Background: In Brazil, mathematical models for deriving estimates and projections of COVID-19 cases have been developed without data on asymptomatic SARS-CoV-2 infection. We estimated the seroprevalence of antibodies to SARS-CoV-2 among blood donors in the State of Rio de Janeiro. Methods: Data were collected on 2,857 blood donors from April 14 to 27, 2020. We report the crude prevalence of antibodies to SARS-CoV-2, the weighted prevalence by the total state population, and adjusted prevalence estimates for test sensitivity and specificity. To establish the correlates of SARS-CoV-2 prevalence, we used logistic regression models. The analysis included period and site of blood collection, sociodemographic characteristics, and place of residence. Results: The proportion of SARS-Cov-2 positive tests without any adjustment was 4.0% (95% CI 3.3-4.7%), and the weighted prevalence was 3.8% (95% CI 3.1-4.5%). Further adjustment by test sensitivity and specificity produced lower estimates, 3.6% (95% CI 2.7-4.4%) and 3.3% (95% CI 2.6-4.1%), respectively. The variable most significantly associated with the crude prevalence was the period of blood collection: the later the period, the higher the prevalence. Regarding socio-demographic characteristics, the younger the blood donors, the higher the prevalence, and the lower the educational level, the higher the odds of a positive SARS-Cov-2 antibody. Similar results were found for the weighted prevalence. Discussion: Although our findings resulted from a convenience sample, they match some basic premises: the increasing trend over time, since the epidemic curve in the state is still on the rise; the higher prevalence among the youngest who are more likely to circulate; and the higher prevalence among the less educated as they have more difficulties in following the social distancing recommendations. Despite the study limitations, it is possible to infer that protective levels of natural herd immunity to SARS-CoV-2 are far from being reached in Rio de Janeiro.

Particle filters for mobile robot localization must balance computational requirements and accuracy of localization. Increasing the number of particles in a particle filter improves accuracy, but also increases the computational requirements. Hence, we investigate a different paradigm to better utilize particles than to increase their numbers. To this end, we introduce the Corrective Gradient Refinement (CGR) algorithm that uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. We develop an observation model for mobile robot localization using point cloud sensors (LIDAR and depth cameras) with vector maps. This observation model is then used to analytically compute the state space gradients necessary for CGR. We show experimentally that the resulting complete localization algorithm is more accurate than the Sampling/Importance Resampling Monte Carlo Localization algorithm, while requiring fewer particles.

We have developed a novel type of submersible called a Synergistically Propelled Ichthyoid [SPI] which is propelled by the combined jet action and oscillatory motion of a fluttering fluid-conveying tail. Two dynamic models for an SPI are presented and solved. An analytically tractable model presented in a previous work which assumes a submersible at constant velocity has been included as a reference point. A more complex model in a general planar reference frame is also presented and is solved numerically. These simulations show a benefit to using a fluttering tail relative to a dimensionally identical rigid tail. Results which show that the SPI can be turned by an appropriate choice of time-variable fluid velocity are also presented. Construction details of an experimental realization are provided. Preliminary measurements taken using that platform are also provided; these measurements qualitatively confirm the simulation's conclusion that a fluttering flexible tail is capable of higher speed than a dimensionally identical rigid tail.

As a contribution to the debate about Fair Trade contributions to the United Nations Sustainability Development Goals, this article investigates Spanish shoppers' behaviour towards Fairtrade coffee. Although consumers generally state that they purchase fairly traded products, the market shares of most of them remain low, a phenomenon known as the ethical purchasing gap. Our review identifies a gap in extant literature to draw insights on the ethical purchasing gap, utilising two existing theories: attitudes and construal level as appropriate theoretical framework. The first theory highlights the duality of individuals' attitudes towards an object: explicit attitudes are accessible to the consumers, whereas implicit attitudes are the ones they cannot recall, but nonetheless affect behaviour. The second theory examines the influence of low-level construal (concrete, specific) or high-level construal (general) information on decision-making. A three-stage experiment took place in two sessions in a large university in Madrid in order to apply these two theories. It was based on an online survey on explicit attitudes and purchase intention, and an Implicit Association Test (IAT) to identify implicit attitudes. It was run two weeks apart to capture three points of time effects. The results reveal that, despite exposure to different stimuli, implicit attitudes remain stable along three points of time. The average difference in purchase intentions was positive for low-level construal and negative for high-level construal. Explicit attitudes were not influenced by the exposure to the stimuli. No correlation was found between purchase intentions and implicit or explicit attitudes. These findings have useful managerial implications for both Fair Trade practitioners and academics.

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