(EPS). Since biofilms are related to detrimental effects such as infection or
antibiotic resistance in different settings, it is essential to model their
formation. In this paper, a stochastic model is proposed for biofilm formation,
using bacterial quorum sensing (QS). In this model, the biological processes in
the biofilm formation are modeled as a chemical reaction network which includes
bacterial reproduction, productions of autoinducer and EPS, and their
diffusion. The modified explicit tau-leap simulation algorithm is adapted based
on the two-state QS mechanism. Our approach is validated by using the
experimental results of IsoF bacteria for
autoinducer and bacteria concentration. It is also shown that the percentage of
EPS in the biofilm increases significantly after the state change in QS, while
it decreases before QS is activated. The presented work shows how the biofilm
growth can be modeled realistically by using the QS mechanism in stochastic
simulations of chemical reactions.Comment: Submitted to ICC 202
A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels
Airborne pathogen transmission mechanisms play a key role in the spread of
infectious diseases such as COVID-19. In this work, we propose a computational
fluid dynamics (CFD) approach to model and statistically characterize airborne
pathogen transmission via pathogen-laden particles in turbulent channels from a
molecular communication viewpoint. To this end, turbulent flows induced by
coughing and the turbulent dispersion of droplets and aerosols are modeled by
using the Reynolds-averaged Navier-Stokes equations coupled with the realizable
model and the discrete random walk model, respectively. Via
simulations realized by a CFD simulator, statistical data for the number of
received particles are obtained. These data are post-processed to obtain the
statistical characterization of the turbulent effect in the reception and to
derive the probability of infection. Our results reveal that the turbulence has
an irregular effect on the probability of infection, which shows itself by the
multi-modal distribution as a weighted sum of normal and Weibull distributions.
Furthermore, it is shown that the turbulent MC channel is characterized via
multi-modal, i.e., sum of weighted normal distributions, or stable
distributions, depending on the air velocity.Comment: Accepted to be published in IEEE Transactions on Molecular,
Biological and Multi-Scale Communication
Localization of a Passive Source with a Sensor Network based Experimental Molecular Communication Platform
In a practical molecular communication scenario such as monitoring air
pollutants released from an unknown source, it is essential to estimate the
location of the molecular transmitter (TX). This paper presents a novel Sensor
Network-based Localization Algorithm (SNCLA) for passive transmission by using
a novel experimental platform which mainly comprises a clustered sensor network
(SN) with sensor nodes and evaporating ethanol molecules as the passive
TX. In SNCLA, a Gaussian plume model is employed to derive the location
estimator. The parameters such as transmitted mass, wind velocity, detection
time, and actual concentration are calculated or estimated from the measured
signals via the SN to be employed as the input for the location estimator. The
numerical results show that the performance of SNCLA is better for stronger
winds in the medium. Our findings show that evaporated molecules do not
propagate homogeneously through the SN due to the presence of the wind. In
addition, our statistical analysis based on the measured experimental data
shows that the sensed signals by the SN have a log-normal distribution, while
the additive noise follows a Student's t-distribution in contrast to the
Gaussian assumption in the literature
Pathways for the Valorization of Animal and Human Waste to Biofuels, Sustainable Materials and Value-Added Chemicals
Human and animal waste, including waste products originating from human or animal digestive systems such as urine, feces, and animal manure, have constituted a nuisance to the environment. Inappropriate disposal and poor sanitation of human and animal waste often cause negative impacts on human health through contamination of the terrestrial environment, soil, and water bodies. Therefore, it is necessary to convert these wastes into useful resources to mitigate their adverse environmental effect. The present study provides an overview and research progress of different thermochemical and biological conversion pathways for the transformation of human- and animal-derived waste into valuable resources. The physicochemical properties of human and animal waste are meticulously discussed as well as nutrient recovery strategies. In addition, a bibliometric analysis is provided to identify the trends in research and knowledge gaps. The results reveal that the U.S.A, China and England are the dominant countries in the research areas related to resource recovery from human or animal waste. In addition, researchers from the University of Illinois, the University of California Davis, the Chinese Academy of Science and Zhejiang University are front runners in research related to these areas. Future research should be centred on developing technologies for the on-site recovery of resources, exploring integrated resource recovery pathways, and exploring different safe waste processing methods