Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Impact of global warming on beef cattle production cost in Brazil

Global warming is affecting agribusiness in its economic aspects. Therefore, the prediction of the evolution of Brazilian beef cattle production cost was made using the IPCC forecast scenario for global warming. The methodology consisted of two steps: (i) the development of a fuzzy model that estimated the grazing land capacity (RP) decrease risk as a function of the changes in the average total rain index, air temperature and increase in extension of the dry season; and (ii) the design of an algorithm for predicting the decrease in production as function of the RPfuzzy model, that results in the impact in beef cattle productivity, and consequent increase in production costs. Historical environmental data from important producing counties in the Cerrado were organized and a set of fuzzy Gaussian functions were developed, and three possible settings (optimistic, medium and pessimistic) were considered. The decrease in beef cattle productivity was estimated using the losses in production due to the increase in air temperature and vulnerability of pasture capacity. The boundary settings for the total increase of production cost scenario used the number of animals per area of grazing land, the adoption of grain supplement and its future scenario; and the result output function pointed to a threshold within a variation from an increase in production cost of 80% (optimistic) to 160% (pessimistic). Under the optimistic scenario the total cost of Brazilian beef cattle production in the Cerrado became near to US$2.88 kg-1, while in the pessimistic scenario this cost reached US$ 4.16 kg-1, challenging the international competitiveness of this economic segment.O aquecimento global afeta o agronegócio em seus aspectos econômicos. Foi feita previsão daevolução do custo de produção de carne bovina brasileira usando a predição de aquecimento global do IPCC. A metodologia consistiu de duas etapas: (i) o desenvolvimento de modelo fuzzy que estimou o risco de decréscimo da capacidade de pastagens (RP) em função das mudanças no índice pluviométrico total, na temperatura do ar e na extensão da estação de seca; e (ii) o desenvolvimento de um algoritmo para predição do decréscimo da produção em função de um modelo fuzzy de RP que resulte no impacto na produtividade bovina de corte e conseqüente aumento no custo de produção. Foram organizados os dados históricos de fatores ambientais dos municípios importante produção no Cerrado e um conjunto de funções Gaussianas fuzzy foi desenvolvido e três estimativas possíveis (otimista, média e negativa) foram consideradas. O decréscimo na produtividade do gado foi estimado usando as perdas de produção devido ao acréscimo da temperatura bem como da vulnerabilidade da capacidade de pastagem. O estabelecimento dos limites para o cenário do acréscimo do custo de produção usou o número de unidade animal por área de pastagem, a adoção de suplemento de grãos e o cenário de produção futura; e o resultado da função de saída apontou para uma variação do acréscimo do custo de produção de 80% (otimista) até 160% (pessimista). Sob o cenário otimista, o custo total da produção brasileira de carne bovina no Cerrado chega a US$2,88 kg-1, enquanto no cenário pessimista este custo pode atingir US$ 4,16 kg-1, o que pode comprometer a competitividade internacional do setor