A ética do silêncio racial no contexto urbano: políticas públicas e desigualdade social no Recife, 1900-1940

Mais de meio século após o preconceito racial ter se tornado o principal alvo dos movimentos urbanos pelos direitos civis nos Estados Unidos e na África do Sul, e décadas depois do surgimento dos movimentos negros contemporâneos no Brasil, o conjunto de ferramentas legislativas criado no Brasil para promover o direito à cidade ainda adere à longa tradição brasileira de silêncio acerca da questão racial. Este artigo propõe iniciar uma exploração das raízes históricas desse fenômeno, remontando ao surgimento do silêncio sobre a questão racial na política urbana do Recife, Brasil, durante a primeira metade do século XX. O Recife foi eé um exemplo paradigmático do processo pelo qual uma cidade amplamente marcada por traços negros e africanos chegou a ser definida política e legalmente como um espaço pobre, subdesenvolvido e racialmente neutro, onde as desigualdades sociais originaram na exclusão capitalista, e não na escravidão e nas ideologias do racismo científico. Neste sentido, Recife lança luzes sobre a política urbana que se gerou sob a sombra do silêncio racial.More than half a century after racial prejudice became central to urban civil rights movements in the United States and South Africa, and decades after the emergence of Brazil’s contemporary Black movements, Brazil's internationally recognized body of rights-to-the-city legislation still adheres to the country's long historical tradition of racial silence. This article explores the historical roots of this phenomenon by focusing on the emergence of racial silence in Recife, Brazil during the first half of the 20th Century. Recife was and remains a paradigmatic example of the process through which a city marked by its Black and African roots came to be legally and politically defined as a poor, underdeveloped and racially neutral space, where social inequalities derived from capitalist exclusion rather than from slavery and scientific racism. As such, Recife'sexperience sheds light on the urban policies that were generated in the shadow of racial silence

Germinação de sementes de jenipapo: temperatura, substrato e morfologia do desenvolvimento pós-seminal Seed germination of Genipa americana l. - rubiaceae: temperature, substrate and post-seminal development

O presente trabalho teve como objetivos definir o tipo de substrato e a temperatura mais adequados à germinação de sementes de jenipapo (Genipa americana L.), conhecer a morfologia das sementes e seu desenvolvimento pós-seminal, caracterizando as plântulas normais, o tipo de germinação e os padrões de anormalidade. Para tanto, realizou-se um experimento colocando-se as sementes sobre os seguintes substratos: papel, vermiculita e solo, nas temperaturas constantes de 20°C, 25°C, 30°C, e 35°C e alternada de 20°C-30°C. O delineamento estatístico empregado foi o inteiramente casualizado (5 x 3), com quatro repetições de 50 sementes. Foram analisados os parâmetros germinação normal (%) e velocidade de germinação. Os melhores resultados foram obtidos nas temperaturas constantes de 25°C, 30°C e 35°C, e nos substratos vermiculita e solo.<br>This study aimed to define the best substrate and temperature for germination of genipap (Genipa americana L. - Rubiaceae) seeds, and to describe the morphology of its seeds, post-seminal development, normal and abnormal seedlings. The experiment was designed according to a 5 x 3 factorial with constant temperatures of 20°C, 25°C, 30°C and 35°C and alternated 20°C-30°C in filter paper, vermiculite and soil substrates. The following parameters were analyzed: normal percentage germination and speed of germination. The 25°C, 30°C and 35°C temperatures and vermiculite and soil substrates were the best conditions for seed germination

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype