Need for Speed
问题描述 :
Jiaoshou is a fan of “Need for Speed”, a famous game which most people have played. Recently, Jiaoshou had cleared “Need for Speed Undercover”. In this game, Jiaoshou acted as a spy. To prove that he is not a spy, Jiaoshou must do some evil things frequently. So lots of police cars often ran after him. In order to escape from the chase, Jiaoshou need to accelerate his Bugatti Veyron.
To simplify this problem, we just assume that only one police car ran after Jiaoshou. And Bugatti Veyron’s speed is vx. The police car’s speed is vy. v is a function of first degree or a constant, as the form of a*m+b. In this form, m means the minute that the car has run.
Now Jiaoshou is S meters ahead. Jiaoshou like to know if he could escape from the chase. He want you to write a program to solve this problem.
输入:
The input contains several tests. The first line of the input is a single integer T (T<1000) which is the number of test cases. Then T test cases follow.
Each test case contains three lines. The first line contains a single real number S (0<S<=10^6) , as the problem has described. The second line contains two real numbers ax, bx, indicating Bugatti Veyron’s speed is ax*m+bx. The last line also contains two real numbers ay, by, indicating the police car’s speed is ay*m+by. All numbers are range from -1000 to 1000, except S.
Each test case contains three lines. The first line contains a single real number S (0<S<=10^6) , as the problem has described. The second line contains two real numbers ax, bx, indicating Bugatti Veyron’s speed is ax*m+bx. The last line also contains two real numbers ay, by, indicating the police car’s speed is ay*m+by. All numbers are range from -1000 to 1000, except S.
输出:
The input contains several tests. The first line of the input is a single integer T (T<1000) which is the number of test cases. Then T test cases follow.
Each test case contains three lines. The first line contains a single real number S (0<S<=10^6) , as the problem has described. The second line contains two real numbers ax, bx, indicating Bugatti Veyron’s speed is ax*m+bx. The last line also contains two real numbers ay, by, indicating the police car’s speed is ay*m+by. All numbers are range from -1000 to 1000, except S.
Each test case contains three lines. The first line contains a single real number S (0<S<=10^6) , as the problem has described. The second line contains two real numbers ax, bx, indicating Bugatti Veyron’s speed is ax*m+bx. The last line also contains two real numbers ay, by, indicating the police car’s speed is ay*m+by. All numbers are range from -1000 to 1000, except S.
样例输入:
2 100 0 10 0 20 1000 0 1 0 0
样例输出:
Oh,God!Jiaoshou will be catched after 10.000 minutes. Good driver,Jiaoshou!
/* T dist ax bx ay by 追及问题:(ay-ax)/2*m^2 + (by-bx)*m - dist = 0 判断是否有解 */ #include <iostream> #include <math.h> using namespace std; const double esp = 0.0000000000001; bool isExistSolution; double ans1,ans2; void solve_equation(double a,double b,double c) //ax^2+bx+c=0 { if (fabs(a) < esp) { if (fabs(b) > 0) { isExistSolution = true; ans1 = ans2 = -c / b; // 分母不能为0!! } else isExistSolution = false; return; } double deltar = b*b - 4.0*a*c; if (deltar < 0) isExistSolution = false; else { isExistSolution = true; deltar = sqrt(deltar)/(2.0*a); b = (-b)/(2.0*a); ans1 = b + deltar; ans2 = b - deltar; } } int main() { #ifndef ONLINE_JUDGE freopen("2.txt","r",stdin); #endif int T; double dist, ax, bx, ay, by; cin >> T; while (T--) { cin >> dist >> ax >> bx >> ay >> by; solve_equation(0.5*(ay-ax),by-bx,-dist); if (!isExistSolution) puts("Good driver,Jiaoshou!"); else { if (ans1 > 0) printf("Oh,God!Jiaoshou will be catched after %.3lf minutes.\n",ans1); else if (ans2 > 0) printf("Oh,God!Jiaoshou will be catched after %.3lf minutes.\n",ans2); else puts("Good driver,Jiaoshou!"); } } return 0; }
参考：http://blog.csdn.net/linraise/article/details/18777521
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程序是对的。有些地方用了C++的写法
样例输出和程序输出不吻合，修改一下样例输出吧。我用的是VC编译器，会提示我的i和j变量重复定义
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老实说，这种方法就是穷举，复杂度是2^n，之所以能够AC是应为题目的测试数据有问题，要么数据量很小，要么能够得到k == t，否则即使n = 30，也要很久才能得出结果，本人亲测
这代码的输出格式不对阿，else语句的low=p移动一下位置，增加可读性
这道题这里的解法最坏情况似乎应该是指数的。回溯的时候
O(n) = O(n-1) + O(n-2) + ….
O(n-1) = O(n-2) + O(n-3)+ …
O(n) – O(n-1) = O(n-1)
O(n) = 2O(n-1)
没有对题目进行一番分析，真是美中不足啊
6题应该就是算概率的吧
第63行是不是漏了 return 1 + dis_right; ?
#!/usr/bin/env python
def cou(n):
arr =
i = 1
while(i<n):
arr.append(arr[i-1]+selfcount(i))
i+=1
return arr[n-1]
def selfcount(n):
count = 0
while(n):
if n%10 == 1:
count += 1
n /= 10
return count
后面的是python代码啊
great!
哎，path[0]应该是从零开始吧，为什么是赋值1呢？
大致看了下，算法的时间复杂度没有很本性的提升，算不上高效。


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ca.ualberta.CMPUT301F13T02.chooseyouradventure
Interfaces
Handler
Classes
ApplicationController
CameraAdapter
Comment
CommentLayoutBuilder
CommentView
Counters
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Decision
DecisionLayoutBuilder
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THE PALLBEARER A film review by John Paul Powell Copyright 1996 John Paul Powell
Originally published in the Outreach Connection Newspaper (May 3, 1996)
Good Friends Aren't So Hard To Find A Review By John Powell Copyright 1996 John Powell
The Pallbearer
Starring: David Schwimmer, Gwyneth Paltrow, Barbara Hershey, Michael Rapaport and Toni Collette. Written by: Jason Katims and Matt Reeves. Directed by: Matt Reeves. Produced by: Jeffrey Abrams and Paul Webster. An Alliance Communications release.
We're Substitute Friends For You
(Sung to the tune of "I'll Be There For You:The Theme To Friends" by The Rembrandts.)
No one told Bill Abernathy it would end this way. His life's a joke. No hope. The Chess Club his mainstay. Bill gave the world the finger on that day. He closed the door, revved the engine and never drove away.
Chorus
His mom found him there. He gave her quite a scare. Industrial-size curlers in her hair. Exhaust fumes floatin' everywhere. She whispered a silent prayer. Bill's corpse drooled without a care.
Tom Thompson can't find a job, no matter how he tries. Graduated the head of his class. He can't take flippin' fries.  Turning thirty in a bunkbed is his greatest fear.
Chorus
His mom sleeps down the hall. She can hear him through the walls. No chicks come and call. It's like having a chaperon at the ball. A worse time you can't recall. Tom's love life is in a deadfall.
Lost and lonely, Mrs. A gives Tom a call. Ruth insists Tom's a friend of her son's. He doesn't remember Bill at all. Mrs. Abernathy cries fibre-optic tears. Tom doesn't have the heart to tell her and the truth disappears.
Chorus
Together they pack Bill's effects. Each item he closely inspects. Tom's memory still neglects. It's her feelings he gallantly protects. What if Ruth suspects? Tom is conned into paying his respects.
Tom and pals write a eulogy for a man they did not know. They mock the funeral. How could anyone stoop so low? Overcome by guilt, Tom comforts Ruth. Sorrow turns to passion and she lusts in his youth.
Chorus
Tom will be there for her. When her breasts start to fall. He'll be there for her. When she's sucking back Geritol. Tom will be there for her. When her brain starts to stall.
Julie, Tom's high school crush, arrives back in town. She professes her love for him. It's an offer he can't turn down. Tom leaves Ruth hanging by a thin thread. How can he admit to knowing Bill only after he was dead?
Chorus
Tom has really stepped into it. And you thought Hugh Grant was a twit. He didn't use a dead guy to score a hit. Still, what an embarrassing deed to commit. The world laughed at the bumbling Brit. He should have picked a street more dimly lit.
David Schwimmer plays Ross on that weekly Friends TV show. He's one up on Matt LeBlanc. His first feature film doesn't blow. A monkey in a baseball movie, what's wrong with this? A question Matt should have asked before sinking into the abyss.
Chorus
Gwyneth Paltrow sure does shine. Barbara Hershey is divine. Schwimmer can leave Friends behind. A better Tom you cannot find. The Pallbearer, a comedy massaging the mind. If dark humor's your favourite kind.
The friends in Pallbearer are as real as you and me. Not like those phoney, well-dressed dopes lurking on TV. Trials and tribulations solved in thirty minutes flat. Perfect haircuts. Perfect lives.
Chorus
We're substitute friends for you. You don't have any of your own. Switch us on, allow us into your home. Count the valuable hours you have blown. Relax and listen to us drone. Dogs chewing on the ratings' bone.
The Pallbearer is rated S for Sensational. Schwimmer / Paltrow / Hershey x a pounding score by Stewart Copeland x a high school year book + one coffin race x Neil Young's Harvest Moon + a yummy glass of chocolate milk x a rib-fracturing eulogy + one mint Pacer automobile = Mrs. Robinson lock lips with Generation X.
Outreach Rating: 10 headstones / 10
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Volos Tourist Attractions | PlanetWare
Volos Tourist Attractions. Vólos, in the much indented Gulf of Vólos (modern Greek Pagasitikós Kólpos), . The Vólos Municipal Gallery is housed in the Town Hall. . The Theophilos Museum at Anakasia has frescoes by a great folk artist.
http://www.planetware.com/tourist-attractions-/volos-gr-thes-volos.htmVolos Flights and Travel Guide - Flights to Volos
Flights to Volos. . Volos Airport is located 30 Km south west of Volos. Car Hire. Volos Travel Guide. Main image. Volos. imageThumb0. imageThumb1 .
http://www.ryanair.com/en/flights-to-volos/Municipal Art Gallery of Volos - Wikipedia, the free encyclopedia
Municipal Art Gallery of Volos is a museum in Volos, Magnesia, Greece. Stub icon, This article related to a museum in Greece is a stub. You can help Wikipedia .
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Few cruise ships call at Volos, though it is the third largest port in Greece. . considered among the country's most important examples of ancient Greek art. The Municipal Gallery (9 a.m. to 5 p.m., tel: 0421 25 760) is housed in the town hall .
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Across Greece Volos
Sep 1, 2011 . Volos, one of the most enchanting towns in Greece, is situated at the centre of the . Also of importance is the Municipal Painting Gallery (housed in the Town . where frescoes painted by the this great folk artist are on display.
http://acrossgreece.com/2011/09/01/volos/ -
Chrysoula Zogia Museum and Art Gallery - Wikipedia, the free ...
. the free encyclopedia. Jump to: navigation, search. The Chrysoula Zogia Museum and Art Gallery is a museum and art gallery in Volos, Magnesia, Greece.
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Ioanna Kafida - biography
2003 "Pictorial itineraries", Metaxourgio, Volos . 2010 "Decomposing the canvas - Fabricating the image" Greek Painters, Municipal Art Gallery of Thessaloniki, .
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Volos, the city
The city of Volos is situated at the centre of the Greek mainland, at an . includes the Municipality of Volos with 90,000 inhabitants, the municipality of Nea Ionia .
http://students.ceid.upatras.gr/~xyannis/volos/volos.html


Administrator is designed to manage the configuration of the system (see Figure 1). Configuring is a process of editing the configuration database, which is a structured description of the entire automated system.
Figure 1. Administrator application
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A few days ago, my friend M and I decided to try smoking meth. It was the second time I'd ever done it. The first time, I'd smoked a really LARGE crystal in the house of an old dealer friend of my ex-partner's in Southern California.   I don't really remember much about that time, because I had followed up the meth experience with GHB, and I did not have a really good gauge about the effects of meth by itself or anything, but I remember the most intense euphoria in my life, and INCREDIBLE amounts of horniness.
This time, my friend M had some low grade, small crystals, that we smoked out of a simple glass meth pipe. We started at 3 o clock in the afternoon (approximately) and ended our session at 9 o clock. The hits were never quite as strong as my first time, but it was certainly very very addictive. We spent the whole afternoon catching up on old times, conversation about our lovers and our lives. I had not spent much time with her in a year, and so the meth helped facilitate really good conversation. The euphoria was mild, although moderately intense with some of the larger hits that I took. All in all, we used hardly any meth at all, perhaps a total (in small crystals) about half the size of a normal-sized tablet (not sure how much that is in mg), shared between the two of us over an extended period of time.
We stopped at 9, and cooked dinner back at my place. However, neither of us could eat anything, so we smoked some weed. At 11pm, we began to feel a little bit tired, so we each popped one diet pill that contained a proprietary blend of various Central Nervous System stimulants, like caffeine, synephrine, etc. However, this product also contains DMAE, which I'd read elsewhere as being a so-called 'Smart Drug' which affects people similar to Ritalin, and has been shown to be effective in treating people with ADD/ADHD. I am upset that the product doesn't list the exact amount of DMAE.
When that pill kicked in, as both M and I were high, and our feelings of meth were wearing down, I started to feel like I was beginning to roll, at around 12 midnight. I could not stop grinding my teeth, and I felt on top of the world, and that everything would be ok. it was VERY VERY strange. I was not expecting that, because I'd taken each of these three substances by themselves before (meth, weed and this energy pill containing DMAE), and none of them had this effect on me this intensely. It was EXACTLY like I was on MDMA.
Anyway, M and I ended up watching Michel Gondry music videos on my computer. She left at 3am, and I could not sleep until well into the next day, at around 7am...
I have negotiated doing meth again, but because I know it is extremely addictive, and I have a very addictive personality, I am going to wait for awhile before I do it. All in all, this experience was very very good, and I was particularly happy that I bonded with a very good old friend of mine, and though it may have been partially drug facilitated, all of our feelings came from the heart.
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 In groups of {{ players_per_group }} students, you are to distribute fictional bidding points over a selection of courses. Each participant has a total endowment of {{ endowment }} points. You can be allocated to a maximum of {{ s_len }} courses. You can choose over {{ nr_courses }} courses in total. Also, each course has a different capacity of students. {% endblocktrans %}
{% blocktrans trimmed %} Every available course is linked to a potential payoff. These payoffs can vary within the group. Imagine the payoff as a representation of you much you enjoy being allocated with a certain course. In this experiment, the payoffs - and therefore you personal joy - are given by the experimenter. {% endblocktrans %}
{% if Constants.show_capacities %}{% blocktrans trimmed %} In other words, the amount of bidding points you bid for a certain course should reflect how much you really would like to have the course. Suppose you get a payoff of 100 for obtaining course 1 and a payoff of 10 for course 2, then this would mean that you like course 1 much more than course 2. The following table shows all available courses, the capacities of the courses, and the payoffs for each course. In order to be considered for the allocation process, you have bid a minimum of 1 point for a course. A bid 0 would mean, that you are not interested in the course at all. {% endblocktrans %}
{% else %}{% blocktrans trimmed %} In other words, the amount of bidding points you bid for a certain course should reflect how much you really would like to have the course. Suppose you get a payoff of 100 for obtaining course 1 and a payoff of 10 for course 2, then this would mean that you like course 1 much more than course 2. The following table shows all available courses and the payoffs for each course. In order to be considered for the allocation process, you have bid a minimum of 1 point for a course. A bid 0 would mean, that you are not interested in the course at all. {% endblocktrans %}
{% endif %}{% blocktrans trimmed %} Please keep in mind that the following table is not necessarily the same for all your group members. {% endblocktrans %}
| {% trans "Index" %} | {% for i in indices %}{% trans "C" %}{{ i }} | {% endfor %}
|---|---|
|
{% trans "Potential Payoff" %}
{% if Constants.show_types and Constants.nr_types > 1 %}
({% trans "You are" %} {{ player.role }}) {% endif %} |
{% for i in valuations %}
{{ i|c }} | {% endfor %}
| {% trans "Capacities" %} | {% for i in capacities %}{{ i }} | {% endfor %}
{% trans "How are courses allocated?" %}
{% blocktrans trimmed %} In this system, students not only bid for courses, but also rank them according to how much they want them (preference rank). This is called the participant's "preference list." If two or more courses are equally desirable (e.g., if two courses are both third best for you), please give them different ranks randomly (third and fourth rank) - i.e., you need to break the tie among courses in your preference list. If a participant wants to be considered for a course, he/she needs to bid at least one point for this course. The sum of the bids cannot exceed {{ endowment }} bid points. {% endblocktrans %}
{% blocktrans trimmed %} The system works as follows: After all students stated their preference ranks and bids, the allocation is found as follows: {% endblocktrans %}
{% blocktrans trimmed %} (1) In the first round, each participant is tentatively placed in his/her top {{ s_len }} courses from his/her preference list (or fewer if he/she has bid for fewer than three courses). Then the computer adds up course enrollment across students and calculates enrollment in each course. If a course is assigned more students than its capacity, the lowest-bidding students for that courses are dropped, so that each course tentatively holds no more students than its capacity. However, this is just the first computer allocation and is tentative. {% endblocktrans %}
{% blocktrans trimmed %} (2) Then, in the next computer allocation round, each participant who was dropped from any courses in the first computer allocation round is tentatively placed in replacement courses. Then the computer inspects the courses again. If a course is assigned more students than its capacity, then the bids of students who were tentatively placed in it in the previous round and in this round are inspected. The lowest-bidding students tentatively placed in this or previous rounds for that course are dropped, so that each course tentatively holds no more students than its capacity. {% endblocktrans %}
{% blocktrans trimmed %} (3) The computer iteratively continues until no participant is dropped from a course, or all options of the students are exhausted in their preference lists. The tentative assignments are realized as real assignments. {% endblocktrans %}
{% if Constants.instructions_example %}{% trans "Example:" %}
{% blocktrans trimmed %} Suppose that there are four students and four courses with a capacity of two students each. Each participant can have two courses in his/her schedule and has a bidding point endowment of 100 points. Further suppose that the students make their decisions as indicated in the following table: {% endblocktrans %}
| {% trans "Student" %} 1 | {% trans "Student" %} 2 | {% trans "Student" %} 3 | {% trans "Student" %} 4 | ||||
|---|---|---|---|---|---|---|---|
| {% trans "Course" %} {% trans "(ordered according to preferences)" %} |
Bid | {% trans "Course" %} {% trans "(ordered according to preferences)" %} |
Bid | {% trans "Course" %} {% trans "(ordered according to preferences)" %} |
Bid | {% trans "Course" %} {% trans "(ordered according to preferences)" %} |
Bid |
| {% trans "Course" %} 1 | 8 | {% trans "Course" %} 1 | 16 | {% trans "Course" %} 3 | 2 | {% trans "Course" %} 2 | 88 |
| {% trans "Course" %} 2 | 79 | {% trans "Course" %} 2 | 77 | {% trans "Course" %} 2 | 89 | {% trans "Course" %} 3 | 6 |
| {% trans "Course" %} 3 | 13 | {% trans "Course" %} 3 | 7 | {% trans "Course" %} 1 | 4 | {% trans "Course" %} 1 | 5 |
| {% trans "Course" %} 4 | 0 | {% trans "Course" %} 4 | 0 | {% trans "Course" %} 4 | 3 | {% trans "Course" %} 4 | 1 |
{% blocktrans trimmed %} Computer Round 1 (will consider top two ranks of students because students can only have two courses.) {% endblocktrans %}
| {% trans "Students" %} | {% trans "Courses" %} | |||||||
|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | {% trans "Computer inspects the courses:" %} | 1 | 2 | 3 | 4 |
| {% trans "C" %}1 | {% trans "C" %}1 | {% trans "C" %}3 | {% trans "C" %}2 | {% trans "S" %}1 - 8 | {% trans "S" %}3 - 2 | |||
| {% trans "C" %}2 | {% trans "C" %}2 | {% trans "C" %}2 | {% trans "C" %}3 | {% trans "S" %}2 - 16 | {% trans "S" %}4 - 6 | |||
| {% trans "S" %}3 - 89 | ||||||||
| {% trans "S" %}4 - 88 |
{% blocktrans trimmed %} Rejected bids are crossed. Because some students are dropped from courses, they are now assigned to courses from their third rank on. {% endblocktrans %}
{% blocktrans trimmed %} Computer Round 2: S1 and S2, who were rejected by one of their courses, are tentatively assigned to their third-choice course. {% endblocktrans %}
| {% trans "Students" %} | {% trans "Courses" %} | |||||||
|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | {% trans "Computer inspects the courses:" %} | 1 | 2 | 3 | 4 |
| {% trans "C" %}1 | {% trans "C" %}1 | {% trans "C" %}3 | {% trans "C" %}2 | {% trans "S" %}1 - 8 | ||||
| {% trans "C" %}2 | {% trans "C" %}3 | {% trans "S" %}2 - 16 | ||||||
| {% trans "C" %}3 | {% trans "C" %}3 | {% trans "S" %}3 - 89 | {% trans "S" %}1 - 13 | |||||
| {% trans "S" %}4 - 88 | {% trans "S" %}2 - 7 |
{% blocktrans trimmed %} S3 and S4 no longer get C3. Instead, S1 and S2 are tentatively placed in C3. {% endblocktrans %}
{% blocktrans trimmed %} Computer Round 3: S3 and S4, who were rejected by one of their courses, are tentatively assigned to their third-choice courses. {% endblocktrans %}
| {% trans "Students" %} | {% trans "Courses" %} | |||||||
|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | {% trans "Computer inspects the courses:" %} | 1 | 2 | 3 | 4 |
| {% trans "C" %}1 | {% trans "C" %}1 | {% trans "C" %}3 | {% trans "S" %}1 - 8 | |||||
| {% trans "C" %}3 | {% trans "S" %}2 - 16 | |||||||
| {% trans "C" %}3 | {% trans "C" %}3 | {% trans "C" %}1 | {% trans "C" %}1 | {% trans "S" %}3 - 89 | {% trans "S" %}1 - 13 | |||
| {% trans "S" %}4 - 88 | {% trans "S" %}2 - 7 |
{% blocktrans trimmed %} S3 and S4 cannot get C1 because S1 and S2 have bid a higher amount for this course. Therefore, S3 and S4 are now to be considered for their fourth-choice course. {% endblocktrans %}
{% blocktrans trimmed %} Computer Round 4: S3 and S4 are now to be considered for their fourth-choice course. The tentative allocation is finalized as: {% endblocktrans %}
| {% trans "Students" %} | {% trans "Courses" %} | |||||||
|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | {% trans "Computer inspects the courses:" %} | 1 | 2 | 3 | 4 |
| {% trans "C" %}1 | {% trans "C" %}1 | {% trans "C" %}3 | {% trans "S" %}1 - 8 | {% trans "S3" %} - 3 | ||||
| {% trans "C" %}3 | {% trans "S" %}2 - 16 | {% trans "S4" %} - 1 | ||||||
| {% trans "C" %}3 | {% trans "C" %}3 | {% trans "S" %}3 - 89 | {% trans "S" %}1 - 13 | |||||
| {% trans "C" %}4 | {% trans "C" %}4 | {% trans "S" %}4 - 88 | {% trans "S" %}2 - 7 |
{% blocktrans trimmed %} Please click the "Next" button to continue. {% endblocktrans %}
{% next_button %} {% endblock %}


Internet of things and prelude of google.
Internet of things and prelude of google.
Internet of things and prelude of google.
Internet of things and prelude of google.
Internet of things and prelude of google.
Internet of things and prelude of google.
Internet of things and prelude of google.


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Bharbi Hazarika
11/15/2018
At first glance, the acroamatic piece of amber-colored fabric stuck to the doorframe goes unnoticed and so do the chappals (slippers) tossed to a corner of the walkway that leads up to the burnished door. Binda Adhikari has a strict “no shoes inside” policy, as is typical in most Asian households. A tradition that the family neatly tucked inside their suitcases, in between the folds of their sarees and Dhaka topi, before heading to “America.”
The suburban house is overwhelmed by a sharp smell of mustard oil. The sound of sizzling spices quickly catches your attention, which is naturally drawn to the mosaic of colorful portraits of Hindu gods and godmen glued to the whitewashed-walls.
Bharbi Hazarika| SENIOR WRITER
The Adhikaris pose for a photograph in their suburban home in Columbus, Ohio.
 Snuggled in an oversized grey cardigan, Binda swims around the kitchen island. Her husband, Bhagawat, says she is the hardest worker in their family. From preparing their children for school to reminding her in-laws to take their medicine, she does it all.
Raven hair clipped back, her eyes are fixated on the chopping board as she meticulously slices the tomatoes. Binda hardly lets anything distract her, a determination that carried her through high school in the U.S. in her 20s. It’s not until she sweeps toward the kitchen sink to wash the greens, when the maroon-colored gnuiu, a Nepali fabric wrapped around her waist, appears. Her attire seems to mirror their current lifestyle — a fusion of the worlds, mismatched yet balanced.
Unlike her daughter-in-law, Madhavi Adhikari hasn’t picked up the latest trend in fashion. A Nepali wrapper modestly draped over her bosom, she sits in a corner of the living room gently rocking the cradle, back and forth.
The typicality of the Adhikari household is reflected in the roles played by the three generations, carrying distinct experiences, tied with a single thread, which is their Bhutanese-Nepalese identity. It’s a characteristic that the suburban house dons well.
At first glance, the peculiarities of the houses in the neighborhood are unremarkable. From the outside, Etna Township passes for any other American neighborhood with symmetrical houses and coiling roads. The Columbus suburb, however, is home to over 30 immigrant families from Bhutan who were expelled from their native country in an ethnic cleansing attempt during the ‘90s. They spent much of their lives in refugee camps across Nepal before they were catapulted to United States as a part of a United Nations third country resettlement.
“When I first came here looking to settle down, I saw plains,” Bhagawat said. “It reminded me of our village in Bhutan and I instantly knew I wanted to live here.”
The Adhikari household is the new normal in this bourgeois neighborhood.
“I was jobless, homeless, countryless,” Bhagawat, a warehouse worker at KDC/ONE Columbus, said with an unusually resilient smile on his face.
Bhagawat and his family emigrated to Tucson, Arizona, in 2009 after living as refugees for 18 years in Jhapa, Nepal. They later moved to Columbus, Ohio, in 2013 to finally settle in their American life.
Bharbi Hazarika| SENIOR WRITER
In the mid-1980s Bhutan, led by the monarch Jigme Singye Wangchuck, began tightening its citizenship laws calling for “One nation, One people.” The Himalayan kingdom conducted a census of its demographic and then proceeded to evict nearly 100,000 people, almost all of them Hindus of Nepalese origin, including the Adhikaris. It declared the Nepali-origin community, called Lhotshampa, illegal immigrants, even though their lineage went back several generations in Bhutan. The process of cultural homogenization, administered by the king, required all Bhutanese to follow the lifestyle of the Drukpa, the lineage of the royal family who follow Buddhism, and its norms which included their eating, drinking and clothing habits, and even their speech.
Bhutan, which mainly comprises of people who follow Buddhism, a religion that touts its pacifistic and non-violent edicts, forced out a significant portion of its citizens using draconian measures. The anti-immigrant drive was implemented starting in 1989, when Nepalese books were rounded up in town squares and burned, Nepalese houses were rampaged, women and children lived in the fear of rape and abduction. From Bhutan, many were forced to escape to neighboring countries, where they lived in refugee camps along the border. It hasn’t let any of them move back.
About one-sixth of the Bhutanese population was cast out of the nation and had to seek refuge in the neighboring countries such as India and Nepal.
Alex Penrose | MULTIMEDIA DIRECTOR
Considering the country’s relatively small population, the magnitude of this exodus was one of the world’s largest by per capita. The Buddhist Shangri-La, however, still seems to maintain its title by advancing the philosophy of “gross national happiness,” coined by the 4th King of Bhutan, Wangchuck, in 1972.
For Bhagawat, the hallmark happiness was just a veneer that cracked as soon as he turned 9 and was forced to flee the country.
Many asylum seekers ended up in the seven refugee camps in southern Nepal. The United Nations High Commissioner for Refugees (UNHCR), the International Organization for Migration and the World Food Programme among others were responsible for the camps, which were home to these Bhutanese migrants.
Camp life was tough both psychologically and physically.
Rife with disease and squalor, the Goldhap refugee camp became the new home for the Adhikaris. The camp had sprawling dirt streets monopolized by bands of scrawny children, who didn’t have anywhere else to play.
UN volunteers distributed food rations every two weeks. It included the staples: rice, lentils and some salt. The Adhikaris were often told to stretch the ration for as long as they could. Bhagawat’s mother, Madhavi, would come up with novel ways to keep her hunger at bay. She would often take a wet cloth and fasten it around her waist. The snug knot that hung just above her navel, hardly let her breathe, a sacrifice that the mother justified by necessity — one more day of food for her family.
Bharbi Hazarika| SENIOR WRITER
The Adhikari household prepares for Dussehra festival, a celebration symbolizing the victory of good over evil.
People lived in feeble bamboo houses with thatched roofs and no toilets. The communal lavatories, which were basically pits in the ground, were located outside. Groves of banana trees separated the houses from each other. Tilak Niroula, Bhagawat’s nephew, said their houses had no electricity or running water. There were a few tanks and hand-pumps used to draw water, installed sporadically throughout the camp. Approximately 40 households had to share one tap. These low-cost, temporary shelters housed more than 15,000 refugee families.
“It was miserable in the camps,” Bhagawat said. “Where to go? So many of my friends and other people committed suicide.”
Nature’s wrath was apparent and the thin walls of the houses didn’t help. The cottages were prone to fire, and rain would easily seep through their flimsy ceilings. Lack of building materials meant it wasn’t unusual for families to mount tents out of plastic from time to time.
The cerulean tents provided some relief to the eyes in the otherwise dusty-colored neighborhood. The tranquil blue of the tent, however, did little to offer seamless sleep to Bhagawat and his family.
Most of those refugees spent close to 20 years of their lives in the cramped nooks of southeastern Nepal.
Their title didn’t allow the people to step out of the refugee camps except for official reasons, Niroula said. They were forced by law to be confined to close quarters. Niroula knew if he stepped out, he would have no recognized identity to support himself.
Bharbi Hazarika| SENIOR WRITER
A lack of national credentials automatically made them ineligible to pursue jobs outside the camps and many were unable to provide sufficient nutrition to their family, let alone buy other luxuries such as clothes and shoes. There were some jobs available within the camps, but provided little incentive.
Bhagawat said he is educated and was a diligent student in the camp school, but the fact that he couldn’t get a job frustrated him.
Some days, Bhagawat would hike up the hills on the border of Nepal overlooking Bhutan and vignettes from days spent in his village in Bhutan would flash through his head and he would contemplate sneaking back in. The temptation was as inconceivable as the idea of a home. A teenaged Bhagavad was forced to emancipate from his motherland before he came of age.
“It feels like my own mom has disowned me,” he said.
Now, Bhagawat sleeps on a comfortable mattress wrapped in clean white sheets. The walls of his house are solid and impenetrable, and he says he is thankful it doesn’t burn down. Bhagawat and Binda got married in the U.S. in 2010 and soon after they filed for citizenship. The family finally discarded their refugee titles for to become citizens of the United States in 2013 and for the first time in a long time Bhagawat said he felt at home.
“I never thought I could say this,” Bhagawat said. “That I am a proud citizen of America.”
Chris Boian, the spokesperson for the UN Refugee Agency for the United States and the Caribbean, said the UN Refugee Agency is invested in finding durable solutions for people fleeing persecution, war and violence across the world. Boian added that the UN proposed resettlement program has had a significant positive impact on the lives of refugees.
In 2016, the number of countries offering resettlement programs doubled, from 14 resettlement countries in 2005 to 37 countries worldwide, including the United States, according to UNHCR.
“Resettlement is a discretionary activity, in other words, it’s not mandatory for any country,” Boian explained. “It’s a choice that countries make for humanitarian reasons, to ensure that very vulnerable human beings such as women, children and men, who fled their homes are able to receive the safe haven and the ability to restart their lives with dignity that those countries themselves feel is necessary.”
“Statelessness is a terrible tragedy. These are individuals who are recognized as nationals by no country in the world and it has very sad consequences on the ability of these people to lead meaningful lives.”Chris Boian
Despite this increase, the dream of resettlement is a realization for a meager less than 1 percent of the global refugee population, Boian said. More than 99 percent still remain without a nationality or are internally displaced in their countries.
The privilege that comes along with the title of a citizenship is rarely experienced by refugees and asylum seekers. According to the 2018 UNHCR survey, there are 25.4 million refugees, over half of whom are under the age of 18 worldwide. Additionally, an estimated 10 million stateless people, which previously included the Bhutanese-Nepali diaspora, stand without a nationality or access to basic rights such as education, healthcare, employment and freedom of movement.
Bharbi Hazarika| SENIOR WRITER
Binda Adhikari holds her younger daughter Shrenika.
“Statelessness is a terrible tragedy,” Boian said. “These are individuals who are recognized as nationals by no country in the world and it has very sad consequences on the ability of these people to lead meaningful lives.”
The identity of a refugee has lent unfathomable challenges to families like the Adhikaris. Bhuwan Pyakurel, the chairperson of the Bhutanese Community of Central Ohio, said the trauma of being displaced comes with added woes. Once resettled, the sense of entitlement that comes with being a citizen still seems like a distant benefit for several members of the community.
Serious mental health issues plague the Bhutanese-Nepali diaspora in Ohio. Pyakurel said several members experience post traumatic stress disorder that harkens back to the atrocious experiences of fleeing Bhutan and the subsequent refugee life. Many of them find it difficult to let go of their prior identity, which is why several still identify as “second-class people,” he added. The self-attached title is a remnant of the profound vulnerability that the community experienced in Bhutan.
“We were scared all the time, twenty-four seven,” Pyakurel said. “We used to think anybody can kick us, anybody can hit us, anybody can do anything they wanted to us.”
The Bhutanese Community of Central Ohio with the help of North Community Counseling Center, a mental-health agency on the northeast side of Columbus, provides facilities to support the victims. It’s estimated that the center helps over 5,000 people annually by hosting programs and training that promote integration into society.
The mental-health agency stepped in after the Trump administration and the Office of Refugee Resettlement nixed federal funds to the community. Pyakurel said the organization had initially received a three-year federal grant totaling $450,000.
Last year, after the federal government canceled the funds, the group used its savings to operate. The services were well on their way to becoming defunct when the Columbus City Council donated $45,000, a fraction of their initial grant, but Pyakurel said any aid helps.
Bharbi Hazarika| SENIOR WRITER
For most people, the struggle of living as a refugee is an afterimage prodded by the ceaseless images of the state in Syria and Yemen plastered on TV screens. The Adhikaris have known no reality other than the constant instability for much of their life.
Now, Binda and Bhagawat are grateful that they can finally offer to their children what they never had growing up — a stable life.
“We didn’t have a roof or anything in our classrooms. If it rained, class got over,” Bhagawat said recounting his school days in the camp. “But Shreya goes to a school that takes care of everything from her alphabets to if she is growing okay and that means a lot.”
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Documentation
About websheet.js
Websheet.js is a lightweight standalone JavaScript library for querying, retrieving, and displaying data from Google Spreadsheet. Load entire worksheets or leverage SQL-like queries to sort, group, and filter data. All you need is the URL of a public Google Spreadsheet.
This is useful to allow other people than you to manage easily some pages content without CMS (restaurant menu, events list, ...).
- Use SQL-like Google Visualization query language, for grouping, sorting, filters, pivots, ...
- Create re-usage templates
- Customize with your own callbacks
- Define programatically new column aliases
Get Started
1. Create & Share your spreadsheet
First, create a new Google Spreadsheet including a header row (that will be used as your column names), and your data. You can use any cell format (dates, currencies, checkboxes, ...), even with URLs (links or photos), that will be rendered in your HTML.
After that, click on Share, and in the Get link section, click on Change to anyone with the link to allow public read access to your spreadsheet. Then, copy the spreadsheet link.
2. Import websheet.js
Import websheet.js on your website before , give a name to the data source and provide the spreadsheet link and tab name. You can declare as many data sources as you want, using websheet function multiple times.
You can use the CDN (recommended), or download it here.

- Aliases to create programatically new columns
- Querying to filter, group and order rows
- Caching to enable/disable client-side caching
- Callbacks to get raw data when they are loaded
3. Render data
Use data sources anywhere in the HTML page to render spreadsheets rows. And that's it!

- Conditions to display or not elements according to data
- Binding to use data in HTML attributes (images, links, ...)
- States to display or not elements according to state (loader, ...)
- Templates to reuse similar structures in your page
Full Example
To see a full example in action, using all the features of websheet.js,
check the demo below:
Open Full Demonstration
Conditions
Use conditions to display or not elements according to data. Provide directly the column name to check as boolean (you can use checkboxes in your spreadsheet).

Yes !

No

If you need more complex conditions (string comparisions, ranges, ...), use Aliases:

It's Bread! !

...

Read Aliases section for more information.
Binding
Binding allows you to use data in HTML attributes, useful for example in image.src, a.href, ...

States
States allows you to display or not elements according to state (loader, ...).

Loading...

Templates
Templates allows you to reuse similar structures in your page, for example if you wants to display at different locations of you website the same data.

...

Aliases
You can easily create new column aliases programatically, in order to use them in your templates. It's very useful for conditional checks, or to aggregate data from multiple columns.
Simply declare new column names, with value a function providing row data as parameter. Those will be triggered for each row of your spreadsheet.
websheet('products', {
...
aliases: {
// Conditional Check
'Is Bread?': row => row['Category'].value === 'Breads',
// Aggregation
'Full Name': row => row['First Name'] + " " + row['Last Name'],
// More Complex
'Discount?': row => {
if (row['Discounted Price'].value && row['Discount End'].value) {
return row['Discount End'].value > new Date();
} else {
return false;
}
}
}
})
After that, you can use them in your templates as if it were an existing column:

 It's bread!

