The Odyssey’s mythological network

We are grateful to Maurício A. Ribeiro which fitted the power law with cut-off degree distribution for Facebook’s and Odyssey’s networks. This work was supported by 1) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) to PJM, and 2) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) to SESP.Peer reviewedPublisher PD

The Oral Tolerance as a Complex Network Phenomenon

Date of Acceptance: 23/05/2015 Funding: MSB acknowledges the Engineering and Physical Sciences Research Council (EPSRC) - UK grant EP/I032606/1. PJM and MD received regular scholarships from the Brazilian the following agency: Higher Education Personnel Training Coordination (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) CAPES http://www.fisica.uepg.br:7080/ppgfisica​/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Pool boiling of nanofluids on biphilic surfaces: An experimental and numerical study

This study addresses the combination of customized surface modification with the use of nanofluids, to infer on its potential to enhance pool-boiling heat transfer. Hydrophilic surfaces patterned with superhydrophobic regions were developed and used as surface interfaces with different nanofluids (water with gold, silver, aluminum and alumina nanoparticles), in order to evaluate the effect of the nature and concentration of the nanoparticles in bubble dynamics and consequently in heat transfer processes. The main qualitative and quantitative analysis was based on extensive post-processing of synchronized high-speed and thermographic images. To study the nucleation of a single bubble in pool boiling condition, a numerical model was also implemented. The results show an evident benefit of using biphilic patterns with well-established distances between the superhydrophobic regions. This can be observed in the resulting plot of the dissipated heat flux for a biphilic pattern with seven superhydrophobic spots, δ = 1/d and an imposed heat flux of 2132 w/m2. In this case, the dissipated heat flux is almost constant (except in the instant t* ≈ 0.9 when it reaches a peak of 2400 W/m2), whilst when using only a single superhydrophobic spot, where the heat flux dissipation reaches the maximum shortly after the detachment of the bubble, dropping continuously until a new necking phase starts. The biphilic patterns also allow a controlled bubble coalescence, which promotes fluid convection at the hydrophilic spacing between the superhydrophobic regions, which clearly contributes to cool down the surface. This effect is noticeable in the case of employing the Ag 1 wt% nanofluid, with an imposed heat flux of 2132 W/m2, where the coalescence of the drops promotes a surface cooling, identified by a temperature drop of 0.7 °C in the hydrophilic areas. Those areas have an average temperature of 101.8 °C, whilst the average temperature of the superhydrophobic spots at coalescence time is of 102.9 °C. For low concentrations as the ones used in this work, the effect of the nanofluids was observed to play a minor role. This can be observed on the slight discrepancy of the heat dissipation decay that occurred in the necking stage of the bubbles for nanofluids with the same kind of nanoparticles and different concentration. For the Au 0.1 wt% nanofluid, a heat dissipation decay of 350 W/m2 was reported, whilst for the Au 0.5 wt% nanofluid, the same decay was only of 280 W/m2. The results of the numerical model concerning velocity fields indicated a sudden acceleration at the bubble detachment, as can be qualitatively analyzed in the thermographic images obtained in this work. Additionally, the temperature fields of the analyzed region present the same tendency as the experimental results.This work was funded by Portuguese national funds of FCT/MCTES (PIDDAC) through the base funding from the following research units: UIDB/00532/2020 (Transport Phenomena Research Center, CEFT), UIDB/04077/2020 (MEtRICs) and UIDP/04436/2020. The authors are also grateful for the funding of FCT through the projects LISBOA-01-0145-FEDER-030171/NORTE-01-0145-FEDER-030171 (PTDC/EME-SIS/30171/2017), funded by COMPETE2020, NORTE2020, PORTUGAL2020 and FEDER. The authors also acknowledge FCT for partially financing the research under the framework of the project UTAP-EXPL/CTE/0064/2017, financiado no ambito do Projeto 5665-Parcerias Internacionais de Ciencia e Tecnologia, UT Austin Programme. Mr Pedro Pontes also acknowledgesFCT for his fellowship ref. SFRH/BD/149286/2019

The Worcester Alternative School : a study in the development of an educational innovation.

<p>Graphical representation of the mean flux of walkers f(i) (black histogram) and the KO CD103⁺ (grey histogram).</p

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost