The influence of fuel bias in the primary air duct on the gas particle flow characteristics
高二英语批判思维单选题50题
高二英语批判思维单选题50题1. In the news report about environmental protection, it is mentioned that a new technology can reduce carbon emissions by 50%. Which of the following statements can be inferred?A. All industries will adopt this technology immediately.B. The environment will be completely cleaned up soon.C. This technology will have a significant impact on reducing carbon emissions.D. There will be no more environmental problems.答案：C。
解析：A 选项所有行业立刻采用该技术过于绝对，新闻中未提及。
B 选项环境很快被完全清理干净也不现实，仅一种技术不能做到。
D 选项不会再有环境问题太绝对，一种技术不能解决所有环境问题。
C 选项，新闻中提到新技术能减少碳排放50%，可以推断出该技术对减少碳排放有重大影响。
2. After reading a famous novel, we know that the main character is brave and kind. Which of the following can we conclude?A. All the characters in the novel are brave and kind.B. The main character will never make mistakes.C. The main character's actions will always lead to positive results.D. The main character's qualities can inspire readers.答案：D。
No2阅读题翻译
NO2-11.美国的非洲黑奴所讲述的各别的民间故事究竟源自何处？有关这方面旷日持久的争论，不幸得很，已远远超越了对这些故事本身的内涵和功能之分析。
与非洲的文化沿袭性并非取决于对特定民间故事按其原始形式输入，并使之世代相传。
唯有在这些故事于奴隶们生活中所占据的位置，以及奴隶们所能从中获取的意义中，与非洲传统最为彰著的近似才能得以被揭示出来。
虽然黑奴生活在白人当中，但他们并没有不分青红皂白地从白人那里照搬照抄故事情节。
对黑人影响最为深远的，是那样一些欧美故事，其功能涵义和美学魅力与那些深深植根于其故土的故事具有最大程度上的类似。
不管黑奴的故事源自何方，关键在于，就语汇，叙事模式，性格塑造诸细节，以及故事情节而言，奴隶们均很快地将其汲取，为其所用。
2.在地球的地壳（crust）内部，岩石中所蕴含的能量代表着某种近乎取之不尽、用之不竭的能源（energy source），但直至近期，商业回收（commercial retrieval）仅限于地下热水以及/或者蒸气的回收系统。
这些系统在近期火山活动地区发展起来，在这些地方，频繁的热流（heat flow）导致水以温泉（geyser）和热泉（hot spring）的形式明晰可辨的喷发。
然而，在其它地区，也有热岩石在靠近地表处存在，但所存在的水甚不充分，不足以产生喷发现象。
因此，每当自发产生的（spontaneously produced）地热（geothermal）液体被认定为对于现存的（existing）商业系统不充分时，一种潜在的（potential）干热岩石储藏（HDR reservoir）便告存在。
由于近期能源危机的缘故，创立干热岩石（HDR）回收系统的新概念——这些涉及到钻孔打洞并将它们与置于地壳深处的人造蓄水也联系起来——正被研究开发。
在所有从HDR′s回收能量的尝试中，人为的剌激将必不可少，用以造成充分的渗透性，或有界流径（bounded flow path），以便通过液体在岩石表面循环流动这一手段，从而促进热量的提取回收。
计量经济学导论CH13习题答案
CHAPTER 13TEACHING NOTESWhile this chapter falls under “Advanced Topics,” most of this chapter requires no more sophistication than the previous chapters. (In fact, I would argue that, with the possible exception of Section 13.5, this material is easier than some of the time series chapters.)Pooling two or more independent cross sections is a straightforward extension of cross-sectional methods. Nothing new needs to be done in stating assumptions, except possibly mentioning that random sampling in each time period is sufficient. The practically important issue is allowing for different intercepts, and possibly different slopes, across time.The natural experiment material and extensions of the difference-in-differences estimator is widely applicable and, with the aid of the examples, easy to understand.Two years of panel data are often available, in which case differencing across time is a simple way of removing g unobserved heterogeneity. If you have covered Chapter 9, you might compare this with a regression in levels using the second year of data, but where a lagged dependent variable is included. (The second approach only requires collecting information on the dependent variable in a previous year.) These often give similar answers. Two years of panel data, collected before and after a policy change, can be very powerful for policy analysis. Having more than two periods of panel data causes slight complications in that the errors in the differenced equation may be serially correlated. (However, the traditional assumption that the errors in the original equation are serially uncorrelated is not always a good one. In other words, it is not always more appropriate to used fixed effects, as in Chapter 14, than first differencing.) With large N and relatively small T, a simple way to account for possible serial correlation after differencing is to compute standard errors that are robust to arbitrary serial correlation and heteroskedasticity. Econometrics packages that do cluster analysis (such as Stata) often allow this by specifying each cross-sectional unit as its own cluster.108SOLUTIONS TO PROBLEMS13.1 Without changes in the averages of any explanatory variables, the average fertility rate fellby .545 between 1972 and 1984; this is simply the coefficient on y84. To account for theincrease in average education levels, we obtain an additional effect: –.128(13.3 – 12.2) ≈–.141. So the drop in average fertility if the average education level increased by 1.1 is .545+ .141 = .686, or roughly two-thirds of a child per woman.13.2 The first equation omits the 1981 year dummy variable, y81, and so does not allow anyappreciation in nominal housing prices over the three year period in the absence of an incinerator. The interaction term in this case is simply picking up the fact that even homes that are near the incinerator site have appreciated in value over the three years. This equation suffers from omitted variable bias.The second equation omits the dummy variable for being near the incinerator site, nearinc,which means it does not allow for systematic differences in homes near and far from the sitebefore the site was built. If, as seems to be the case, the incinerator was located closer to lessvaluable homes, then omitting nearinc attributes lower housing prices too much to theincinerator effect. Again, we have an omitted variable problem. This is why equation (13.9) (or,even better, the equation that adds a full set of controls), is preferred.13.3 We do not have repeated observations on the same cross-sectional units in each time period,and so it makes no sense to look for pairs to difference. For example, in Example 13.1, it is veryunlikely that the same woman appears in more than one year, as new random samples areobtained in each year. In Example 13.3, some houses may appear in the sample for both 1978and 1981, but the overlap is usually too small to do a true panel data analysis.β, but only13.4 The sign of β1 does not affect the direction of bias in the OLS estimator of1whether we underestimate or overestimate the effect of interest. If we write ∆crmrte i = δ0 +β1∆unem i + ∆u i, where ∆u i and ∆unem i are negatively correlated, then there is a downward biasin the OLS estimator of β1. Because β1 > 0, we will tend to underestimate the effect of unemployment on crime.13.5 No, we cannot include age as an explanatory variable in the original model. Each person inthe panel data set is exactly two years older on January 31, 1992 than on January 31, 1990. This means that ∆age i = 2 for all i. But the equation we would estimate is of the form∆saving i = δ0 + β1∆age i +…,where δ0 is the coefficient the year dummy for 1992 in the original model. As we know, whenwe have an intercept in the model we cannot include an explanatory variable that is constant across i; this violates Assumption MLR.3. Intuitively, since age changes by the same amount for everyone, we cannot distinguish the effect of age from the aggregate time effect.10913.6 (i) Let FL be a binary variable equal to one if a person lives in Florida, and zero otherwise. Let y90 be a year dummy variable for 1990. Then, from equation (13.10), we have the linear probability modelarrest = β0 + δ0y90 + β1FL + δ1y90⋅FL + u.The effect of the law is measured by δ1, which is the change in the probability of drunk driving arrest due to the new law in Florida. Including y90 allows for aggregate trends in drunk driving arrests that would affect both states; including FL allows for systematic differences between Florida and Georgia in either drunk driving behavior or law enforcement.(ii) It could be that the populations of drivers in the two states change in different ways over time. For example, age, race, or gender distributions may have changed. The levels of education across the two states may have changed. As these factors might affect whether someone is arrested for drunk driving, it could be important to control for them. At a minimum, there is the possibility of obtaining a more precise estimator of δ1 by reducing the error variance. Essentially, any explanatory variable that affects arrest can be used for this purpose. (See Section 6.3 for discussion.)SOLUTIONS TO COMPUTER EXERCISES13.7 (i) The F statistic (with 4 and 1,111 df) is about 1.16 and p-value ≈ .328, which shows that the living environment variables are jointly insignificant.(ii) The F statistic (with 3 and 1,111 df) is about 3.01 and p-value ≈ .029, and so the region dummy variables are jointly significant at the 5% level.(iii) After obtaining the OLS residuals, ˆu, from estimating the model in Table 13.1, we run the regression 2ˆu on y74, y76, …, y84 using all 1,129 observations. The null hypothesis of homoskedasticity is H0: γ1 = 0, γ2= 0, … , γ6 = 0. So we just use the usual F statistic for joint significance of the year dummies. The R-squared is about .0153 and F ≈ 2.90; with 6 and 1,122 df, the p-value is about .0082. So there is evidence of heteroskedasticity that is a function of time at the 1% significance level. This suggests that, at a minimum, we should compute heteroskedasticity-robust standard errors, t statistics, and F statistics. We could also use weighted least squares (although the form of heteroskedasticity used here may not be sufficient; it does not depend on educ, age, and so on).(iv) Adding y74⋅educ, , y84⋅educ allows the relationship between fertility and education to be different in each year; remember, the coefficient on the interaction gets added to the coefficient on educ to get the slope for the appropriate year. When these interaction terms are added to the equation, R2≈ .137. The F statistic for joint significance (with 6 and 1,105 df) is about 1.48 with p-value ≈ .18. Thus, the interactions are not jointly significant at even the 10% level. This is a bit misleading, however. An abbreviated equation (which just shows the coefficients on the terms involving educ) is110111kids= -8.48 - .023 educ + - .056 y74⋅educ - .092 y76⋅educ(3.13) (.054) (.073) (.071) - .152 y78⋅educ - .098 y80⋅educ - .139 y82⋅educ - .176 y84⋅educ .(.075) (.070) (.068) (.070)Three of the interaction terms, y78⋅educ , y82⋅educ , and y84⋅educ are statistically significant at the 5% level against a two-sided alternative, with the p -value on the latter being about .012. The coefficients are large in magnitude as well. The coefficient on educ – which is for the base year, 1972 – is small and insignificant, suggesting little if any relationship between fertility andeducation in the early seventies. The estimates above are consistent with fertility becoming more linked to education as the years pass. The F statistic is insignificant because we are testing some insignificant coefficients along with some significant ones.13.8 (i) The coefficient on y85 is roughly the proportionate change in wage for a male (female = 0) with zero years of education (educ = 0). This is not especially useful since we are not interested in people with no education.(ii) What we want to estimate is θ0 = δ0 + 12δ1; this is the change in the intercept for a male with 12 years of education, where we also hold other factors fixed. If we write δ0 = θ0 - 12δ1, plug this into (13.1), and rearrange, we getlog(wage ) = β0 + θ0y85 + β1educ + δ1y85⋅(educ – 12) + β2exper + β3exper 2 + β4union + β5female + δ5y85⋅female + u .Therefore, we simply replace y85⋅educ with y85⋅(educ – 12), and then the coefficient andstandard error we want is on y85. These turn out to be 0ˆθ = .339 and se(0ˆθ) = .034. Roughly, the nominal increase in wage is 33.9%, and the 95% confidence interval is 33.9 ± 1.96(3.4), or about 27.2% to 40.6%. (Because the proportionate change is large, we could use equation (7.10), which implies the point estimate 40.4%; but obtaining the standard error of this estimate is harder.)(iii) Only the coefficient on y85 differs from equation (13.2). The new coefficient is about –.383 (se ≈ .124). This shows that real wages have fallen over the seven year period, although less so for the more educated. For example, the proportionate change for a male with 12 years of education is –.383 + .0185(12) = -.161, or a fall of about 16.1%. For a male with 20 years of education there has been almost no change [–.383 + .0185(20) = –.013].(iv) The R -squared when log(rwage ) is the dependent variable is .356, as compared with .426 when log(wage ) is the dependent variable. If the SSRs from the regressions are the same, but the R -squareds are not, then the total sum of squares must be different. This is the case, as the dependent variables in the two equations are different.(v) In 1978, about 30.6% of workers in the sample belonged to a union. In 1985, only about 18% belonged to a union. Therefore, over the seven-year period, there was a notable fall in union membership.(vi) When y85⋅union is added to the equation, its coefficient and standard error are about -.00040 (se ≈ .06104). This is practically very small and the t statistic is almost zero. There has been no change in the union wage premium over time.(vii) Parts (v) and (vi) are not at odds. They imply that while the economic return to union membership has not changed (assuming we think we have estimated a causal effect), the fraction of people reaping those benefits has fallen.13.9 (i) Other things equal, homes farther from the incinerator should be worth more, so δ1 > 0. If β1 > 0, then the incinerator was located farther away from more expensive homes.(ii) The estimated equation islog()price= 8.06 -.011 y81+ .317 log(dist) + .048 y81⋅log(dist)(0.51) (.805) (.052) (.082)n = 321, R2 = .396, 2R = .390.ˆδ = .048 is the expected sign, it is not statistically significant (t statistic ≈ .59).While1(iii) When we add the list of housing characteristics to the regression, the coefficient ony81⋅log(dist) becomes .062 (se = .050). So the estimated effect is larger – the elasticity of price with respect to dist is .062 after the incinerator site was chosen – but its t statistic is only 1.24. The p-value for the one-sided alternative H1: δ1 > 0 is about .108, which is close to being significant at the 10% level.13.10 (i) In addition to male and married, we add the variables head, neck, upextr, trunk, lowback, lowextr, and occdis for injury type, and manuf and construc for industry. The coefficient on afchnge⋅highearn becomes .231 (se ≈ .070), and so the estimated effect and t statistic are now larger than when we omitted the control variables. The estimate .231 implies a substantial response of durat to the change in the cap for high-earnings workers.(ii) The R-squared is about .041, which means we are explaining only a 4.1% of the variation in log(durat). This means that there are some very important factors that affect log(durat) that we are not controlling for. While this means that predicting log(durat) would be very difficultˆδ: it could still for a particular individual, it does not mean that there is anything biased about1be an unbiased estimator of the causal effect of changing the earnings cap for workers’ compensation.(iii) The estimated equation using the Michigan data is112durat= 1.413 + .097 afchnge+ .169 highearn+ .192 afchnge⋅highearn log()(0.057) (.085) (.106) (.154)n = 1,524, R2 = .012.The estimate of δ1, .192, is remarkably close to the estimate obtained for Kentucky (.191). However, the standard error for the Michigan estimate is much higher (.154 compared with .069). The estimate for Michigan is not statistically significant at even the 10% level against δ1 > 0. Even though we have over 1,500 observations, we cannot get a very precise estimate. (For Kentucky, we have over 5,600 observations.)13.11 (i) Using pooled OLS we obtainrent= -.569 + .262 d90+ .041 log(pop) + .571 log(avginc) + .0050 pctstu log()(.535) (.035) (.023) (.053) (.0010) n = 128, R2 = .861.The positive and very significant coefficient on d90 simply means that, other things in the equation fixed, nominal rents grew by over 26% over the 10 year period. The coefficient on pctstu means that a one percentage point increase in pctstu increases rent by half a percent (.5%). The t statistic of five shows that, at least based on the usual analysis, pctstu is very statistically significant.(ii) The standard errors from part (i) are not valid, unless we thing a i does not really appear in the equation. If a i is in the error term, the errors across the two time periods for each city are positively correlated, and this invalidates the usual OLS standard errors and t statistics.(iii) The equation estimated in differences islog()∆= .386 + .072 ∆log(pop) + .310 log(avginc) + .0112 ∆pctsturent(.037) (.088) (.066) (.0041)n = 64, R2 = .322.Interestingly, the effect of pctstu is over twice as large as we estimated in the pooled OLS equation. Now, a one percentage point increase in pctstu is estimated to increase rental rates by about 1.1%. Not surprisingly, we obtain a much less precise estimate when we difference (although the OLS standard errors from part (i) are likely to be much too small because of the positive serial correlation in the errors within each city). While we have differenced away a i, there may be other unobservables that change over time and are correlated with ∆pctstu.(iv) The heteroskedasticity-robust standard error on ∆pctstu is about .0028, which is actually much smaller than the usual OLS standard error. This only makes pctstu even more significant (robust t statistic ≈ 4). Note that serial correlation is no longer an issue because we have no time component in the first-differenced equation.11311413.12 (i) You may use an econometrics software package that directly tests restrictions such as H 0: β1 = β2 after estimating the unrestricted model in (13.22). But, as we have seen many times, we can simply rewrite the equation to test this using any regression software. Write the differenced equation as∆log(crime ) = δ0 + β1∆clrprc -1 + β2∆clrprc -2 + ∆u .Following the hint, we define θ1 = β1 - β2, and then write β1 = θ1 + β2. Plugging this into the differenced equation and rearranging gives∆log(crime ) = δ0 + θ1∆clrprc -1 + β2(∆clrprc -1 + ∆clrprc -2) + ∆u .Estimating this equation by OLS gives 1ˆθ= .0091, se(1ˆθ) = .0085. The t statistic for H 0: β1 = β2 is .0091/.0085 ≈ 1.07, which is not statistically significant.(ii) With β1 = β2 the equation becomes (without the i subscript)∆log(crime ) = δ0 + β1(∆clrprc -1 + ∆clrprc -2) + ∆u= δ0 + δ1[(∆clrprc -1 + ∆clrprc -2)/2] + ∆u ,where δ1 = 2β1. But (∆clrprc -1 + ∆clrprc -2)/2 = ∆avgclr .(iii) The estimated equation islog()crime ∆ = .099 - .0167 ∆avgclr(.063) (.0051)n = 53, R 2 = .175, 2R = .159.Since we did not reject the hypothesis in part (i), we would be justified in using the simplermodel with avgclr . Based on adjusted R -squared, we have a slightly worse fit with the restriction imposed. But this is a minor consideration. Ideally, we could get more data to determine whether the fairly different unconstrained estimates of β1 and β2 in equation (13.22) reveal true differences in β1 and β2.13.13 (i) Pooling across semesters and using OLS givestrmgpa = -1.75 -.058 spring+ .00170 sat- .0087 hsperc(0.35) (.048) (.00015) (.0010)+ .350 female- .254 black- .023 white- .035 frstsem(.052) (.123) (.117) (.076)- .00034 tothrs + 1.048 crsgpa- .027 season(.00073) (0.104) (.049)n = 732, R2 = .478, 2R = .470.The coefficient on season implies that, other things fixed, an athlete’s term GPA is about .027 points lower when his/her sport is in season. On a four point scale, this a modest effect (although it accumulates over four years of athletic eligibility). However, the estimate is not statistically significant (t statistic ≈-.55).(ii) The quick answer is that if omitted ability is correlated with season then, as we know form Chapters 3 and 5, OLS is biased and inconsistent. The fact that we are pooling across two semesters does not change that basic point.If we think harder, the direction of the bias is not clear, and this is where pooling across semesters plays a role. First, suppose we used only the fall term, when football is in season. Then the error term and season would be negatively correlated, which produces a downward bias in the OLS estimator of βseason. Because βseason is hypothesized to be negative, an OLS regression using only the fall data produces a downward biased estimator. [When just the fall data are used, ˆβ = -.116 (se = .084), which is in the direction of more bias.] However, if we use just the seasonspring semester, the bias is in the opposite direction because ability and season would be positive correlated (more academically able athletes are in season in the spring). In fact, using just theβ = .00089 (se = .06480), which is practically and statistically equal spring semester gives ˆseasonto zero. When we pool the two semesters we cannot, with a much more detailed analysis, determine which bias will dominate.(iii) The variables sat, hsperc, female, black, and white all drop out because they do not vary by semester. The intercept in the first-differenced equation is the intercept for the spring. We have∆= -.237 + .019 ∆frstsem+ .012 ∆tothrs+ 1.136 ∆crsgpa- .065 seasontrmgpa(.206) (.069) (.014) (0.119) (.043) n = 366, R2 = .208, 2R = .199.Interestingly, the in-season effect is larger now: term GPA is estimated to be about .065 points lower in a semester that the sport is in-season. The t statistic is about –1.51, which gives a one-sided p-value of about .065.115(iv) One possibility is a measure of course load. If some fraction of student-athletes take a lighter load during the season (for those sports that have a true season), then term GPAs may tend to be higher, other things equal. This would bias the results away from finding an effect of season on term GPA.13.14 (i) The estimated equation using differences is∆= -2.56 - 1.29 ∆log(inexp) - .599 ∆log(chexp) + .156 ∆incshrvote(0.63) (1.38) (.711) (.064)n = 157, R2 = .244, 2R = .229.Only ∆incshr is statistically significant at the 5% level (t statistic ≈ 2.44, p-value ≈ .016). The other two independent variables have t statistics less than one in absolute value.(ii) The F statistic (with 2 and 153 df) is about 1.51 with p-value ≈ .224. Therefore,∆log(inexp) and ∆log(chexp) are jointly insignificant at even the 20% level.(iii) The simple regression equation is∆= -2.68 + .218 ∆incshrvote(0.63) (.032)n = 157, R2 = .229, 2R = .224.This equation implies t hat a 10 percentage point increase in the incumbent’s share of total spending increases the percent of the incumbent’s vote by about 2.2 percentage points.(iv) Using the 33 elections with repeat challengers we obtain∆= -2.25 + .092 ∆incshrvote(1.00) (.085)n = 33, R2 = .037, 2R = .006.The estimated effect is notably smaller and, not surprisingly, the standard error is much larger than in part (iii). While the direction of the effect is the same, it is not statistically significant (p-value ≈ .14 against a one-sided alternative).13.15 (i) When we add the changes of the nine log wage variables to equation (13.33) we obtain116117 log()crmrte ∆ = .020 - .111 d83 - .037 d84 - .0006 d85 + .031 d86 + .039 d87(.021) (.027) (.025) (.0241) (.025) (.025)- .323 ∆log(prbarr ) - .240 ∆log(prbconv ) - .169 ∆log(prbpris )(.030) (.018) (.026)- .016 ∆log(avgsen ) + .398 ∆log(polpc ) - .044 ∆log(wcon )(.022) (.027) (.030)+ .025 ∆log(wtuc ) - .029 ∆log(wtrd ) + .0091 ∆log(wfir )(0.14) (.031) (.0212)+ .022 ∆log(wser ) - .140 ∆log(wmfg ) - .017 ∆log(wfed )(.014) (.102) (.172)- .052 ∆log(wsta ) - .031 ∆log(wloc ) (.096) (.102) n = 540, R 2 = .445, 2R = .424.The coefficients on the criminal justice variables change very modestly, and the statistical significance of each variable is also essentially unaffected.(ii) Since some signs are positive and others are negative, they cannot all really have the expected sign. For example, why is the coefficient on the wage for transportation, utilities, and communications (wtuc ) positive and marginally significant (t statistic ≈ 1.79)? Higher manufacturing wages lead to lower crime, as we might expect, but, while the estimated coefficient is by far the largest in magnitude, it is not statistically different from zero (tstatistic ≈ –1.37). The F test for joint significance of the wage variables, with 9 and 529 df , yields F ≈ 1.25 and p -value ≈ .26.13.16 (i) The estimated equation using the 1987 to 1988 and 1988 to 1989 changes, where we include a year dummy for 1989 in addition to an overall intercept, isˆhrsemp ∆ = –.740 + 5.42 d89 + 32.60 ∆grant + 2.00 ∆grant -1 + .744 ∆log(employ ) (1.942) (2.65) (2.97) (5.55) (4.868)n = 251, R 2 = .476, 2R = .467.There are 124 firms with both years of data and three firms with only one year of data used, for a total of 127 firms; 30 firms in the sample have missing information in both years and are not used at all. If we had information for all 157 firms, we would have 314 total observations in estimating the equation.(ii) The coefficient on grant – more precisely, on ∆grant in the differenced equation – means that if a firm received a grant for the current year, it trained each worker an average of 32.6 hoursmore than it would have otherwise. This is a practically large effect, and the t statistic is very large.(iii) Since a grant last year was used to pay for training last year, it is perhaps not surprising that the grant does not carry over into more training this year. It would if inertia played a role in training workers.(iv) The coefficient on the employees variable is very small: a 10% increase in employ increases hours per employee by only .074. [Recall:∆≈ (.744/100)(%∆employ).] Thishrsempis very small, and the t statistic is also rather small.13.17. (i) Take changes as usual, holding the other variables fixed: ∆math4it = β1∆log(rexpp it) = (β1/100)⋅[ 100⋅∆log(rexpp it)] ≈ (β1/100)⋅( %∆rexpp it). So, if %∆rexpp it = 10, then ∆math4it= (β1/100)⋅(10) = β1/10.(ii) The equation, estimated by pooled OLS in first differences (except for the year dummies), is4∆ = 5.95 + .52 y94 + 6.81 y95- 5.23 y96- 8.49 y97 + 8.97 y98math(.52) (.73) (.78) (.73) (.72) (.72)- 3.45 ∆log(rexpp) + .635 ∆log(enroll) + .025 ∆lunch(2.76) (1.029) (.055)n = 3,300, R2 = .208.Taken literally, the spending coefficient implies that a 10% increase in real spending per pupil decreases the math4 pass rate by about 3.45/10 ≈ .35 percentage points.(iii) When we add the lagged spending change, and drop another year, we get4∆ = 6.16 + 5.70 y95- 6.80 y96- 8.99 y97 +8.45 y98math(.55) (.77) (.79) (.74) (.74)- 1.41 ∆log(rexpp) + 11.04 ∆log(rexpp-1) + 2.14 ∆log(enroll)(3.04) (2.79) (1.18)+ .073 ∆lunch(.061)n = 2,750, R2 = .238.The contemporaneous spending variable, while still having a negative coefficient, is not at all statistically significant. The coefficient on the lagged spending variable is very statistically significant, and implies that a 10% increase in spending last year increases the math4 pass rate118119 by about 1.1 percentage points. Given the timing of the tests, a lagged effect is not surprising. In Michigan, the fourth grade math test is given in January, and so if preparation for the test begins a full year in advance, spending when the students are in third grade would at least partly matter.(iv) The heteroskedasticity-robust standard error for log() ˆrexpp β∆is about 4.28, which reducesthe significance of ∆log(rexpp ) even further. The heteroskedasticity-robust standard error of 1log() ˆrexpp β-∆is about 4.38, which substantially lowers the t statistic. Still, ∆log(rexpp -1) is statistically significant at just over the 1% significance level against a two-sided alternative.(v) The fully robust standard error for log() ˆrexpp β∆is about 4.94, which even further reducesthe t statistic for ∆log(rexpp ). The fully robust standard error for 1log() ˆrexpp β-∆is about 5.13,which gives ∆log(rexpp -1) a t statistic of about 2.15. The two-sided p -value is about .032.(vi) We can use four years of data for this test. Doing a pooled OLS regression of ,1垐 on it i t r r -,using years 1995, 1996, 1997, and 1998 gives ˆρ= -.423 (se = .019), which is strong negative serial correlation.(vii) Th e fully robust “F ” test for ∆log(enroll ) and ∆lunch , reported by Stata 7.0, is .93. With 2 and 549 df , this translates into p -value = .40. So we would be justified in dropping these variables, but they are not doing any harm.。
小学上册第九次英语第6单元综合卷
小学上册英语第6单元综合卷英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1.My aunt loves to do ____ (yoga) every morning.2.The _____ (蛋糕) is delicious.3. A ______ is a visual representation of a chemical equation.4.I always have ______ for dinner.5.I love to draw ______.6.My sister is a ______. She enjoys baking cookies.7.What is the capital city of the United States?A. New YorkB. Los AngelesC. Washington,D.C. D. ChicagoC8.The _____ (frost) can damage young plants.9.My _____ (外甥) loves to play video games.10.The snapping turtle can bite very _________ (痛).11. Carta influenced the development of modern ________. The Magn12.My grandma bakes the best ________ (饼干). I help her mix the ________ (材料).13. A __________ is a reaction that involves a change in color.14.What do we call the act of providing opportunities for success?A. EmpowermentB. SupportC. PromotionD. All of the AboveD15. A __________ is a change in the physical properties of a substance.16.What do you call the process of providing nutrients to plants?A. FertilizationB. IrrigationC. CultivationD. Planting17.The chemical symbol for zinc is ______.18.The _____ (车子) is parked outside.19.The concept of conservation emphasizes the importance of protecting ______ resources.20.In a chemical reaction, the total mass of the reactants equals the total mass of the _____.21.The ________ is a type of insect that helps plants.22.The _____ can affect the tides on Earth.23. A ____ is often found in gardens and is known for its beautiful colors.24.The _____ (老师) is teaching us.25.The city of Riyadh is the capital of _______.26.My ___ (小仓鼠) keeps its cheeks full of food.27.The cat is very ___ (lazy/energetic).28.My friend is very __________ (适应性强).29.What do we call a baby dog?A. KittenB. PuppyC. CalfD. Chick30.The ________ (气候适应) is necessary for survival.31.What is the primary function of the heart?A. Pump bloodB. Digest foodC. Filter airD. Protect the body32.I like to ride my ______ (horse).33.How many sides does a square have?A. FourB. FiveC. SixD. Seven34.What do you call a tall structure used for climbing?A. TowerB. HillC. MountainD. Cliff35.My favorite vegetable is ______.36.How many days are there in a week?A. FiveB. SixC. SevenD. EightC37.We will have a ________ next week.38.What is the name of the famous landmark in Egypt?A. Taj MahalB. ColosseumC. Great Pyramid of GizaD. Eiffel TowerC39.She likes to eat ___ (apples/rocks).40.What is the capital of Italy?A. RomeB. MilanC. FlorenceD. VeniceA41.The ______ (果皮) protects the fruit inside.42.The monkey loves to eat ______.43.What is the main ingredient in chocolate?A. MilkB. CocoaC. SugarD. FlourB44.The Earth's atmosphere is vital for protecting ______ life.45.The __________ (历史的展望) inspires hope.46.What is the name of the superhero who wears a cape and can fly?A. Spider-ManB. BatmanC. SupermanD. Iron ManC47.Listen and number.听录音排序。
中国人群尘肺病疾病负担的系统评价
. 276 .CHINESE JOURNAL OF EVIDENCE-BASED MEDICINE, Mar. 2021, Vol. 21, No.3•论著•二次研究•中国人群尘肺病疾病负担的系统评价张瞾慧'黄磊'石璐\况杰1>3,周小军1>31. 南昌大学公共卫生学院流行病学教研室（南昌330006)2. 四川大学华西公共卫生学院/四川大学华西第四医院（成都610041)3. 江西省预防医学重点实验室（南昌330006)【摘要】目的系统评价中国人群尘肺病疾病负担情况，为制定有效的尘肺病防控对策提供科学依据。
方法计算机检索PubMed、EBSCO、Web of Science、CNKI、WanFang D ata和V IP数据库，搜集以尘肺患者疾病负担为主题的研究文献，检索时限均从建库至2020年1月31日。