Querying
Websheet.js supports Google Visualization API Query Language, which is similar to SQL syntax. It provides the ability to initialize the data source format, by filtering, grouping or sorting rows.
NOTE: For now, only column indexes (A, B, ...) are supported in query language.
For example, the code below will initialize the data source with first 100 rows, ordered by B (second) column.
websheet('products', {
...
query: `select * order by B limit 100`,
})
For more information about how to write queries, read the detailed documentation here.
Caching
In order to render and display the data faster, websheet.js is using client-side caching. By default, caching is enabled. Thus, the timing is the following:
- If cached version of the data exists in the client browser, data are loaded instantly.
- At the same time, a query to the Google Spreadsheet is always sent.
- When Google servers has responded (generally after ~400ms), if websheet.js detects modifications between the cached version and the new one, the displayed data are replaced.
- Received data are stored in client browser cache, for the next time.
With this system, spreadsheets data are always loaded instantly to the user.
To disable caching, simply mention it:
websheet('products', {
...
caching: false, // disable caching
})
Callbacks
For now, websheet.js handle a onLoaded callback, triggered when data are loaded from Google Spreadsheet.
websheet('products', {
...
onLoaded: data => {
/**
* `data` format:
* [
* {"Display?": true, "Product": "Baguette", ...},
* {"Display?": true, "Product": "Olive Bread", ...}
* ...
* ]
*/
}
})


--- layout: talk title: Exploring Python Dependency Graphs with Impulse location: London Django Meetup tags: [python, impulse] image: impulse-demo.jpg description: A quick demo of Impulse, a command line tool for visualising the architecture of any Python package. skills_matter_link: https://skillsmatter.com/skillscasts/13509-impulse-demo#video slides_link: http://slides.com/davidseddon/impulse-demo#/ ---
After watching this talk, you may want to read the Impulse documentation.


Check for primary key and if not set try to obtain it Return true on success
API Tags:
boolean delete(
$data, [
$where = ''], [
$params = null]
)
|
|
Delete the data into the database according the table element
A primaryKey should be set. If the key is not set It can be obtained from jqGridDB::getPrimaryKey Return true on success, false when the operation is not succefull
Parameters:
|
array |
$data: |
associative array which key values correspond to the names in the delete command |
|
|
$where: |
|
|
array |
$params: |
|
API Tags:
Information Tags:
| Todo: | possibility to set additional where clause |
void editGrid(
[
$summary = null], [
$params = null], [string
$oper = false], [
$echo = true]
)
|
|
Perform the all CRUD operations depending on the oper param send from the grid and the table element If the primaryKey is not set we try to obtain it using jqGridDB::getPrimaryKey If the primary key is not set or can not be obtained the operation is aborted.
Also the method call the queryGrid to perform the grid ouput
Parameters:
|
array |
$summary: |
- set which columns should be sumarized in order to be displayed to the grid By default this parameter uses SQL SUM function: array("colmodelname"=>"sqlname"); It can be set to use the other one this way array("colmodelname"=>array("sqlname"=>"AVG")); By default the first field correspond to the name of colModel the second to the database name |
|
array |
$params: |
additional parameters that can be passed to the query |
|
string |
$oper: |
if set the requested oper operation is performed without to check the parameter sended from the grid. |
|
|
$echo: |
|
API Tags:
Return the fields generated for CRUD operations
API Tags:
string getPrimaryKeyId(
)
|
|
Return the primary key of the table
API Tags:
boolean insert(
array
$data
)
|
|
Insert the data array into the database according to the table element.
A primaryKey should be set. If the key is not set It can be obtained from jqGridDB::getPrimaryKey Return true on succes, false otherwiese.
Parameters:
|
array |
$data: |
associative array which key values correspond to the names in the table. |
API Tags:
Information Tags:
| Todo: | in the future we should return the last insert id from the table |
void setAfterCrudAction(
string
$oper, string
$sql, [array
$params = null]
)
|
|
Run a sql command(s) after the operation from the CRUD is succefull.
Can run a unlimited
Parameters:
|
string |
$oper: |
- the operation after which the command should be run |
|
string |
$sql: |
- the sql command |
|
array |
$params: |
- parameters passed to the sql query.
|
API Tags:
void setBeforeCrudAction(
string
$oper, string
$sql, [array
$params = null]
)
|
|
Run a sql command(s) before the operation from the CRUD.
Can run a unlimited
Parameters:
|
string |
$oper: |
- the operation after which the command should be run |
|
string |
$sql: |
- the sql command |
|
array |
$params: |
- parameters passed to the sql query.
|
API Tags:
void setPrimaryKeyId(
string
$keyid
)
|
|
Set a primary key for the table
Parameters:
API Tags:
void setSuccessMsg(
string
$msg
)
|
|
Set the message which will be send to client side grid when succefull CRUD operation is performed. Usually afterSubmit event should be used in this case.
Parameters:
API Tags:
void setTable(
string
$_newtable
)
|
|
Set table for CRUD and build the fields
Parameters:
API Tags:
boolean update(
array
$data
)
|
|
Update the data into the database according the table element
A primaryKey should be set. If the key is not set It can be obtained from jqGridDB::getPrimaryKey Return true on success, false when the operation is not succefull
Parameters:
|
array |
$data: |
associative array which key values correspond to the names in the table |
API Tags:
Information Tags:
| Todo: | possibility to set additional where clause |
true _actionsCRUDGrid(
string
$oper,
$event
)
|
|
Executes the sql command after the crud is succefull
Parameters:
|
string |
$oper: |
can be add,edit,del |
|
|
$event: |
|
API Tags:
| Return: | if ooperation is succefull. |
| Access: | protected |
Build the fields array with a database fields from the table.
Also we get the fields types Return false if the fields can not be build.
API Tags:


Cpu Usage
0%
| manufacturer | Brand | Cores | Physical Cores | Vendor | Speed | SpeedMin | SpeedMax | Vartualization |
|---|---|---|---|---|---|---|---|---|
| Fetching... | Fetching... | Fetching... | Fetching... | Fetching... | Fetching... | Fetching... | Fetching... | Fetching... |
Memory Info
| Total | Free | Used | Active | Available |
|---|---|---|---|---|
| Fetching... | Fetching... | Fetching... | Fetching... | Fetching... |


List tables
.tables this will list all active tables in the current session.
List tables and headers
.schema - typing this in will display all active tables.
Selecting
SELECT The SELECT statement is used to select data from a database. The result is stored in a result table, called the result-set.
SELECT column_name,column_name FROM table_name;
ORDER BY The ORDER BY keyword is used to sort the result-set by one or more columns. The ORDER BY keyword sorts the records in ascending order by default. To sort the records in a descending order, you can use the DESC keyword.
SELECT column_name,column_name FROM table_name ORDER BY column_name,column_name ASC|DESC;
Wildcards
% A substitute for zero or more characters
_ A substitute for a single character
[charlist] Sets and ranges of characters to match
[^charlist] or [!charlist] Matches only a character NOT specified within the brackets
Searching
WHERE The WHERE clause is used to extract only those records that fulfill a specified criterion.
SELECT column_name,column_name FROM table_name WHERE column_name operator value;
LIKE The LIKE operator is used to search for a specified pattern in a column.
SELECT column_name(s) FROM table_name WHERE column_name LIKE pattern;
IN The IN operator allows you to specify multiple values in a WHERE clause.
SELECT column_name(s) FROM table_name WHERE column_name IN (value1,value2,...);
Adding data
INSERT INTO The INSERT INTO statement is used to insert new records in a table.
INSERT INTO table_name VALUES (value1,value2,value3,...);
Deleting data
DELETE The DELETE statement is used to delete rows in a table.
DELETE FROM table_name WHERE some_column=some_value;


DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
Schools Minister Nick Gibb sets out proposals to overhaul education for pupils of 16 and over.
Schools Minister Nick Gibb today set out proposals to overhaul post-16 education and give students the best chance to go on to university or skilled employment.
Consultations launched today by the Department for Education and the Young People’s Learning Agency ( YPLA ) recommend that:
More than a third were unhappy with levels of numeracy.
The reforms follow recommendations made by Professor Alison Wolf in her review of vocational education. She said more than 300,000 16- to 19-year-olds were on courses which did not benefit them.
Schools Minister Nick Gibb said:
The current funding system is plagued by perverse incentives that have diverted some students towards weaker qualifications.
Every student should be taking courses which give them the best chance of succeeding in life.
The current system incentivises schools and colleges to load too many students with low-quality, small or easy qualifications, often in random combinations, that employers do not value.
These reforms will ensure young people are given the information they need to take the courses that benefit them, and that all courses available broaden rather than narrow their options.
It is time the system was fairer and simpler - and worked in favour of young people.
He added:
Too many young people are dropping English and maths before they have secured a good grounding. These vital subjects are critical to the economy and as a country we need all our young people to be fluent and comfortable in these basic skills.
The proposed 16 to 19 funding formula would see schools and colleges paid on a per student basis. Currently further education colleges, sixth-form colleges and school sixth forms are paid by the number of qualifications a student takes. Professor Wolf said this meant some schools or colleges maximised income by “piling up” young people with low-quality qualifications which did not offer progression.
The existing ‘success payments’ system would also be amended to discourage providers from placing students on courses that are easy to pass but which do not lead to skilled employment or further education. The funding for disadvantaged students would be adapted so that it aligns with the principles of the pre-16 pupil premium.
The consultation sets out the characteristics of high-quality study programmes for 16- to 19-year-old students.
It says programmes:
The chairman of the YPLA , Les Walton, said:
Our stakeholders have been calling for these reforms for a number of years and I am delighted that through our work with the department we are able to present these proposals to the wider sector. The system can be fairer, more simple and transparent. It will require all partners to work differently and it is vitally important that we do this to ensure the best futures for all our young people.
The Department for Education is also seeking views on the provision of high-quality work experience for those aged 16 to 19, and on the proposed removal of the duty on schools to provide work-related learning for 14- to 16-year-olds. Professor Wolf said in her report that “the blanket requirement to give all KS4 pupils ‘work experience’… has served its time”. She said it was expensive and too often did not involve going to a workplace. Schools providing high-quality experience would still be able to provide it.
Professor Wolf said:
The government’s proposals recognise that maths and English are the most important vocational as well as the most important academic skills of all, and critical to young people’s success in life. The proposed funding reforms should enable innovation and responsiveness to local needs and demand and I look forward to watching new high-quality programmes for 16- to 19-year-olds develop in the years ahead.
A revised funding formula would support the policy objectives of:
The consultations close on January 4.
The independent Wolf review into vocational education was published in March this year.
The government response to Wolf was published in May this year.
Professor Alison Wolf is the Sir Roy Griffiths Professor of Public Sector Management at King’s College London, and specialises in the relationship between education and the labour market. She has been a specialist adviser to the House of Commons select committee on education and skills; is an academic member of the Council of King’s College London; writes widely for the national press and is a presenter for Analysis on BBC Radio 4.
The Young People’s Learning Agency was established by the Apprenticeships, Skills, Children and Learning Act 2009 and launched in April 2010. It is sponsored by the Department for Education and exists to support the delivery of training and education to all 16- to 19-year-olds in England, and oversees funding of academies.
In England, more than 1.6 million young people are in some form of education, the highest ever number, with 91.6% of 16- and 17-year-olds participating in education or work-based education and training at the end of 2010. Participation of 16-year-olds alone stands at 96.1%. In 2010, 4 out of 5 young people aged 19 were qualified to at least level 2 and more than half of all young people were qualified to level 3.
At the end of 2010, 141,800 16- to 18-year-olds were not in education, employment or training ( NEET ). This is 7.3% of all 16- to 18-year-olds.
The pre-16 pupil premium has been introduced to provide schools with extra funding to spend on interventions that can support the attainment of disadvantaged pupils. The Government remains committed to full participation in education and training for 16- and 17-year-olds, to raising the participation age to 17 in 2013 and to 18 in 2015. The question for post-16 funding is how it can be most effectively used to better support young people’s prospects, which are too often determined by home background and circumstance.
In 2010 only 55% of young people managed a C or better in GCSE English and maths. Of those who did not achieve this level by age 16, only 24% in English and 17% in maths went on to reach this level by age 19.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 6 October 2011


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| 1 |
Proposed Kenya National spatial Data Infrastructure (KNSDI)
complex at Survey of Kenya, Ruaraka, Kenya
|
The permanent Secretary, Ministry of Public Works, P.O. Box
30743 - 00100, Nairobi
|
813,398,857 |
Main Labour Contractor |
| 2 |
Proposed Development of National Intelligence Academy (NIA)
|
Director General (NIS) P.O. Box 30091 — 00100, Nairobi |
1,907,198,445 |
Main Labour Sub-Contractor |
| 3 |
Proposed construction and completion of fourteen 3BHK/4BHK houses
at shaba village mlolongo along Mombasa road. Block 1&2
|
Lafey construction co / Taran Contractors Amos / Dahil C.o.W / S
agent 0711 325 260
|
77,836,310 |
Main Sub-Contractor |
| 4 |
Proposed Research, Technology & Innovation Laboratories &
Related Infrastructure on Plot LR No. 209/5811, Dunga Road,
South B, Nairobi
|
The Director, Kenya Industrial Research and Development
Institute (KIRDI), P.O. Box 30650 - 00100, Nairobi
|
3,933,008,763 |
Main Labour Sub-Contractor |


