Sharif Digital Repository

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment―either experimentally or computationally―to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches,... 

This paper investigates the performance of epidemic routing in mobile social networks considering several communities which are frequently visited by nodes. To this end, a monolithic Stochastic Reward Net (SRN) is proposed to evaluate the delivery delay and the average number of transmissions under epidemic routing by considering skewed location visiting preferences. This model is not scalable enough, in terms of the number of nodes and frequently visited locations. In order to achieve higher scalability, the folding technique is applied to the monolithic model, and an approximate folded SRN is proposed to evaluate performance of epidemic routing. Discrete-event simulation is used to... 

We first propose a quantitative approach to detect high risk outbreaks of independent and coinfective SIR dynamics on three empirical networks: A school, a conference and a hospital contact network. This measurement is based on the k-means clustering method and identifies proper samples for calculating the mean outbreak size and the outbreak probability. Then we systematically study the impact of different temporal correlations on high risk outbreaks over the original and differently shuffled counterparts of each network. We observe that, on the one hand, in the coinfection process, randomization of the sequence of the events increases the mean outbreak size of high-risk cases. On the other... 

The novel coronavirus pandemic has rapidly spread around the world since December 2019. Various techniques have been applied in identification of SARS-CoV-2 or COVID-19 infection including computed tomography imaging, whole genome sequencing, and molecular methods such as reverse transcription polymerase chain reaction (RT-PCR). This review article discusses the diagnostic methods currently being deployed for the SARS-CoV-2 identification including optical biosensors and point-of-care diagnostics that are on the horizon. These innovative technologies may provide a more accurate, sensitive and rapid diagnosis of SARS-CoV-2 to manage the present novel coronavirus outbreak, and could be... 

The novel coronavirus pandemic has rapidly spread around the world since December 2019. Various techniques have been applied in identification of SARS-CoV-2 or COVID-19 infection including computed tomography imaging, whole genome sequencing, and molecular methods such as reverse transcription polymerase chain reaction (RT-PCR). This review article discusses the diagnostic methods currently being deployed for the SARS-CoV-2 identification including optical biosensors and point-of-care diagnostics that are on the horizon. These innovative technologies may provide a more accurate, sensitive and rapid diagnosis of SARS-CoV-2 to manage the present novel coronavirus outbreak, and could be... 

Recent progress in the areas of network science and control has shown a significant promise in understanding and analyzing epidemic processes. A well-known model to study epidemics processes used by both control and epidemiological research communities is the susceptible–infected–susceptible (SIS) dynamics to model the spread of disease/viruses over contact networks of infected and susceptible individuals. The SIS model has two metastable equilibria: one is called the endemic equilibrium and the other is known as the disease-free or healthy-state equilibrium. Control theory provides the tools to design control actions (allocating curing or vaccination resources) in order to achieve and... 

The disasters caused by epidemic outbreaks is different from other disasters due to two specific features: their long-term disruption and their increasing propagation. Not controlling such disasters brings about severe disruptions in the supply chains and communities and, thereby, irreparable losses will come into play. Coronavirus disease 2019 (COVID-19) is one of these disasters that has caused severe disruptions across the world and in many supply chains, particularly in the healthcare supply chain. Therefore, this paper, for the first time, develops a practical decision support system based on physicians' knowledge and fuzzy inference system (FIS) in order to help with the demand... 

In ecological systems, heterogeneous interactions between pathogens take place simultaneously. This occurs, for instance, when two pathogens cooperate, while at the same time, multiple strains of these pathogens co-circulate and compete. Notable examples include the cooperation of human immunodeficiency virus with antibiotic-resistant and susceptible strains of tuberculosis or some respiratory infections with Streptococcus pneumoniae strains. Models focusing on competition or cooperation separately fail to describe how these concurrent interactions shape the epidemiology of such diseases. We studied this problem considering two cooperating pathogens, where one pathogen is further structured... 

