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Q965878 Inglês

                        From Nail bars to car washes: how big

                             is the UK’s slavery problem?

                                                                                                  by Annie Kelly


      Does slavery exist in the UK?

      More than 250 years since the end of the transatlantic slave trade, there are close to 41 million people still trapped in some form of slavery across the world today. Yet nobody really knows the scale and how many victims or perpetrators of this crime there are in Britain.

      The data that has been released is inconsistent. The government believes there are about 13,000 victims of slavery in the UK, while earlier this year the Global Slavery Index released a much higher estimate of 136,000.

      Statistics on slavery from the National Crime Agency note the number of people passed on to the government’s national referral mechanism (NRM), the process by which victims of slavery are identified and granted statutory support. While this data gives a good snapshot of what kinds of slavery are most prevalent and who is falling victim to exploiters, it doesn’t paint the whole picture. For every victim identified by the police, there will be many others who are not found and remain under the control of traffickers, pimps and gangmasters.

      There are also many potential victims who don’t agree to go through the mechanism because they don’t trust the authorities, or are too scared to report their traffickers. Between 1 November 2015 and 30 June 2018, the government received notifications of 3,306 potential victims of modern slavery in England and Wales who were not referred to the NRM.

      […]

      The police recorded 3,773 modern slavery offences between June 2017 and June 2018.

      […]

(Source: https://www.theguardian.com/global-development/2018/ oct/18/nail-bars-car-washes-uk-slavery-problem-anti-slavery-day. Access: 20/10/2018)

Taking into account the following excerpt: “There are also many potential victims who don’t agree to go through the mechanism because they don’t trust the authorities (…)”, mark the option which best describes the word “they”:
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Q956587 Inglês

                  


      America’s deadliest building fire for more than a decade struck Oakland, California, on December 2nd 2016, killing 36 people attending a dance party in a warehouse that had become a cluttered artist collective. The disaster highlights an open secret: many cities lack resources to inspect for fire risk all the structures that they should. Even though the Oakland building had no fire sprinklers and at least ten people lived there illegally, no inspector had visited in about 30 years. How might cities make better use of the inspectors they do have?

      A handful of American cities have begun to seek help from a new type of analytics software. By crunching diverse data collected by government bodies and utilities, the software works out which buildings are most likely to catch fire and should therefore be inspected first. Plenty of factors play a role. Older, wooden buildings, unsurprisingly, pose more risk, as do those close to past fires and leaks of gas or oil. Poverty also pushes up fire risk, especially if lots of children, who may be attracted to mischief, live nearby. More telling are unpaid taxes, foreclosure proceedings and recorded complaints of mould, rats, crumbling plaster, accumulating rubbish, and domestic fights, all of which hint at property neglect. A building’s fire risk also increases the further it is from its owner’s residence.

      Predictive software designed at Harvard that Portland, Oregon, will soon begin using will do that. Perhaps more importantly, the city’s fire chief noticed that buildings marked as being the biggest risks are clustered in areas lacking good schools, public transport, health care and food options. Healthier, happier people start fewer fires, he concluded. He now lobbies officials to reduce Portland’s pockets of deteriorated areas.

(The Economist. www.economist.com/the-economist-explains /2018/08/29/how-cities-can-better-prevent-fires. Adaptado)

No trecho do segundo parágrafo “A building’s fire risk also increases the further it is from its owner’s residence”, o termo em destaque (it) se refere a
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Q949371 Inglês
O texto a seguir é referência para a questão.

Ancient dreams of intelligent machines: 3,000 years of robots

    The French philosopher René Descartes was reputedly fond of automata: they inspired his view that living things were biological machines that function like clockwork. Less known is a strange story that began to circulate after the philosopher’s death in 1650. This centred on Descartes’s daughter Francine, who died of scarlet fever at the age of five.
    According to the tale, a distraught Descartes had a clockwork Francine made: a walking, talking simulacrum. When Queen Christina invited the philosopher to Sweden in 1649, he sailed with the automaton concealed in a casket. Suspicious sailors forced the trunk open; when the mechanical child sat up to greet them, the horrified crew threw it overboard.
    The story is probably apocryphal. But it sums up the hopes and fears that have been associated with human-like machines for nearly three millennia. Those who build such devices do so in the hope that they will overcome natural limits – in Descartes’s case, death itself. But this very unnaturalness terrifies and repulses others. In our era of advanced robotics and artificial intelligence (AI), those polarized responses persist, with pundits and the public applauding or warning against each advance. Digging into the deep history of intelligent machines, both real and imagined, we see how these attitudes evolved: from fantasies of trusty mechanical helpers to fears that runaway advances in technology might lead to creatures that supersede humanity itself.

(Disponível em: <https://www.nature.com/articles/d41586-018-05773-y>.)
In the sentence “Those who build such devices do so in the hope that they will overcome natural limits …”, the underlined word refers to:
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Ano: 2018 Banca: Marinha Órgão: EAM Prova: Marinha - 2018 - EAM - Marinheiro |
Q892550 Inglês

Read the sentences and mark the correct option to fill in the blanks respectively.


Sarah is ____________friend.____________ lives next to my house. We love riding our bikes.___________ bike is red. ____________ is green. We love spending tome together!

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Q848987 Inglês

Based on the text above, judge the following items.


The pronoun “You” (.4) refers specifically to the individuals who are reading the text.

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Respostas
6: B
7: B
8: B
9: A
10: E