由2位评价员独立筛选文献、提取资料并评价纳人研究的偏倚风险后，对纳人研究的尘肺病相关人口、死亡和疾病负担进行系统评价。
结果共纳人26个研究。
定性分析结果显?K:近10年中国人群尘肺病的伤残调整寿命年（disability adjusted life years, DALY)和过早死亡损失寿命年（year of life lost, YLL)降幅低于全球，伤残损失寿命年（year lived with disability, YLD)升幅高于全球，Y L D所占D A L Y比重呈上升趋势=在尘肺病患者经济负担或住院费用的影响因素分析中包含14个因素，其中住院天数、相关合并症、尘肺分期是对患者经济负担（或住院费用）有影响或存在差异最重要的指标。
中国人群因尘肺病引起的疾病负担主要集中在中老年男性人群，但年轻患者因发病年龄早、病程长及合并症/并发症等因素造成其Y LD更大的现象也应重视。
结论我国尘肺患者疾病负担仍然沉重，建议将持续降低中国人群尘肺病的DALY作为长期健康管理目标，强化遏制尘肺病早发病和早死亡的控制策略。
2022年考研考博-考博英语-厦门大学考试全真模拟易错、难点剖析AB卷(带答案)试题号：51
2022年考研考博-考博英语-厦门大学考试全真模拟易错、难点剖析AB卷（带答案）一.综合题(共15题)1.单选题Changing from solid to liquid, water takes in heat from all substances near it and this_______produces artificial cold surrounding it.问题1选项A.absorptionB.transitionC.consumptionD.interaction【答案】A【解析】absorption吸收; transition过渡, 转变; consumption消费, 消耗; interaction相互作用。
句意：水从固体变成液体, 会吸收附近所有物质的热量, 这种吸收会在周围产生人工寒潮。
选项A符合句意。
2.单选题The British historian Niall Ferguson speculated that the end of American_______might not fuel an orderly shift to a multipolar system.问题1选项A.domainB.hegemonyC.sovereigntyD.preference【答案】B【解析】domain领地,领域; hegemony霸权; sovereignty主权,君主; preference偏爱, 优先权。
句意：英国历史学家Niall Ferguson推测, 美国霸权主义的终结可能不会推动美国向多极体系的有序转变。
选项B符合句意。
3.翻译题(1). When we talk about the danger of romantic love, we don't mean danger in the obvious heartbreak way—the cheap betrayals, the broken promises—we mean the dark danger that lurks when sensible, educated women fall for the dogmatic idea that romantic love is the ultimate goal for the modern female. Every day, thousands of films, books, articles and TV programs hammer home this message—that without romance, life is somehow barren.However, there are women who entertain the subversive notion, like an intellectual mouse scratching behind the skirting board, that perhaps this higher love is not necessarily the celestial highway to absolute happiness. (2). Their empirical side kicks in. and they observe that couples who marry in a haze of adoration and sex are, ten years later, throwing china and fight bitterly over who gets the dog.(3). But the women who notice these contradictions are often afraid to speak them in case they should be labeled cynics. Surely only the most jaded and damaged would challenge the orthodoxy of romantic love. The received wisdom that there is not something wrong with the modern idea of sexual love as ultimate panacea, but (hat if you don't get it, there is something wrong with you. You freak, go back and read the label. (4).We say the privileging of romantic love over all others, the insistence that it is the one essential, incontrovertible element of human happiness, traced all the way back to the caves, is a trap and a snare. The idea that every human heart, since the invention of the wheel, was yearning for its other half is a myth.(5). Love is a human constant: it is the interpretation of it that changes. The way that love has been expressed, its significance in daily life, have never been immutable or constant. The different kinds of love and what they signify are not fixed, whatever the traditionalists may like to tell you.So the modern idea that romantic love is a woman's highest calling, that she is somehow only half a person without it, that if she questions it she is going against all human history, does not stand up to scrutiny. It is not an imperative carved in stone; it is a human idea, and human beings are frail and suggestible, and sometimes get the wrong end of the stick.Read the passage carefully and translate the underlined sentences into Chinese.【答案】1.当说到浪漫爱情的危险时, 我们并不是指显而易见令人心碎的危险一可耻的背叛、破碎的誓言——而是指当明智的知识女性对教条主义思想信以为真, 即浪漫的爱情是现代女性的终极目标时, 潜伏着的隐秘危险。
高三英语文章全球问题单选题50题
高三英语文章全球问题单选题50题1.Global warming is causing the sea level to rise. Which of the following is NOT a possible consequence?A.Loss of coastal landB.Increased frequency of hurricanesC.Decrease in air pollutionD.Disruption of marine ecosystems答案：C。
全球变暖导致海平面上升，会造成沿海土地流失、飓风频率增加以及海洋生态系统被破坏。
而海平面上升不会直接导致空气污染减少。
2.The depletion of the ozone layer is mainly caused by________.A.emission of carbon dioxideB.release of chlorofluorocarbonsC.burning of fossil fuelsD.cutting down of forests答案：B。
臭氧层的消耗主要是由释放氟氯烃引起的。
二氧化碳排放、燃烧化石燃料主要导致全球变暖；砍伐森林主要影响生态平衡等方面。
3.Which of the following is a measure to address climate change?A.Increasing the use of plastic bagsB.Building more coal-fired power plantsC.Planting more treesD.Dumping waste into the ocean答案：C。
种植更多的树可以吸收二氧化碳，有助于应对气候变化。
增加塑料袋使用、建设更多的燃煤发电厂、向海洋倾倒废物都会加剧环境问题。
4.Deforestation can lead to________.A.increased biodiversityB.more fertile soilC.flooding and soil erosionD.cooler climate答案：C。
海航笔试题目及答案
一、单选题（共90题，每题1分）1、依次填入下列各句横线处的词语，最恰当的一组是①逐步推广使用清洁的可再生能源，减少使用污染环境的能源，是环境恶化的正确选择。
②随着人们自律程度的不断提高，过去有些需要用铁栏杆来维持的地方，现在只要拉绳或画线就行了。
③具有世界影响的中国画大师张干千，人物、花鸟、鱼虫、走兽无一不精，尤其画山水A、遏制次序善于B、遏制秩序擅长C、遏止秩序擅长D、遏止次序善于2、下列各句中，语义明确、没有歧义的一句是A、天吃饭，大家一律不准用筷子。
B、女工工作做得好，可以解决一些女工特有的切身问题。
C、领导对群众反映个别职员玩忽职守的问题十分气愤。
D、方在钓鱼岛问题上的种种错误做法，使中日关系正常发展受到严重干扰。
3、下列没有错别字的一句是：A、我能成为你人生道路上的诤友，感到十分荣幸。
B、截止今年10月底，他已完成了全年的生产任务。
C、经有关部门认真鉴定，这些东西全是膺品。
D、新建的城市应按现代化的要求来安排市政设施。
4、以下哪一位是现任的俄罗斯总统？A、普京B、就加诺夫C、库德林D、梅德韦杰夫5、世界上最长的跨海大桥，也是世界上建造难度最大的跨海大桥之一的是A、杭州湾跨海大桥B、澎湖跨海大桥C、澳门跨海大桥D、青岛跨海大桥6、地热资源、太阳能、水能资源均丰富的地区是：A、青藏高原B、海南岛C、塔里木盆地D、四川盆地7、日本党首在2009年9月16日下午举行的特别国会首相指名选举中当选为日本新一任首相。
A、自民党鸠山由纪夫B、民主党麻生太郎C、民主党鸠山由纪夫D、自民党麻生太郎8、美国次贷危机中的“次”是指：A、贷款人的第二次贷款B、贷款人的收入较低，信用等级较低C、贷款机构的实力和规模较小D、贷款机构的信用等级较低9、澳大利亚的首都是下列哪座城市：A、堪培拉B、墨尔本C、布里斯班D、悉尼10、2009年10月31日上午，我国科学巨星、被誉为中国航天之父的逝世于北京，享年98岁。
A、钱钟书B、闫怀礼C、钱学森D、林耀基11、2009年秋季，我国第十一届全国运动会在省顺利举办。
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The influence of fuel bias in the primary air duct on thegas/particle flow characteristics near the swirl burner regionZhichao Chen a,b,⁎,Zhengqi Li a ,Jianping Jing a ,Fuqiang Wang a ,Lizhe Chen a ,Shaohua Wu aa School of Energy Science and Engineering,Harbin Institute of Technology,92,West Dazhi Street,Harbin 150001,PR China bPostdoctoral Station of Civil Engineering,Harbin Institute of Technology,Harbin 150090,PR ChinaA R T I C L E I N F OA B S T R A C TArticle history:Received 12July 2007Received in revised form 10March 2008Accepted 24March 2008A three-component particle-dynamics anemometer is used to measure,in the near-burner region,the influence of the particle bias in the primary air duct on the gas/particle flow characteristics for a centrally fuel rich swirl coal combustion burner,in conjunction with a gas/particle two-phase test facility.Velocities,particle volume flux profiles and normalized particle number concentrations were pared with a common burner (a centrally fuel rich burner without a particle concentrator),the degree of penetration for the centrally fuel rich burner is higher,the residence time of particles in the central recirculation zone is longer and the central recirculation zone is larger.The particle volume flux and normalized particle number concentration for the centrally fuel rich burner are much larger near the chamber axis.The influence of gas/particle flow characteristics on combustion has been analyzed.©2008Elsevier B.V.All rights reserved.Keywords:BurnerParticle concentrator Gas/particle flow Coal combustionThree-dimensional particle-dynamics anemometer1.IntroductionMuch low grade coal of low calorific value is used in power plants in China.It has either a small amount of volatile matter or high moisture and/or ash content.Generally the flame from these coals is not stable.The power industry requires coal combustion techniques that show flame stability,no slagging propensity and high combustion efficiency that also meet pollution control standards.To reduce NO x emissions,low NO x combustion technologies were developed [1–3].A low NO x burner is the most effective method for reducing NO x emis-sions.The quality of coal provided to power plants often fluctuates and is usually low grade in China.When burning low grade coal,it is difficult for low NO x burners that are designed to burn high-grade coal to meet power industry requirements such as flame stability and no slagging propensity [4–6].Some experiments have shown that increasing the fuel concentration within a defined range can increase the flame velocity [7–9].It has become possible to significantly reduce their emissions via combustion process modifications,by maintaining sequentially fuel rich and fuel-lean combustion zones in a burner flame or in the combustion chamber,or by injecting a hydrocarbon rich fuel into the NO x containing combustion products of a primary fuel such as coal [10].Zhang et al.[11]investigated the effect of a coal concentrator on NO formation in swirling coal combustion using both numerical simulation and experiments.The combustion mod-eling results indicate that although the coal concentrator increases the turbulence and combustion temperature,it can remarkably reduce the NO formation due to the creation of a high coal concentration in the recirculation zone.Calculated results of swirling pulverized-coal combustion indicate theF U E L P R O C E S S I NG T E CH N O L O G Y 89(2008)958–965⁎Corresponding author.School of Energy Science and Engineering,Harbin Institute of Technology,92,West Dazhi Street,Harbin 150001,PR China.Tel.:+8645186413231;fax:+8645186412528.E-mail address:chenzc@ (Z.Chen).0378-3820/$–see front matter ©2008Elsevier B.V.All rights reserved.doi:10.1016/j.fuproc.2008.03.005a v a i l ab l e a t w w w.sc i e n c ed i re c t.c o mw w w.e l s e v i e r.c o m /l o c a t e /f u p r o cpulverized-coal concentrator has a strong effect on coal combustion and NO x formation,increasing the combustion rate of coal and decreasing the NO formation in the fuel [12].Wei et al.[13]investigated fuel rich/lean pulverized-coal combustion in a tangentially fired furnace burning low volatile fuel.Fuel rich/lean streams cannot only improve the ignition and burnout of low volatile coal but may also reduce NO x emission.Li et al.[14–16]investigated the gas/particle flows for a radial bias combustion swirl burner using a three-dimensional particle-dynamics anemometer (PDA).Zhou and Cen [17]investigated the effect of solid concentration on the concentra-tion performance of a collision-block-type fuel rich/lean burner using a fiber-optic measurement system in a two-phase flow test facility.To solve the above problems,Li and co-workers [18]proposed a new burner,the centrally fuel rich swirl coal combustion burner (Fig.1),in 2003.Both the inner and outer secondary airs are swirling.The primary air –coal mixture duct is at the center of the burner and the primary air is non-swirling.Cone separators are installed in the primary air –coal mixture duct to concentrate the pulverized coal into the central zone of the burner.The main factors that determine an accurate concentration value are the setting parameters of the PDA system,the properties of particles and particle concentration.In the experiment,the degree of sphericity of glass beads employed as seeds is larger than 95%,which is advantageous in obtaining an accurate concentration.The fuel rich primary air particle mass concentration was 0.20kg (fuel)/kg,which corresponds todispersed two-phase flows.Experiment indicates the PDA technique is suitable for spherical particle concentration and local flux measurements in dispersed two-phase flows [19].In the same cross-section,the setting parameters of the PDA system are the same.It is virtually impossible to replicate all the physical and chemical processes of a full-sized industrial burner in a scaled down model used in research.On the other hand,it would be too expensive to do experiments in a full-sized burner.However,results from a great number of different small scale cold-flow tests compared to those of full-size burner tests show reliable predictions can be made from the scaled down model tests [20–22].For example,Pickett et al.[22]found the velocity profiles for reacting flow showed similar trends and patterns to those observed in cold-flow experiments.The PDA is an instrument based on phase Doppler anemo-metry,which is an extension of laser Doppler anemometry (LDA).The velocity is measured from the frequency of the Doppler burst as for LDA [22].Using PDA,the velocity,size and concentration of two-phase flow can be measured [19,23–25].Chen et al.discussed the particle volume flux in different cross-sections of the CFR burner,the radial bias combustion burner and volute burners [26].A three-dimensional PDA system was used to study the gas/particle flow characteristics of a centrally fuel rich and common (centrally fuel rich without a pulverized-coal concentrator)burners.2.ExperimentalA three-dimensional PDA made by Dantec was used in this study.The instrument includes an argon ion laser,a transmitter,fiber optics,receiver optics,signal processors,a traversing system,a computer system and a three-dimensional auto-coordinated rack.The PDA uses the proven phase Doppler principle for simulta-neous non-intrusive and real-time measurements of three velocity components and turbulence characteristics,and makes use of new methods for phase differences between Doppler signals received by three detectors located in different positions.The instrument uses 60×fiber flow optics and 57×10PDA receiver optics.Several optical configurations from 0to 500mm are radially available.All instrument settings,such as bandwidth and voltage,are computer controlled.An analog –digitalconverterFig.1–Centrally fuel richburner.Fig.2–Schematic drawing of test facility.1.Wind box.2.Valve.3.Electronic scale.4.Feeder.5.Particle reservoir.6.Burner model.7.Test chamber.8.Platform.9.Equalizing hole.10.Bracket.11.Cyclone separator.12.Air lock.13.Suction pump.959F U E L P R O C E S S I NG T E CH N O L O G Y 89(2008)958–965allows the computer to read the anode current of photomulti-pliers.The combination of photomultiplier and particle velocity correlation bias can contribute to uncertainty,but the error is likely to be small.Overall uncertainties for measured values of the mean velocity,particle diameter and particle volume flux are1%, 4%and30%respectively,and the measurable ranges for size and velocity are0.5–1000µm and−500to500m/s,respectively.The test facility is illustrated in Fig.2.It consists of a suction device,feeder,burner model,test chamber and cyclone separator. An air flow is induced from the wind box to the burner by the suction device.Glass beads are fed via the feeder into the fuel rich primary air duct.The density of the glass beads used was2500kg/m3.The particle size distribution obtained by the PDA is shown in Fig.3.The mean diameter was42µm.Because coal particles cannot meet the steradian and reflectance characteristics required for PDA particle measurements,glass beads,which do meet these requirements, were used instead.The full industrial-scale centrally fuel rich burner studied in the experiments was designed for a1025t/h coal-fired boiler.For installation in the test facility,the burner had to be scaled.The model's geometric sizes and operational parameters were obtained using scaling criteria:(1)geometric similarity;(2)second-ary self-modeling flows;(3)boundary condition similarity;(4) material similarity;(5)unaltered momentum ratios with scale reduction.A scale ratio of1:7was employed for two model burners (Fig.4).No concentrator was mounted in the centrally fuel rich burner model and glass beads were fed only into the fuel rich duct. This simulates the extreme case in which particles in the primary air are all concentrated into the central zone of the burner.Other than a fuel rich primary air duct installed in the centrally fuel rich burner,the structures of the two burners are the same.The fuel rich and fuel-lean primary air velocities for the centrally fuel rich burner and the primary air velocity for the common burner were 