Motivated by security issues in networks, we study the problem of incentive mechanism design for dynamic resource allocation in a multi-agent networked system. Each strategic agent has a private security state which can be safe or unsafe and is only known to him. At every time, each agent faces security threats from outside as well as from his unsafe neighbors. Therefore, the agents’ states are correlated and have interdependent stochastic dynamics. Agents have interdependent valuations, as each agent’s instantaneous utility depends on his own security state as well as his neighbors’ security states. There is a network manager that can allocate a security resource to one agent at each time... 

In this paper, we introduce a general framework for coinfection as cooperative susceptible-infected-removed (SIR) dynamics. We first solve the SIR model analytically for two symmetric cooperative contagions [L. Chen, Europhys. Lett. 104, 50001 (2013)10.1209/0295-5075/104/50001] and then generalize and solve the model exactly in the symmetric scenarios for three and more cooperative contagions. We calculate the transition points and order parameters, i.e., the total number of infected hosts. We show that the behavior of the system does not change qualitatively with the inclusion of more diseases. We also show analytically that there is a saddle-node-like bifurcation for two cooperative SIR... 

In some applicable scenarios, such as community patrolling, mobile nodes are restricted to move only in their own communities. Exploiting the meetings of the nodes within the same community and the nodes within the neighboring communities, a delay tolerant network (DTN) can provide communication between any two nodes. In this paper, two analytical models based on stochastic reward nets (SRNs) are proposed to evaluate the performance of the epidemic content retrieval in such multi-community DTNs. Performance measures computed by the proposed models are the average retrieval delay and the average number of transmissions. The monolithic SRN model proposed in the first step is not scalable, in... 

Research in modeling epidemic spreading in static networks has reached maturity in recent years. On the contrary, disease spreading in dynamic networks can be considered as an important open issue. Hence, mapping dynamic interactions of crowds to a static network and then immunizing the resulting network is the subject of this paper. In this work, we analogize spreading of diseases in dynamic networks -based on dynamic interactions- to message delivery in opportunistic networks. Thus, different diseases which have different spreading behaviors could be simulated by specific routing protocols. Having used interactions among individuals in the utilized dataset, the resulting network is... 

In this paper, a nonlinear robust adaptive sliding mode control strategy is presented for the influenza epidemics in the presence of model uncertainties. The nonlinear epidemiological model of influenza with five state variables (the numbers of susceptible, exposed, infected, asymptomatic and recovered individuals) and two control inputs (vaccination and antiviral treatment) is considered. The objective of the proposed controller is decreasing the number of susceptible and infected humans to zero by tracking the desired scenarios. As a result of this decreasing, the number of exposed and asymptomatic individuals is also decreased and converged to the zero. Accordingly, it is shown that the... 

We study a dynamic mechanism design problem for a network of interdependent strategic agents with coupled dynamics. In contrast to the existing results for static settings, we present a dynamic mechanism that is incentive compatible, individually rational, budget balanced, and social welfare maximizing. We utilize the correlation among agents’ states over time, and determine a set of inference signals for all agents that enable us to design a set of incentive payments that internalize the effect of each agent on the overall network dynamic status, and thus, align each agent’s objective with the social objective. © 2017, ICST Institute for Computer Sciences, Social Informatics and... 

Nowadays, we are facing epidemic spreading in many different areas; examples are infection propagation, rumor spreading and computer viruses in computer networks. Finding a strategy to control and mitigate the spread of these epidemics is gaining much interest in recent researches. Due to limitation of immunization resources, it is important to establish a strategy for selecting nodes which has the most effect in mitigating epidemics. In this paper, we propose a new algorithm that minimizes the worst expected growth of an epidemic by reducing the size of the largest connected component of the underlying contact network. The proposed algorithm is applicable to any level of available resources... 

We study a system of ordinary differential equations which describes the metapopulation SIR model. The main target is to control the epidemic spreading using the structure of connections between cities. For this purpose, we formulate optimal control strategy that the control represents a drug treatment and Prevention strategies . Some methods are given in numerical example section such as control through spectral radius of movement matrix or optimal control through travelling between cities. Existence results for the optimal control are studied