PM2.5指的是颗粒的体积还是长度
三人行ゝ倠
直径.PM2.5是指大气中直径小于或等于2.5微米的颗粒物,也称为可入肺颗粒物.
为您推荐：
其他类似问题
PM2.5指的是颗粒的直径。
直径。PM2.5是指大气中直径小于或等于2.5微米的颗粒物，也称为可入肺颗粒物。九头鹰口罩终结者2，这种滤片防泄漏经过国家专利认证的靠得住。
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在现代都市生活中，大家都越来越好学，那么今天图老师【雾霾】栏目为你带来雾霾的知识，不了解的朋友们赶紧看看吧！
雾霾经验分享图钉问：PM2.5指数的正常范围是多少？指数多少的时候是属于严重污染的？
图老师答：PM2.5就是指直径小于或等于2.5μm的尘埃或飘尘，在环境空气中的浓度，这些悬浮物直径还不到人的头发丝粗细的1/20。
世界卫生组织2005年的《空气质量准则》中，PM2.5年均值为10微克/立方米，日均值为25微克/立方米。世界卫生组织(WHO)认为，PM2.5小于10是安全值，而中国的这些地区全部高于50接近80，比撒哈拉沙漠还要高很多。
而中国的PM2.5多少合格标准就要“宽泛”得多，空气质量指数分为6个级别，根据指数大小， 0至50为一级优，51至100为二级良，101至150为三级轻度污染，151至200为四级中度污染，201至300为五级重度污染和300以上为六级严重污染。
中美PM2.5标准对比
美国在1997年颁布了PM2.5的空气质量标准：年均值为15微克/立方米，日均值为65微克/立方米。2006年，美国主动将PM2.5的日均值标准由65微克/立方米调整为35微克/立方米，年标准仍为原来的15微克/立方米。
中国在2012年2月，环境保护部发布了《环境空气质量标准》（GB），其中增设了PM2.5 的浓度限值，这个浓度限值是依照世界卫生组织建议的最宽松的过渡期标准制定的，即年平均浓度限值大于等于35μg/m3，24小时平均浓度限值大于等于 75μg/m3，这项规定要到日才开始实行。 雾霾天总是咳嗽怎么办？图钉问：最近雾霾很严重啊，一直咳嗽，感觉嗓子不舒服怎么办？
图老师答：雾霾天要减少室外活动，出门记得戴口罩，另外，可以吃一些清肺润喉的食物缓解咽喉的不适感。
1、冰糖梨汁
取雪梨一个，冰糖适量。用50毫升温开水融化冰糖，将雪梨切成块，一起放入榨汁机中榨汁喝。可以润肺止咳，帮助缓解咽喉不适引起的咳嗽。
2、马蹄藕汁
新鲜马蹄配芦根、鲜藕汁同饮，可以清热化痰、生津止渴，雾霾天气在室外活动后，可以喝一大杯。雾霾严重的时候 戴口罩有没有用？图钉问：雾霾严重的时候，在外面人都看不清，空气中都弥漫着一股硫磺味，戴一个口罩就能完全的过滤掉空气里面的有毒物质吗？
图老师答：雾霾严重的时候出门一定要戴口罩，特别是小孩和老人。戴口罩有没有用，跟口罩的质量和类型有关系，普通的口罩效果微乎其微，因为细小的悬浮颗粒还是会通过口罩进入呼吸系统。最好选择要阻挡PM2.5需要医用N95口罩，其对0.3微米的颗粒能抵挡95%，在PM2.5爆表的天气也能起到一定效果。
购买口罩的时候最好选择正规合格、与自己脸型大小匹配的N95口罩，取下后要等到里面干燥后对折起来以防呼吸的潮气让口罩滋生细菌。佩戴的时间不宜过长，老年人和心血管疾病的人，要尽可能的避免佩戴，如果外出必须佩戴，也一定控制好佩戴时间，以免呼吸困难导致头昏。雾霾天气为什么会感觉呼吸困难？图钉问：每天要上班，我不得不外出，每次一出门就感觉呼吸困难，特别是雾霾严重的时候，喉咙会痒咳嗽，为什么雾霾天气会感觉呼吸困难呢？
图老师答：空气中的灰尘、硫酸、硝酸等颗粒物组成的气溶胶系统造成视觉障碍的叫霾。雾霾天时，空气中直径小于5微米的悬浮物较多，这些悬浮颗粒会随着空气进入人体呼吸系统，刺激口腔、鼻腔、咽喉、气道、支气管、细支气管等，导致上皮细胞出现一系列炎症反应。引起分泌物增加、气道壁水肿、气道狭窄，长期积累下来会导致肺部感染。
慢性阻塞性肺病是雾霾季节最容易复发的呼吸系统疾病之一，主要包括慢性支气管炎、肺气肿。患者常表现为呼吸不畅、气短、喘憋、咳嗽、闷气等哮喘症状。所以雾霾严重的时候，会有呼吸困难的感觉。
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石榴是成熟在秋季的水果，所以在这个时候食用的人也非常多。石榴的果实被果皮紧紧的皮住，想要吃到酸甜可口的石榴果粒一定要把外皮打开。剥石榴外壳时除了会弄到石榴汁，还不容易洗干净，那么怎么剥石榴快速简单呢？
为了改善居室的环境很多人喜欢在家中摆一些花草，因为植物可以净化空气美化环境。其实室内也不是什么花都可以摆的，有的花摆在室内不但没有好处还会危及生命，那么究竟哪些花是不能摆在室内的呢？
炎夏烈日炎炎，就在你与阳光亲密接触时，也让紫外线有了可乘之机，有的时候，虽然你认真做了防晒的工作，却依旧没能躲过炙热的阳光，晒伤之后，要立马着手修复晒伤的肌肤，那么晒伤后怎么处理呢？下面就一起来看看吧。
肚子上的赘肉看上去让人烦恼，尤其是对于想穿裙子的女生来说更是一件郁闷的事情。现在减肥的方法有很多，但是很多人并不知道专门瘦肚子的方法。那么，如何瘦肚子呢？下面就给大家介绍几种减肚子的方法吧。
脸上肉嘟嘟的女生和男生们是不是选择发型的时候很伤大脑。没关系，我们这里肯定有一款适合你的发型，本文也是大圆脸一枚，下面分享下我收集多年的圆脸女生男生发型以及学会的圆脸发型修饰技巧。
孕妇能吃西瓜吗？面对炎炎夏暑的到来，西瓜作为夏季最常见的水果之一，已经成了人们必吃的解暑水果，作为特殊人群的孕妈妈们也馋的直流口水了，那么问题来了，孕妇到底可以吃西瓜吗？下文为你详解。
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android笔记（45）
移动应用（39）
首先，说下概念（网上很多帖子几个地方都搞混了，理一下）：
dip ：&device independent pixels ，设备无关像素。 我看很多帖子写的五花八门的，关于d的，什么display啊各种都有，既然是设备无关，我还是觉得device靠谱。
& &dp就是dip
px ：&像素不多说
dpi ：dots per inch ， 直接来说就是一英寸多少个点。常见取&#2，160，240。我一般称作像素密度，简称密度
density ：&直接翻译的话貌似叫 密度。常见取&#2 ， 1.0 。
分辨率：&横纵2个方向的像素点的数量，常见取&#20 ，320X480
屏幕尺寸：&屏幕对角线的长度。电脑电视同理。
& & &这里还涉及另外一个问题，就是屏幕比例的问题。因为只确定了对角线长，2边长度还不一定。所以有了4：3、16：9这种，这样就可以算出屏幕边长了。
重点来了，网上很多帖子直接把 density 叫做“密度”，然后就说他是像素密度，然后就说他是dpi。
在android里面，获取一个窗口的metrics，里面有这么几个值
metrics.densityD
densityDpi就是我们常说的dpi。density其实是 &DPI / (160像素/英寸) &后得到的值。是不是有点奇怪，因为我带了单位。。。这个涉及到后面一个比较重要的东西，后面再说。
从上面就看得出了，DPI本身的单位也是 像素/英寸，所以density其实是没单位的，他就是一个比例值。
而dpi的单位是 像素/英寸，比较符合物理上面的密度定义，密度不都是单位度量的值么，所以我更喜欢把dpi叫像素密度，简称密度，density还是就叫density。
然后，来算算dpi。
比如一个机器，屏幕4存，分辨率480X800，他的dpi能算么。
因为不知道边长，肯定不能分开算，4是对角线长度，那直接用勾股定理算对角线像素，除以4，算出来大概是 dpi = 233 像素/英寸。
那么density就是 &（233 px/inch）/（160 px/inch）=1.46 左右
顺带说下，android默认的只有3个dpi，low、medium和high，对应 120、160、240，如果没有特别设置，所有的dpi都会被算成这3个，具体可以参考下这个帖子
/Android/tutorial/176.shtml
其中的default就是160。
然后就该算了，我们写布局的时候，肯定还是要知道1个dp到底有多少px的。
换算公式如下： & &dp = （DPI/（160像素/英寸））px &= &density px
& & & &&注意，这里都是带单位的。px是单位，dp是单位，density没单位。
为了方便，假设dpi是240 像素/英寸 ， 那么density就是1.5
那么就是 & dp=1.5px ，注意这是带了单位的，也就是&设备无关像素 = density 像素
那么转换为数值计算的话，应该是下面这个式子
PX = density * DP
像素值&= density * 设备无关像素值 &，请注意这里有个值字。
所以，90px 就应该是 60 dp 。不要问我为什么和公式不符了，全是单位的问题，物理老师死得早啊&
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引用源：http://blog.csdn.net/cymm_liu/article/details/8846141
Matt -- Thanks for the question regarding &density&, version 8.1.6
Submitted on 6-Mar- UTC
Last updated 28-Nov-
What does the density column in dba_tab_columns mean?
and we said...
Here is a support note on the topic:
Article-ID:
&Note:43041.1&
Circulation:
PUBLISHED (LIMITED)
server.Performance.SqlTuning
Optimizer Terms
QTUNE: What is Density?
Document-Type:
Skill-Level:
Updated-Date:
05-APR-:35
References:
&NOTE:50750.1& Shared-Refs:
Content-Type:
TEXT/PLAIN
CBO; FEATURE;
5/RDBMS (V7.X to V8.X);
Platforms:
To give an explanation of 'Density' as used by the CBO.
SCOPE & APPLICATION
For Oracle Support Analysts trying to understand how density is used by the
CBO when determining the best access path.
Density is a statistic used by the Cost Based Optimizer to give selectivity
estimates for columns where better information is unavailable (i.e. from
histograms etc.).
You must gather statistics on your table to get density statistics.
You can see density statistics by looking at the following views:
USER_TAB_COLUMNS
ALL_TAB_COLUMNS
DBA_TAB_COLUMNS
Density is a column statistic and provides selectivity estimates for
equi-join predicates (e.g. and A.COL1 = B.COL1) and equality predicates
(e.g. COL1 = 5).
The density is expressed as a decimal number between 0 and 1.
Values close to 1 indicate that this column is unselective
Values close to 0 indicate that this column is highly selective
The more selective a column, the less rows are likely to be returned by a
query referencing this column in its predicate list.
The column selectivity is part of the equation used to decide on the best
path for a query to take to retrieve the data required in the most effective
manner and hence impacts the final cost value for the query.
Density is calculated as follows:
Density = 1 / Number of distinct NON null values
The number of distinct NON-null values for a column (COL1) on table TABLE1
can be obtained as follows:
select distinct count(COL1)
The Density calculation has been refined by the use of histograms. If
you have created histograms on your columns we can now use the histogram
information to give more accurate information. Otherwise the Density is
calculated as before. With histograms we can use information on
popular and non-popular values to determine the selectivity.
A non-popular value is one that does not span multiple bucket end points.
A popular value is one that spans multiple end points.
(Refer to &Note:50750.1& for details on histograms)
For non-popular values the density is calculated as the number of non-popular
values divided by the total number of values. Formula:
Number of non-popular values
----------------------------
total number of values
We only use the density statistic for non-popular values.
Popular values calculate the selectivity of a particular column values by
using histograms as follows:
The Selectivity for popular values is calculated as the number of end points
spanned by that value divided by the total number of end points. Formula:
Selectivity = Number of end points spanned by this value
------------------------------------------
total number of end points
[Top of Page]
&&&November 21, 2002 - 8am UTC
Reviewer:&Stephan Bressler&from germany
what's the formula
Number of non-popular values
----------------------------
total number of values
In case there are no popular values, is the density = 1?
I thought there would be a density per bucket? Wouldn't that make sense?