10.0m/s,the inner secondary air velocity for the two burners was 10.58m/s,and the outer secondary air velocity for both burners was16.07m/s.The primary air particle mass concentration,which is defined as the ratio of particle mass flow rate to primary air mass flow rate,was0.20kg(fuel)/kg(air).During the experiment some of the smaller particles were lost due to the low efficiency of the cyclone separator.The particle material had to be frequently renewed to maintain the same particle size distribution as closely as possible.The ratio of the test chamber diameter to the nozzle diameter of the secondary air outflow was4.83.Ratios greater than3are considered to be low-confinement flows[27].Particles with diameters of0–8µm were used to trace the air flow,and particles with diameters of10–100µm were used to represent the particle phase flow.Particles with diameters of0–100µm were used for analysis of the particle volume flux and normalized particle number concentration. Owing to uncertainties in the determination of the cross-section of the control volume,the measured mass flux was corrected using the global mass balance.Therefore,the total particle mass flow rate at the inlet was obtained by integrating the mass flux profile.The global mass flow rate was obtained by weighing particles collected during a certain time period.In addition,a correction factor was applied to the mass flux measurements for all other cross-sections[13–15,27].3.Results and discussionGas/particle flow characteristics were measured for cross-sections of x/d=0.1,0.3,0.5,0.7,1.0,1.5,and2.5,where x is the distance to the exit of the burner along the jet flow direction (Fig.4),and d is the diameter of the outer secondary air duct, which is∅176mm.3.1.Gas/particle velocitiesFig.5shows profiles of the gas/particle mean axial velocities for the two burners.From the burner jet to the x/d=0.7cross-sections,there are two peaks in the profiles:the peak near the chamber axis is the primary flow zone for the gas/particle mixture and the other near the wall is the secondary airflow zone.The peak near the chamber axis is always greater than that near the wall.With diffusion of the primary gas/particle mixture into the secondary air and diffusion of thesecondaryFig.4–Detail of the model burners'jets.Fig.3–Particle size distributions.960F U E L P R O C E S S I N G T E C H N O L O G Y89(2008)958–965air towards the wall,both peaks gradually decrease and the peak value near the wall move towards the wall.All particles in the primary air are concentrated into the fuel rich primary duct for the centrally fuel rich burner,which results in the fuel rich primary air momentum being higher than that for the common burner in the radius from 0–20mm,which is the radius of the fuel rich duct for the centrally fuel rich burner.Thus,in the cross-sections of x /d =0.1–0.5,in the radius range 0–20mm,the gas/particle mean axial velocities for the centrally fuel rich burner are larger than those for the common burner.Because no particles were fed into the fuel-lean primary air duct of the centrally fuel rich burner,the fuel-lean primary air momentum is less than that of the common burner in the radius from 20–40mm,which is the radius of the fuel-lean duct for the centrally fuel rich burner.The fuel-lean primary air is easily taken by the secondary air.Thus,in the cross-sections of x /d =0.1–0.5,the central recirculation zone for the centrally fuel rich burner is larger than the common zone.The primary air total momenta of the two burners are identical.With the development of the burner jet,the influence of the difference between the fuel rich primary air momentum for the centrally fuel rich burner and primary air momentum in the same radius range for the common burner on the gas/particle mean axial velocities is small.In cross-sections of x /d =0.7–2.5,profiles of the gas/particle mean axial velocities for the two burners are similar.Profiles of the gas/particle RMS axial velocities for the two burners are similar.In cross-sections of x /d =0.1–1.5,the profiles show two peaks.The two peaks indicate there is comparatively high axial turbulent diffusion in these regions.With jet development,the two peaks gradually decrease,the peak near the wall diffuses towards the wall and the profiles become flat.The primary gas/particle mixture for two burners partially penetrates the central recirculation zone and is then deflected radially.In the cross-sections of x /d =0.1–0.5,in the radius range 0–20mm,the gas/particle mean axial velocities for the centrally fuel rich burner are larger than those for the common burner.The degree of penetration (how far the primary air penetrates the central recirculation zone)for the centrally fuel rich burner is higher than that for the common burner.For the centrally fuel rich burner,the residence time of particles in the central recirculation is prolonged.The particle residence time in the central recirculation zone affects particle burnout in the burner region [28,29].The central recirculation zone consists of hot burned gases with a low amount of O 2.Devolatilization takes place in the zone and hydrocarbons compete with nitrogen for the available substoichiometric amount of O 2.In this reducing environment,NO formation is low and most of the reactive nitrogen is converted to N 2.Thus,the centrally fuel rich burner is advantageous in keeping a stable flame and inhibits the formation of fuel-NO x .Fig.6shows profiles of the gas/particle mean radial velocities for the two burners.From the burner jet to the x /d =0.7section,the profiles for the two burners show two peaks:the peak near the chamber axis is the primary gas/particle mixture flow zone,and the other near the wall is the secondary air flow zone.The peak near the wall is always greater than the peak near the chamber axis.With diffusion of the primary gas/particle mixture into the secondary air and diffusion of the secondary air towards the wall,the two peaks move towards the wall.With jet development,the profiles of the gas/particle mean radial velocities become flat.Near the chamber axis,from the burner jet to the x /d =0.7cross-section,the mean radial velocities are negative.This means the primary gas/particle mixture flows towards the chamber axis,which enhances the particle volume flux near the chamber axis.With the development of the burner jet,the influence of the difference between the fuel-lean primary air momentum for the centrally fuel rich burnerandFig.5–Profiles of gas/particle mean axial velocities for two burners.961F U E L P R O C E S S I NG T E CH N O L O G Y 89(2008)958–965that in the same radius range for the common burner in the same zone on the gas/particle mean radial velocities is small. Profiles of the gas/particle mean radial velocities for the two burners are similar.The fuel-lean primary air momentum for the centrally fuel rich burner is less than that of the common burner in the same zone.The fuel-lean primary air is easily taken by the secondary air.In the cross-sections of x/d=0.1–0.5,in the radius range20–40mm,the gas/particle mean radial velocities for the centrally fuel rich burner are larger than those for the common burner.Thus,compared with the case for the common burner,it is easier to form a central recirculation zone for the centrally fuel rich burner.Profiles of the gas/particle RMS radial velocities for two the burners are similar.In the cross-sections from x/d=0.1–0.5, there is a peak for the RMS radial velocities,whichindicates Fig.7–Profiles of gas/particle mean tangential velocities for twoburners.Fig.6–Profiles of gas/particle mean radial velocities for two burners.962F U E L P R O C E S S I N G T E C H N O L O G Y89(2008)958–965there is comparatively high radial turbulent diffusion in this region.With jet development,the secondary air diffuses towards the wall,the peak gradually