In GIF system, protein residue is best cleaned by ?->(Show Answer!)

1. In GIF system, protein residue is best cleaned by

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MCQ->In GIF system, protein residue is best cleaned by....

MCQ->Statements: About 50 per cent of the animal by-products - hair, skin, horns etc. is edible protein. American chemists have developed a method of isolating 45 per cent of this protein. They used an enzyme developed in Japan to break down soya protein. Conclusions: Americans have not been able to develop enzymes. Animal by-products protein has the same composition as soya protein.

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MCQ-> Read the following passage carefully and answer the questions given. Certain words have been given in bold to help you locate them while answering some of the questions.We are told that economy is growing and that such growth benefits all of us. However, what you see is not what you always get. Most people are experiencing declining economic security in response to the problems of the global system, many communities have turned to Local Exchange Systems (LESs) to help regain some control over their economic situations.Local exchange systems come in many forms. They often involve the creation of a local currency, or a system of bartering labour, or trading of agricultural products as a means of supporting the region in which they are traded. Such a system helps preserve the viability of local economies.Local currencies allow communities to diversify their economies, reinvest resources back into their region and reduce dependence on the highly concentrated and unstable global economy. Each local currency system serves as an exchange bank for skills and resources that Individuals in the community are willing to trade. Whether in the form of paper money, service credits, or other units, a local currency facilitates the exchange of services and resources among the members of a community.By providing incentives for local trade, communities help their small businesses and reduce underemployment by providing the jobs within the community. In addition, the local exchange of food and seeds promotes environmental conservation and community food security. Local food production reduces wasteful transportation and promotes self-reliance and genetic diversity. Each transaction within a local exchange system strengthens the community fabric as neighbours interact and meet one another.There are over 1,000 local change programs worldwide more than 30 local paper currencies in North America and at least 800 Local Exchange Trading Systems (LETS) throughout Europe. New Zealand and Australia Local Exchange Systems vary and evolve in accordance with the needs and circumstances of the local area. This diversity is critical to the success of the local currencies. For instance, a bank in rural Massachusetts refused to lend a fanner the money needed to make it through the winter. In response, the farmer decided to print his own money Berkshire Farm Preserve Notes. In winter, customers buy the notes for $9 and they may redeem them in the summer for $10 worth of vegetables. The system enabled the community to help a farm family after being abandoned by the centralised monetary system. As small family farms continue to disappear at an alarming rate, local currencies provide tools for communities to bind together, support their local food growers and maintain their local food suppliers.Local Exchange Systems are not limited to developed countries.Rural areas of Asia, Latin America and Africa have offered some of the most effective and important programs, by adopting agriculture-based systems of exchange rather than monetary ones. In order to preserve genetic diversity, economic security and avoid dependence on industrial seed and chemical companies, many villages have developed seed saving exchange banks. For example, the village women in Ladakh have begun to collect and exchange rare seeds selected for their ability to grow in a harsh mountain climate. This exchange system protects agriculture diversity while promoting self-reliance. There is no one blueprint for a local exchange system, which is exactly why they are successful vehicles for localisation and sustainability. They promote local economic diversity and regional self-reliance while responding to a region’s specific needs. Local exchange systems play a pivotal role in creating models for sustainable societies. They are an effective educational tool, raising awareness about the global financial system and local economic matters. Local exchange systems also demonstrate that tangible, creative solutions exist and that communities can empower themselves to address global problems.Which of the following is same in meaning as the word ‘LIMITED TO’ as used in the passage?
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MCQ->In NutsAndBolts factory, one machine produces only nuts at the rate of 100 nuts per minute and needs to be cleaned for 5 minutes after production of every 1000 nuts.Another machine produces only bolts at the rate of 75 bolts per minute and needs to be cleaned for 10 minutes after production of every 1500 bolts. If both the machines start production at the same time, what is the minimum duration required for producing 9000 pairs of nuts and bolts?....

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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