&Selectivity with bind variables (Oracle 9.2.0.5)&&&November 26, 2005 - 1am
Reviewer:&Vladimir Sadilovskiy&from MA, US
Could you look into this test case:
SQL& create table t1 as select 1 f from all_
Table created.
SQL& create index i_t1 on t1(f);
Index created.
SQL& exec dbms_stats.gather_table_stats(null,'T1',method_opt=&'for all columns size
254',estimate_percent=&100, cascade=&true);
PL/SQL procedure successfully completed.
SQL& exec :f := 1;
PL/SQL procedure successfully completed.
SQL& set autotrace on
&b& with constants the plan is nice&/b&
SQL& select count(*) from t1 where f=2;
----------
Execution Plan
----------------------------------------------------------
SELECT STATEMENT Optimizer=CHOOSE (Cost=2 Card=1 Bytes=3)
SORT (AGGREGATE)
INDEX (RANGE SCAN) OF 'I_T1' (NON-UNIQUE) (Cost=1 Card=1
Statistics
----------------------------------------------------------
recursive calls
db block gets
consistent gets
physical reads
bytes sent via SQL*Net to client
bytes received via SQL*Net from client
SQL*Net roundtrips to/from client
sorts (memory)
sorts (disk)
rows processed
SQL& select count(*) from t1 where f=1;
----------
Execution Plan
----------------------------------------------------------
SELECT STATEMENT Optimizer=CHOOSE (Cost=8 Card=1 Bytes=3)
SORT (AGGREGATE)
TABLE ACCESS (FULL) OF 'T1' (Cost=8 Card=45765 Bytes=137
Statistics
----------------------------------------------------------
recursive calls
db block gets
consistent gets
physical reads
bytes sent via SQL*Net to client
bytes received via SQL*Net from client
SQL*Net roundtrips to/from client
sorts (memory)
sorts (disk)
rows processed
&b&but with bind variable the plan is different&/b&
SQL& select count(*) from t1 where f=:f;
----------
Execution Plan
----------------------------------------------------------
SELECT STATEMENT Optimizer=CHOOSE (Cost=2 Card=1 Bytes=3)
SORT (AGGREGATE)
INDEX (RANGE SCAN) OF 'I_T1' (NON-UNIQUE) (Cost=1 Card=4
58 Bytes=1374)
Statistics
----------------------------------------------------------
recursive calls
db block gets
consistent gets
physical reads
bytes sent via SQL*Net to client
bytes received via SQL*Net from client
SQL*Net roundtrips to/from client
sorts (memory)
sorts (disk)
rows processed
&b&1/NDV doesn't seem to match calculated density &/b&
SQL& select object_id from dba_objects where object_name='T1';
----------
SQL& set pages 999
SQL& select * from hist_head$ where obj#=56142;
COL# BUCKET_CNT
NULL_CNT TIMESTAMP
---------- ---------- ---------- ---------- ---------- ---------- ---------
SAMPLE_SIZE
----------- ---------- ---------- ----------
----------------------------------------------------------------
---------------------------------------------------------------- ----------
---------- ---------- ---------- ---------- ---------- ----------
0 25-NOV-05
Thank you.
- Vladimir
Followup&& November 26, 2005 - 12pm UTC:
autotrace (explain plan) do not BIND PEEK - so they can &lie&
To demnonstrate this and the effect of bind variable peeking, I'll run this script:
create table t
select 99 id, a.* from all_
update t set id = 1 where rownum = 1;
create index t_idx on t(id);
dbms_stats.gather_table_stats
( user, 'T',
method_opt=&'for all indexed columns size 254',
cascade=&TRUE );
variable n number
alter session set sql_trace=
exec :n := 1
select count(object_type) from t n_is_1_first where id = :n;
exec :n := 99
select count(object_type) from t n_is_1_first where id = :n;
exec :n := 99
select count(object_type) from t n_is_99_first where id = :n;
exec :n := 1
select count(object_type) from t n_is_99_first where id = :n;
The tkprof was generated with:
tkprof tracefile outputfile aggregate=no explain=u/p
to get both an EXPLAIN PLAN as well as getting all four statements printed individually:
we start with :N of one, the optimizer will actually optimize this statement as if the literal
number one was in the query itself:
BEGIN :n := 1; END;
********************************************************************************
select count(object_type) from t n_is_1_first where id = :n
Misses in library cache during parse: 1
Optimizer goal: CHOOSE
Parsing user id: 70
(OPS$TKYTE)
we did a hard parse (misses = 1), the number one was put in place of :n and the query optimizer
said &use index! it'll only get one row!&.