decreases and profiles of the gas/particle RMS radial velocities become flat.Fig.7shows profiles of the gas/particle mean tangential velocities for the two burners.As the primary air is non-swirling,the mean tangential velocities for the two burners are relatively small in the x /d =0.1cross-section (r ≤40mm).In the x /d =0.3cross-section,the distribution of the mean tangential velocities is a Rankine-type vortex,which is a combination of a solid-body rotational core and a free vortex.Downstream from the x /d =0.5cross-section,the peak mean tangential velocities move towards the chamber axis,which indicates the gas/particle mixture near the chamber axis begins to swirl,driven by secondary air.With jet development,profiles of the gas/particle mean tangential velocities become flat.With the development of the burner jet,the influence of the difference between the fuel-lean primary air momentum for the centrally fuel rich burner and that in the same radius range for the common burner in the same zone on the gas/particle mean tangential velocities is small.Profiles of the gas/particle mean tangential velocities for the two burners are similar.Because the fuel-lean primary air momentum for the centrally fuel rich burner is less than that in the same radius range for the common burner in the same zone,the fuel-lean primary air is easily taken by the secondary air.Thus,in the cross-sections of x /d =0.3–1.0,in the radius range 20–350mm,the gas/particle mean tangential velocities for the centrally fuel rich burner are larger than those for the common burner.Profiles of the gas/particle RMS radial velocities for the two burners are similar.Gas/particle RMS tangential velocities are high,particular in the secondary zone in the x /d =0.1cross-section,where there are two peaks for the RMS tangentialvelocity in the inner and outer secondary air zones.This indicates there is high turbulent diffusion in the two zones.With diffusion of the secondary air towards the wall,the peaks gradually decrease and move towards the wall.3.2.Particle volume flux and normalized particle number concentrationFig.8shows profiles of the particle volume flux from 0–100µm in different cross-sections for the two burners,all of which have back flows near the wall.In the four cross-sections from x /d =0.1–0.7,the profiles of particle volume flux show two peaks for both burners.With movement of the secondary air towards the wall,the two peaks gradually decrease and the peak near the wall moves towards the wall.The peak near the chamber axis is the primary gas/particle mixture flow zone and the other near the wall is the secondary airflow zone.For the two burners,the primary gas/particle mixture partially penetrates the central recirculation zone and is then deflected radially.Because the fuel rich primary air momentum for the centrally fuel rich burner is higher than that for the common burners,the degree of penetration for the centrally fuel rich burner is higher than that for the common pared with the case for the common burner,in the four cross-sections from x /d =0.1–0.7,near the chamber axis,the particle volume flux for the centrally fuel rich burner is much larger and much closer to the chamber axis.In each cross-section,the particle volume flux for the centrally fuel rich burner in the central recirculation is much larger than that for the common burner.Fig.9shows normalized particle number concentration profiles for the centrally fuel rich burner,where C n is the particle number concentration at a given point and C nmax is the maximum number concentration in the samecross-Fig.8–Particle volume flux profiles for two burners.963F U E L P R O C E S S I NG T E CH N O L O G Y 89(2008)958–965section.In the three cross-sections from x /d =0.1to x /d =0.5,the profiles of normalized particle number concentration for both burners show a peak near the chamber axis.Because the mean diameter is different in the radius range in the same cross-section,the profiles of normalized particle number concentration are different from the profiles of the particle volume pared with the case for the common burner,the normalized particle number concentration for the centrally fuel rich burner is much closer to the chamber axis.In the six cross-sections from x /d =0.1to x /d =1.5,in the radius range 0–20mm,the normalized particle number concentration for the centrally fuel rich burner is larger than that for the common burner.The degree of blackness of the fuel stream increases with increasing fuel concentration,and as radiation heat absorbed from the high-temperature flame of the furnace increases,the gas temperature increases.For the centrally fuel rich burner,near the chamber axis,there is a large particle volume flux and high gas temperature,which exhibits intense heat convection with the central recirculation zone.This is advantageous for coal heating,firing and flame stability.The particle volume flux and particle diameter are large near the chamber axis,and large particles are mainly resident in the central recirculation zone,where the temperature is high.Thus,the performance of flame stability and burnout for the centrally fuel rich burner is better than that of the common burner.The particle volume flux in the central recirculation zone for the centrally fuel rich burner is larger than that for the common burner.The degree of penetration for the centrally fuel rich burner is higher than that for the common burner.The residence time of particles for the centrally fuel rich burner in the central recirculation zone is longer than that for the common burner.The central recirculation zone consists of hot burned gases with a low amount of O 2.Devolatilization takes place in the zone and hydrocarbons compete withnitrogen for the available substoichiometric amount of O 2.In this reducing environment,NO formation is low and most of the reactive nitrogen is converted to N 2.This inhibits the formation of fuel-NO x .Hence the centrally fuel rich burner is more advantageous in inhibiting the formation of fuel-NO x than is the common burner.Particles are ejected from the center of the centrally fuel rich burner.The particle volume flux for the centrally fuel rich burner is much larger near the chamber axis and the particle volume flux peak much closer to the chamber axis.This is advantageous for the formation of an oxidizing atmosphere near the water-cooled wall,which increases the ash fusion point,and for resisting slagging and high-temperature corrosion.4.ConclusionsA PDA measurement system is an effective method to obtain three-dimensional velocities,particle volume fluxes and normalized particle number concentrations of the gas/particle two-phase jet flow.The influence of the particle concentrator on the gas/particle two-phase characteristics for the centrally fuel rich burner was obtained in this work.Compared with the common burner,the degree of pene-tration for the centrally fuel rich burner is higher and the central recirculation zone is larger.Compared with the common burner,the particle volume flux and normalized particle number concentration for the centrally fuel rich burner is much larger near the chamber axis and the particle volume flux peak and normalized particle number concentration are much closer to the chamber axis.In each cross-section,the particle volume flux for the centrally fuel rich burner in the central recirculation is much larger than that for the commonburner.Fig.9–Normalized particle number concentration profiles for two burners.964F U E L P R O C E S S I NG T E CH N O L O G Y 89(2008)958–965AcknowledgementsThis work was supported by the Hi-Tech Research and Develop-ment Program of China(Contract No.2007AA05Z301),Post-doctoral Foundation of Heilongjiang Province(LRB07-216),the Ministry of Education of China via the2004New 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