The current in a given circuit is not to exceed 24 A. Which value of fuse is best? ?->(Show Answer!)

1. The current in a given circuit is not to exceed 24 A. Which value of fuse is best?

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MCQ-> The English alphabet is divided into five groups. Each group starts with the vowel and the consonants immediately following that vowel and the consonants immediately following that vowel are included in that group. Thus, the letters A, B, C, D will be in the first group, the letters E, F, G, H will be in the second group and so on. The value of the first group is fixed as 10, the second group as 20 and so on. The value of the last group is fixed as 50. In a group, the value of each letter will be the value of that group. To calculate the value of a word, you should give the same value of each of the letters as the value of the group to which a particular letter belongs and then add all the letters of the word: If all the letters in the word belong to one group only, then the value of that word will be equal to the product of the number of letters in the word and the value of the group to which the letters belong. However, if the letters of the words belong to different groups, then first write the value of all the letters. The value of the word would be equal to the sum of the value of the first letter and double the sum of the values of the remaining letters.For Example : The value of word ‘CAB’ will be equal to 10 + 10 + 10 = 30, because all the three letters (the first letter and the remaining two) belong to the first group and so the value of each letter is 10. The value of letter BUT = $$10 + 2 \times 40 + 2 \times 50 = 190$$ because the value of first letter B is 10, the value of T = 2 $$\times$$ 40 (T belongs to the fourth group) and the value of U = 2 $$\times$$ 50 (U belongs to the fifth group). Now calculate the value of each word given in questions 161 to 165 :AGE
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MCQ-> Read the passage carefully and answer the given questionsThe complexity of modern problems often precludes any one person from fully understanding them. Factors contributing to rising obesity levels, for example, include transportation systems and infrastructure, media, convenience foods, changing social norms, human biology and psychological factors. . . . The multidimensional or layered character of complex problems also undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is no best person. When putting together an oncological research team, a biotech company such as Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire people whose resumes score highest according to some performance criteria. Instead, they would seek diversity. They would build a team of people who bring diverse knowledge bases, tools and analytic skills. . . .Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category. Thus the team should consist of the ‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria applied to individuals will produce the best team. Each of these domains possesses such depth and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers were published last year covering various techniques, domains of enquiry and levels of analysis, ranging from molecules and synapses up through networks of neurons. Given that complexity, any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors in the 50-metre butterfly, must fail. What could be true is that given a specific task and the composition of a particular team, one scientist would be more likely to contribute than another. Optimal hiring depends on context. Optimal teams will be diverse.Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas tend to rank as high-impact. It can also be found in the structure of the so-called random decision forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? A weighted majority rules. Random forests can serve many ends. They can identify bank fraud and diseases, recommend ceiling fans and predict online dating behaviour. When building a forest, you do not select the best trees as they tend to make similar classifications. You want diversity. Programmers achieve that diversity by training each tree on different data, a technique known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases - those that the current forest gets wrong. This ensures even more diversity and accurate forests.Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. Ranking people by common criteria produces homogeneity. . . . That’s not likely to lead to breakthroughs.Which of the following conditions, if true, would invalidate the passage’s main argument?
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