An illustration of a pictorial simulation of the immunization protocols.

<p>Panel A displays an artificial contact structure where each horizontal line represents an individual. The circles and vertical lines indicate the contacts. There are two regions, separated by half of the sampling time, one for learning (experience) and one for disease spreading. Panel B shows an example of a spreading process with 100% chance of contagion per contact, no recovery and no vaccination. Red lines represent infected individuals. In Panels C and D we see the same spreading event as in (B), but now, one individual is vaccinated by the <i>Recent</i> (C) or <i>Weight</i> (D) strategies. The ego indicates the vertex selected at random in the immunization protocol and the dotted line, its selected neighbor according to <i>Recent</i> or <i>Weight</i> strategy.</p

Evaluating the performance of the vaccination strategies for different types of temporal correlations.

<p>In A and B, we illustrate the models that encode the different temporal contact structures. In the varying activity model (A), the first contact along an edge happens at time <i>t_s</i> after the beginning of the simulation and then subsequent contacts happen with a time interval <i>t_s</i>. In the other, partner turnover, model (B), an edge becomes active with uniform probability in time the interval [0,<i>T</i>−<i>ν</i>]. The edge is active for <i>ν</i> time steps with one contact per time step. Panels C and D show the worst-case scenario, Ω, and panels E and F show the average outbreak sizes in the SIS model. The networks used in C and E follow the temporal profile shown in panel A; panels D and F follow the profile illustrated in panel B. The underlying network topology is the Erdős-Rényi model, which has a minimum of structural bias.</p

The performance of the <i>Recent</i> and <i>Weight</i> strategies relative to the <i>NV</i> method for a dynamic, SIS-type disease simulation.

<p>The performance measure in this case is the average outbreak size ω (total number of infected individuals) in a SIS simulation with a per-contact transmission probability λ = 0.25 and a duration δ of the infected stage of three weeks. Just like <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036439#pone-0036439-g002" target="_blank">Fig. 2</a>, the vaccination is more efficient, relative to <i>NV</i>, the lower Δω is. The error bars correspond to the standard error calculated over all unvaccinated vertices as infection sources and 1000 runs of the vaccination and SIS simulation per source.</p

Degree distributions of the empirical datasets.

<p>In panels A–D, we plot the probability density <i>p</i> as a function of degree <i>k</i>. We plot results both for the accumulated network of all contacts and averages of three networks of ongoing contacts (defined by all edges that, at a certain time <i>t′</i>, a contact over all edges have happened and will happen again). We choose <i>t′</i> as when a quarter, half and three quarter have happened. In panel E, we show the values of two types of temporal statistics of the datasets—the persistence (which separates the e-mail data from the rest) and burstiness.</p

The performance of the <i>Recent</i> and <i>Weight</i> strategies relative to the <i>NV</i> method.

<p>The performance measure Ω is the upper bound of the outbreak size, given the temporal contact structures, averaged over all infection sources. The yellow regions indicate an improvement on <i>NV</i> (the more negative values, the better). The different panels correspond to the four different datasets. The error bars indicate standard errors over the set of infection sources.</p

Porous Zr₆L₃ Metallocage with Synergetic Binding Centers for CO₂

Coordination-driven assembly has been widely successful in the synthesis of metallocages and used for many applications, such as catalysis. However, studies on CO₂ adsorption with metallocages have been rarely conducted, compared to other well-known cage-type materials, such as porous organic cages. In this study, a rational choice of ligand and metal led to the synthesis of a Zr₆L₃-type metallocage, exhibiting exceptional CO₂ adsorption properties. CO₂ adsorption experiments revealed that the metallocage shows highly selective adsorption of CO₂ over N₂ with high CO₂ binding energy. Density functional theory calculations uncovered the origin of this exceptional affinity for CO₂ over N₂

Porous Zr₆L₃ Metallocage with Synergetic Binding Centers for CO₂

Coordination-driven assembly has been widely successful in the synthesis of metallocages and used for many applications, such as catalysis. However, studies on CO₂ adsorption with metallocages have been rarely conducted, compared to other well-known cage-type materials, such as porous organic cages. In this study, a rational choice of ligand and metal led to the synthesis of a Zr₆L₃-type metallocage, exhibiting exceptional CO₂ adsorption properties. CO₂ adsorption experiments revealed that the metallocage shows highly selective adsorption of CO₂ over N₂ with high CO₂ binding energy. Density functional theory calculations uncovered the origin of this exceptional affinity for CO₂ over N₂

Porous Zr₆L₃ Metallocage with Synergetic Binding Centers for CO₂

Coordination-driven assembly has been widely successful in the synthesis of metallocages and used for many applications, such as catalysis. However, studies on CO₂ adsorption with metallocages have been rarely conducted, compared to other well-known cage-type materials, such as porous organic cages. In this study, a rational choice of ligand and metal led to the synthesis of a Zr₆L₃-type metallocage, exhibiting exceptional CO₂ adsorption properties. CO₂ adsorption experiments revealed that the metallocage shows highly selective adsorption of CO₂ over N₂ with high CO₂ binding energy. Density functional theory calculations uncovered the origin of this exceptional affinity for CO₂ over N₂

Initial conditions and interaction rules for agents in the model according to their personality types.

<p>Note: C—cooperate, D—defect, h is the probability of cooperating (for personality types 1,4,5,6) for d days; percentages denote probabilities of respective changes.</p><p>Initial conditions and interaction rules for agents in the model according to their personality types.</p

Correlations between the cooperation index and socio-economic indicators (N = 22).

<p>Note: * p<.05. The number of countries was 21 for variables 4–10, as the data were not available for Algeria.</p><p>Correlations between the cooperation index and socio-economic indicators (N = 22).</p