In the following the row source operation plan is the
ACTUAL PLAN used, the EXECUTION PLAN is what explain plan - which DOES NOT BIND PEEK - would give
NOTE THEY ARE DIFFERENT.
Explain plan doesn't have access to the binds:
Row Source Operation
---------------------------------------------------
SORT AGGREGATE
TABLE ACCESS BY INDEX ROWID T
INDEX RANGE SCAN T_IDX (object id 36010)
Execution Plan
---------------------------------------------------
SELECT STATEMENT
GOAL: CHOOSE
SORT (AGGREGATE)
TABLE ACCESS
GOAL: ANALYZED (FULL) OF 'T'
********************************************************************************
BEGIN :n := 99; END;
********************************************************************************
select count(object_type) from t n_is_1_first where id = :n
Misses in library cache during parse: 0
Optimizer goal: CHOOSE
Parsing user id: 70
(OPS$TKYTE)
This query was soft parsed, the plan already existed.
This too will therefore use an index range
scan - even though in this case, a full scan would have been &better&.
Again, explain plan &lies&
to us here
Row Source Operation
---------------------------------------------------
SORT AGGREGATE
TABLE ACCESS BY INDEX ROWID T
INDEX RANGE SCAN T_IDX (object id 36010)
Execution Plan
---------------------------------------------------
SELECT STATEMENT
GOAL: CHOOSE
SORT (AGGREGATE)
TABLE ACCESS
GOAL: ANALYZED (FULL) OF 'T'
********************************************************************************
BEGIN :n := 99; END;
********************************************************************************
select count(object_type) from t n_is_99_first where id = :n
Note that this is a hard parse (changed the correlation name in the query, new query).
- the optimizer plugs 99 into the query and optimizes.
We do full scan this time:
Misses in library cache during parse: 1
Optimizer goal: CHOOSE
Parsing user id: 70
(OPS$TKYTE)
Row Source Operation
---------------------------------------------------
SORT AGGREGATE
TABLE ACCESS FULL T
Execution Plan
---------------------------------------------------
SELECT STATEMENT
GOAL: CHOOSE
SORT (AGGREGATE)
TABLE ACCESS
GOAL: ANALYZED (FULL) OF 'T'
********************************************************************************
BEGIN :n := 1; END;
********************************************************************************
select count(object_type) from t n_is_99_first where id = :n
but note that this is a soft parse, the full scan plan will be used - first one to run the query
with bind variable inputs (first hard parse of it) gets to to &pick the plan&.... So, basically the
same query - just changed the correlation name and we have different plans:
Misses in library cache during parse: 0
Optimizer goal: CHOOSE
Parsing user id: 70
(OPS$TKYTE)
Row Source Operation
---------------------------------------------------
SORT AGGREGATE
TABLE ACCESS FULL OBJ#(36009)
Execution Plan
---------------------------------------------------
SELECT STATEMENT
GOAL: CHOOSE
SORT (AGGREGATE)
TABLE ACCESS
GOAL: ANALYZED (FULL) OF 'T'
You would need to use a 10053 trace to see the &true& cardinality in this case
&Unexpected cardinality&&&November 26, 2005 - 1pm UTC
Reviewer:&Jonathan Lewis&from UK
Test case does not reproduce on my 9.2.0.6
Note that what you have looks like the standard 1% estimate for function(col) = constant.
Oracle is treating this like a coercion problem.
select count(*) from t1 where f=:f;
----------
Execution Plan
----------------------------------------------------------
SELECT STATEMENT Optimizer=CHOOSE (Cost=2 Card=1 Bytes=3)
SORT (AGGREGATE)
INDEX (RANGE SCAN) OF 'I_T1' (NON-UNIQUE) (Cost=1 Card=458 Bytes=1374)
card = 458 = ceil().
My plan gives card={actual number of rows in table).
What do you see as the filter_predicates if you run the bind variable version (with a to_number()
on the bind) through dbms_xplan ?
Followup&& November 26, 2005 - 4pm UTC:
I didn't even look at the numbers to tell the truth, I saw &bind variable vs literal&.....
but yes, I don't see it in 9206 - but in 9iR1:
ops$tkyte@ORA9IR1& create table t1 as select 1 f from all_
Table created.
ops$tkyte@ORA9IR1&
ops$tkyte@ORA9IR1& create index i_t1 on t1(f);
Index created.
ops$tkyte@ORA9IR1&
ops$tkyte@ORA9IR1& exec dbms_stats.gather_table_stats(null,'T1',method_opt=&'for all columns size
254',estimate_percent=&99.99, cascade=&true);
PL/SQL procedure successfully completed.
ops$tkyte@ORA9IR1&
ops$tkyte@ORA9IR1& variable f number
ops$tkyte@ORA9IR1& set autotrace traceonly explain
ops$tkyte@ORA9IR1& select * from t1 where f=1;
Execution Plan
----------------------------------------------------------
SELECT STATEMENT Optimizer=CHOOSE (Cost=8 Card=31020 Bytes=93060)
TABLE ACCESS (FULL) OF 'T1' (Cost=8 Card=31020 Bytes=93060)
ops$tkyte@ORA9IR1& select * from t1 where f=2;
Execution Plan
----------------------------------------------------------
SELECT STATEMENT Optimizer=CHOOSE (Cost=1 Card=1 Bytes=3)
INDEX (RANGE SCAN) OF 'I_T1' (NON-UNIQUE) (Cost=1 Card=1 Bytes=3)
ops$tkyte@ORA9IR1& select * from t1 where f=:f;
Execution Plan
----------------------------------------------------------
SELECT STATEMENT Optimizer=CHOOSE (Cost=1 Card=310 Bytes=930)
INDEX (RANGE SCAN) OF 'I_T1' (NON-UNIQUE) (Cost=1 Card=310 Bytes=930)
ops$tkyte@ORA9IR1& set autotrace off
it does the 1%, in 9iR2 - 9206 - it did this:
ops$tkyte@ORA9IR2& select * from t1 where f=:f;
Execution Plan
----------------------------------------------------------
SELECT STATEMENT Optimizer=CHOOSE (Cost=7 Card=30693 Bytes=92079)
INDEX (FAST FULL SCAN) OF 'I_T1' (NON-UNIQUE) (Cost=7 Card=30693 Bytes=92079)
&Odd cardinality&&&November 26, 2005 - 2pm UTC
Reviewer:&Jonathan Lewis&from UK
I forgot to mention - Wolfgang Breitling has mentioned to me an oddity that appears when you create
a histogram on a column with just one value. I think there's an article on his site
which mentions it.
&question about the density calculation&&&November 28, 2005 - 4am UTC
Reviewer:&William Moffatt&from London, England
I'm just curious about something in the support note: it says about the density calculation :
&The number of distinct NON-null values for a column (COL1) on table TABLE1
can be obtained as follows:
select distinct count(COL1)
Firstly, isn't the &not null& implied by the count?
Secondly, shouldn't that be &count(distinct COL1)&, rather than &distinct count(COL1)&?
This is just idle curiosity, since the intent is obvious, and I presume the calculation works,
however it's done :)
(I did test it out, on 8.1.7.4, and it does appear that the query as written is incorrect, however
the note does say pre 7.3 - but then again, the &post 7.3& section refers you back to this query
too). I've seen you raise documentation bugs before, and similarly if I've made a mistake
somewhere, I'd rather know what and why :)
BTW, many thanks for the marvelous resource that this site is.
Followup&& November 28, 2005 - 7am UTC:
it should just be
select count(distinct col1) from table1;
the query presented in the note would not make sense.
select distinct count(col1) from table1 where col1
is the same as
select count(col1) from table1;
which would count non-null col1 values - they meant count(distinct col1)
&Calculation of density&&&July 30, 2008 - 6am UTC
Reviewer:&Mette&from DK
Thanks to this entry I now understand how we get the estimated number of rows when using histograms
But I dont understand how Oracle calculates the density (used for the non popular values).
I'm on 10.2.0.3 on AIX
This is the situation:
250 buckets
23644 distinct values
34 popular (spanning more than one bucket)
1.799.977 rows
stats run with 100%
The density is 0..... (taken from TAB_COLUMNS)
How does he get that number?
I tried with the formula from above - and got nothing like this number.
215 / 23644 = 0.
What have I missed here?
Best regards
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