Recognizing the importance of mental health awareness, a group of mental health professionals launched a campaign to destigmatize mental health issues.

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Indian Railways (IR) measures the punctuality of trains at the terminating stations. In other countries, it is measured at the originating point, intermediata station, and at terminating stations. In addition, for measuring punctuality, IR provides an allowance of 15 minutes delay with reference to the scheduled time. Other countries have much stricter thresholds as given follows. Japan in seconds, Netherland 3 minutes, Russia and Germany in 5 minutes, Great Brittan in 10 minutes. Even with a low benchmark and higher threshold, the punctuality of Mail/Express trains over IR declined from 79 per cent (2012-13) to 69.23 per cent (2018-19) that ton at the terminating stations only. As per the ICMS report, the poorest punctuality arnong the zones were in NCR during 2012-13 and 2018-19. In 2015-16, out of 5.85 lakh traine, 1.27 lakh Express trains reached the destination station with delay. During 2018-19, number of delayed trains increased by 43 per cent. Out of 6.22 lakh trains, 1.82 lakh trains did not meet the punctuality yardstick of 15 minutes. Three zonal railways ways-NCR ECR and NR-contributed 100 and 67 per cent in total delay of IR during 2015-16 and 2018-19. Review of the Complaint Management System by by Audit revealed that there was sharp increase in the number or of complaint cases on a of punctually in IR. Duning the period 2015-16, 2016-17 and 2017-18 the number of complaints that were lodged in the system for late e running of trains was 9112, 20,025 and 35,793 res actively The complaints increased to 40,077 (an increase of 340 per cent over the year 2015-16) in 2018-19. Audit analysed the data for Mal/Express trains for 2016-17, 2017-18 and 2018-10 from ICMS report number 201 and noticed that on an average 13,15,450 trains are reported through ICMS per annum. Of these, only 29.64 per cent of trains (3,80,877 trains) reached on time (RT) and 20.17 per cent of trains (2.65,301 trains) arrived before time (IT). Remaining 50.18 per cent of trains (6,60,188 raine) are delayed. Before time cases indicates poor timetabling by provision of extra running time, Ministry of Railways stated (November 2021) that IR measures punctuality on terminating basis However, monitoring of running r is done on continuou ous and real-time basis. To put the punctuality performance in perspective it is to be noted that between 2012-2013 and 2018-2019, the train services have increased In numbers by 20 O per cent. Audit is of the view that Punctuality massured on terminating bels does not conform to global best practicos. Audit noticed that by computerized timetabling, grouping of trains, conflict resolution and integrated maintenance, punctuality of trains can be improved.
From the above analysis it is known that some trains reach the destination before the scheduled time. What is the reason?

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The object of study in game theory is the game, which is a formal model of an interactive situation. It typically involves several players; a game with only one player is usually called a decision problem. The formal definition lays out the players, their preferences, their information, the strategic actions available to them, and how these influence the outcome. Games can be described formally at various levels of detail. A coalitional (or cooperative) game is a high-level description, specifying only what payoffs each potential group, or coalition, can obtain by the cooperation of its members. What is not made explicit is the process by which the coalition forms. As an example, the players may be several parties in parliament. Each party has a different strength, based upon the number of seats occupied by party members. The game describes which coalitions of parties can form a majority, but does not delineate, for example, the negotiation process through which an agreement to vote en bloc is achieved. Cooperative game theory investigates such coalitional games with respect to the relative amounts of power held by various players, or how a successful coalition should divide its proceeds. This is most naturally applied to situations arising in political science or international relations, where concepts like power are most important. For example, Nash proposed a solution for the division of gains from agreement in a bargaining problem which depends solely on the relative strengths of the two parties' bargaining position. The amount of power a side has is determined by the usually inefficient outcome that results when negotiations break down. Nash's model fits within the cooperative framework in that it does not delineate a specific timeline of offers and counteroffers, but rather focuses solely on the outcome of the bargaining process. In contrast, noncooperative game theory is concerned with the analysis of strategic choices. The paradigm of noncooperative game theory is that the details of the ordering and timing of players' choices are crucial to determining the outcome of a game. In contrast to Nash's cooperative model, a noncooperative model of bargaining would posit a specific process in which it is prespecified who gets to make an offer at a given time. The term "noncooperative" means this branch of game theory explicitly models the process of players making choices out of their own interest. Cooperation can, and often does, arise in noncooperative models of games, when players find it in their own best interests.
Nash's model........................

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The outline of our theory can be expressed as follows. When employment increases, aggregate real income is increased. The psychology of the community is such that when aggregate real income is increased aggregate consumption is increased, but not by so much as income. Hence employers would make a loss if the whole of the increased employment were to be devoted to satisfying the increased demand for immediate consumption. Thus, to justify any given amount of employment there must be an amount of current investment sufficient to absorb the excess of total output over what the community chooses to consume when employment is at the given level. For unless there is this amount of investment, the receipts of the entrepreneurs will be less than is required to induce them to offer the given amount of employment. It follows, therefore, that, given what we shall call the community's propensity to consume, the equilibrium level of employment, i.e. the level at which there is no inducement to employers as a whole either to expand or to contract employment, will depend on the amount of current investment. The amount of current investment will depend, in turn, on what we shall call the inducement to invest; and the inducement to invest will be found to depend on the relation between the schedule of the marginal efficiency of capital and the complex of rates of interest on loans of various maturities and risks. Thus, given the propensity to consume and the rate of new investment, there will be only one level of employment consistent with equilibrium; since any other level will lead to inequality between the aggregate supply price of output as a whole and its aggregate demand price. This level cannot be greater than full employment, i.e. the real wage cannot be less than the marginal disutility of labour. But there is no reason in general for expecting it to be equal to full employment. The effective demand associated with full employment is a special case, only realised when the propensity to consume and the inducement to invest stand in a particular relationship to one another. This particular relationship, which corresponds to the assumptions of the classical theory, is in a sense an optimum relationship. But it can only exist when, by accident or design, current investment provides an amount of demand just equal to the excess of the aggregate supply price of the output resulting from full employment over what the community will choose to spend on consumption when it is fully employed.
The equilibrium level of employment is the function of

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India's official statistical machinery is gearing up to relaunch the All-India Household Consumer Expenditure Survey, traditionally undertaken quinquennially, from July 2022. If it fructifies, the result may be known towards the latter half of 2024, provided the Government permits the release. The last such Survey (2017-18), did not get such a sanction- its results reportedly indicated the first fall in monthly per-capita spending by households since 1972-73, with rural households facing a sharper decline compared to 2011-12. The Statistics Ministry had flagged 'discrepancies', 'data quality issues' and 'divergences' between estimated consumption levels and the actual output of goods and services. While it had sought to scuttle suggestions that unflattering data were being obfuscated, a better course of action would have been to release the data with caveats. It could have argued, for instance, that the numbers, at best, reflect the short-term impact of the 'bold structural reforms' carried out in the year preceding the Survey, to 'formalise' the economy-demonetisation and the GST. A fresh survey could then have been commissioned later for a clearer picture. This is what the UPA had done in 2011-12 to measure employment and consumer spending levels afresh, after the 2009-10 Surveys were affected by the global financial crisis and a severe drought that hit rural incomes. The Government had promised to examine the 'feasibility' of a fresh Consumer Spending Survey, over 2020-21 and 2021-22, after 'incorporating all data quality refinements' mooted by a panel. One hopes the exact 'refinements' are spelt out upfront in the upcoming Survey. Of equal import is providing data comparable with past numbers, while factoring in changes in consumption patterns; and it may still not be too late to release the previous Survey's findings to help assess longer term trends. The absence of official data on such a critical aspect of the economy-used to estimate poverty levels, rebase GDP, and to make private investment decisions for over a decade, is damaging to India. Being a free-market and transparent democracy distinguished India from the likes of China where official data are read with a pinch of salt. The Government's actions, including the delayed release of critical jobs data, have dulled that perception. If anything, such Surveys need to be conducted more frequently for more effective policy actions informed by ground realities, no matter how unpleasant they may be. Now, imperfect proxies are deployed to gauge the economy, surmises made about the extinction of extreme poverty, and outlays are tom-tommed without evidence on outcomes. The NSO must be empowered to collect and disseminate more data points, without fear of insinuations about its abilities, or a looming axe on its regular Surveys.
What is the central theme of the above passage?

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After wheat, there is pressure building up for banning exports of raw cotton. The Narendra Modi government must resist any such demand emanating from domestic textile mills and the garment industry. There are at least three reasons why this is so. To start with, the output of one industry is often the input of another. In this case, cotton is spun by mills into yarn, which is further woven or knitted into fabric used for making garments. During the year ended March 31, 2022, India exported $2.8 billion worth of raw cotton, $5.5 billion of cotton yarn, $8.2 billion of cotton fabrics and made-ups, and $9 billion of cotton ready-made garments. Will spinning mills seeking a ban on cotton shipments agree to the same in respect of yarn? When exports are happening at every stage of the value chain, how can there be pick and choose on which one to disallow or promote? Secondly, while it is true that cotton prices have risen by around 50 per cent since the start of 2022, this cannot be blamed just on exports - which are actually expected to halve in the current marketing season (October-September) compared to 2020-21. Domestic prices increasing to international parity levels should, by itself, slow down exports in the natural course. The Modi government did the right thing last month by scrapping the import duty on cotton. It should, in fact, remove the 10 per cent duty on yarn imports as well. The correct approach to tackling inflation, whether in wheat, cotton or yarn, is by allowing duty-free imports without putting fetters on exports. The third reason has to do with timing. Sowing of cotton has already started in Punjab, Haryana and Rajasthan. Plantings in Gujarat, Maharashtra, Telangana and other states will also take off with the arrival of the southwest monsoon rains. High prices would definitely incentivise farmers to expand acreage this time; banning exports will send the opposite signals to the ultimate detriment of the textile industry. The real problem in cotton that needs addressing is yields. The introduction of Bt cotton in the early 2000s led to India's production going up about 2.5 times to 398 lakh bales by 2013-14. Since then, it has been on a falling trajectory, with the latest output estimate for 2021-22 at below 325 lakh bales. The plants incorporating Bt genes have over time developed susceptibility to pink bollworm and whitefly insect pests, reducing yields and also farmer enthusiasm for growing cotton. The Modi government's succumbing to uninformed lobby pressures against genetic engineering technologies has not helped matters. A clearheaded approach is required for this crop, which is a source of not just fibre (lint), but also food (cotton-seed oil) and feed (oil-cake).
The main suggestion in the above passage is...

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The Correct Option is C

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