http://www.speakers-podium.com/susangranger.
Susan Granger's review of "IDLE HANDS" (Columbia Pictures/Sony Releasing)
In the light of the tragic high-school massacre in Littleton, Colorado, this ill-timed horror-comedy is so ghoulish and repulsive that there's no excuse for it. Frankly, I think it should be voluntarily withdrawn by the studio. But, since it's on the screen in local theaters, here goes.   He kills his parents and many of those unlucky enough to be around him until he cuts off the homicidal appendage with a meat cleaver and microwaves it. First-time screenwriters Teri Hughes and Ron Milbauer and director Rodman Flender (TV's "Dawson's Creek," "Chicago Hope") devise numerous grisly, blood-drenched deaths and manage to have greenish corpses rise from the dead to become devilish, wise-cracking zombies. Then there's Viveca A. Fox, supposedly descended from a long line of Druid priestesses, who arrives in town with her sacred dagger. Plus Sawa's girl-friend, Jessica Alba, and the other freaked-out guests at a Halloween costume party who are doomed for disaster. The most offensive scene, however, pokes fun at the mourners at the funeral of two youngsters. On the Granger Movie Gauge of 1 to 10, "Idle Hands" is barely a distasteful, disgusting 1 - and definitely not for the squeamish. If this is what teenagers want as entertainment, as a society, we're in deeper trouble than we realize.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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--- title: "Week 5 Lab" author: "Drew Tyre" date: "2017-08-01" output: html_document weight: 803051 ---
In this exercise we’ll practice fitting glms and making predictions. The data is hanson_birds
in package NRES803
. This data set was collected by Andrea Hanson during her MSc. project in the School of Natural Resources. She sampled 14 transects in CRP fields scattered across SE Nebraska 4 times throughout the summer. She measured Visual Obstruction Readings (VOR) at 12 spots along each transect; these were averaged to obtain a single number representing vegetation structure within the CRP field. She also recorded the amount of 4 types of land use in the surrounding 2 km radius circle. These landscape metrics were highly correlated, so she used a Principle Components analysis to derive two combined metrics that were uncorrelated with each other. PC1 represents a gradient of increasing row crops, while PC2 represents decreasing woodland.
Begin your analysis of Dickcissels (column “DICK” in the data.frame) by plotting the response variable against all three covariates individually. Check for any signs of non-linearities.
library(NRES803)
data(hanson_birds)
library(tidyverse) # load all the things
library(gridExtra) # to put multiple plots on one page
# make up a theme object that does what I want
my_theme <- theme_classic() +
theme(text=element_text(size=rel(4)))
#some points overlap so use geom_count()
ggvor <- ggplot(hanson_birds, aes(x=vor, y=DICK)) +
geom_count() + geom_smooth() +
scale_size_area(max_size=4) +
guides(size=FALSE) +
my_theme
## OMG I just figured out how to make these plots
## with different x axis variables!!!
ggpc1 <- ggvor + aes(x = pc1)
ggpc2 <- ggvor + aes(x = pc2)
# geom_bar with stat="count" nice for discrete data
ggdick <- ggplot(hanson_birds, aes(x=DICK)) +
geom_bar(stat="count") + my_theme
grid.arrange(ggvor, ggpc1, ggpc2, ggdick)
Our response is discrete; only integer values occur. Moreover it is a count of events (seeing a bird), so no negative values occur. Both of these suggest that a Poisson distribution is a good choice.
So, we start by fitting a glm with a poisson error distribution using all three covariates and their pairwise interactions. Get the summary, and calculate the overall goodness of fit test to look for lack of fit. There aren’t many more models more complex than this for these data, so this will be our global model. We assess our assumptions with this model.
library(broom)
dick.1 <- glm(DICK~(vor + pc1 + pc2)^2,data=hanson_birds,
family=poisson)
sum.dick.1 <- glance(dick.1)
gof.p.dick.1 <- with(sum.dick.1,pchisq(deviance,df.residual,lower=FALSE))
So our model is significantly over-dispersed (\(\chi^2=\) 88.06, df = 49, p = 0.0005217). That means that the estimated standard errors of our model are too small. For the moment we will ignore this issue, but keep in mind that tests of significance with this model are biased towards rejecting null hypotheses.
We can check the linearity assumption by plotting the deviance residuals against each covariate.
dick.1.test <- augment(dick.1)
lin.vor <- ggplot(dick.1.test, aes(x = vor, y = .std.resid)) + geom_count() +
scale_size_area(max_size=4) +
geom_hline(yintercept = 0, linetype = 2) +
geom_smooth() + guides(size = FALSE) +
my_theme
lin.pc1 <- lin.vor + aes(x = pc1)
lin.pc2 <- lin.vor + aes(x = pc2)
grid.arrange(lin.vor, lin.pc1, lin.pc2)
That’s a bit squashed, but the horizontal line is inside the confidence polygon of the smooth fit throughout, so there is no indication that the linearity assumption is violated.
Finally, examine the residual plots.
resd1 <- ggplot(dick.1.test, aes(x = .fitted, y = .resid)) +
geom_point() +
geom_smooth() +
geom_hline(yintercept = 0, linetype=2) +
my_theme
resd2 <- resd1 + aes(y = sqrt(abs(.std.resid))) +
geom_hline(yintercept = 1, linetype=2)
resd3 <- ggplot(dick.1.test, aes(.hat, .std.resid)) +
geom_vline(size = 2, colour = "white", xintercept = 0) +
geom_hline(size = 2, colour = "white", yintercept = 0) +
geom_point(aes(size = .cooksd)) + geom_smooth(se = FALSE) +
my_theme
# get int and slope for qqline
probs <- c(0.25,0.75)
y <- quantile(dick.1.test$.std.resid, probs, names = FALSE, na.rm = TRUE)
x <- qnorm(probs)
slope <- diff(y)/diff(x)
int <- y[1L] - slope * x[1L]
qq.dick <- ggplot(dick.1.test, aes(sample=.std.resid)) +
geom_qq(size=rel(4)) +
geom_abline(intercept = int, slope = slope, linetype = 2, size = 2) +
my_theme
grid.arrange(resd1, resd2, resd3, qq.dick)
Nothing particularly worrisome there.
Although this model is overdispersed, we’ll work with it for now. We can use the ratio of the summed Pearson residuals to residual degrees of freedom to correct the AICc values. First we want to create a list of possible models. With 3 main effects and their 3 interactions, we have 48 possible models to consider. That is far too many for such a limited dataset. Background knowledge suggests that we know vor will be important, so all models we consider should include that effect. Then we’ll add each of the landscape variables in turn, together with the interaction with VOR.
models = list(DICK~1,DICK~vor,
DICK~vor + pc1,
DICK~vor + pc1 + vor:pc1,
DICK~vor + pc2,
DICK~vor + pc2 + vor:pc2,
DICK~vor + pc1 + pc2,
DICK~vor + pc1 + pc2 + vor:pc1,
DICK~vor + pc1 + pc2 + vor:pc2,
DICK~vor + pc1 + pc2 + vor:pc1 + vor:pc2,
DICK~(vor + pc1 + pc2)^2)
fits = lapply(models,glm,data=hanson_birds,family="poisson")
We could calculate \(\hat{c}\) manually, but package AICcmodavg has a function to do it for us, and we’ll use it to get a table of the Quasi-AICc values.
library(AICcmodavg)
library(pander)
# get shorter model names (remove DICK ~)
modnames=sapply(models,function(ff)deparse(ff[[3]]))
pander(aictab(fits,c.hat=c_hat(fits[[11]]),modname=modnames), caption="Table 1. AICc model selection table for Dickcissel relative abundance in CRP fields.", split.tables=Inf)
The top model includes pc2 and it’s interaction with vor. In fact, the four top models all include those terms. The second through fourth ranked models are all within 2 quasi-likelihood units of the top model, suggesting that nesting is playing a role here. Thus it seems justifiable to simply use the top model as our model for prediction. However, model averaging the predictions is quite easy given the AICcmodavg package, so lets try both ways and see how much it matters. As before, create some new data assuming pc1 and pc2 are at the median values:
nd = data.frame(vor=seq(2,9,0.1),
pc1=median(hanson_birds$pc1),
pc2=median(hanson_birds$pc2))
pred.dick = augment(fits[[6]], newdata = nd, type.predict="response")
pred.mavg = modavgPred(fits, newdata=nd, type="response",
c.hat=c_hat(fits[[11]]),modnames=modnames)
pred.dick$mod.avg.pred <- pred.mavg$mod.avg.pred
pred.dick$uncond.se <- pred.mavg$uncond.se
ggplot(hanson_birds, aes(x = vor, y = DICK)) +
geom_count() +
scale_size_area(max_size = 4) +
geom_line(aes(y=.fitted), data=pred.dick, size=2) +
geom_line(aes(y=mod.avg.pred), data=pred.dick, size=2, linetype = 2) +
my_theme + guides(size=FALSE)
So the model averaged prediction is right on top of the top model prediction in this case. Let’s look at the model coefficients for those top four models
# poo. easy way with stargazer doesn't play with tidyverse
# make a dataframe
tidy_w_name <- function(.x, .y){
df <- tidy(.x) #get the coefficient table
df$modname <- .y #add a column with the modname
df # return df
}
# just the top 4 models to save grief later
etc <- map2_df(fits[c(6,9,10,11)], modnames[c(6,9,10,11)], tidy_w_name)
etc <- mutate(etc, Estimate = paste(formatC(estimate,digits=3, format="f")," (", formatC(std.error,digits=3, format="f"),")", sep=""))
tab <- spread(etc[,c(1,6,7)], key=modname, value = Estimate )
# arrange the models in order ...
pander(tab[,c(1, 5:2)], split.tables=Inf, caption="Table 2. Estimates (SE) for each of the top four models.")
The row ordering of that table isn’t the best, but it’ll work for now. What I want to do is check to see if any of the coefficients from the second through fourth ranked models have estimates more than 2 SE from zero. For example, pc1
is at most 2 times the SE in those models. The value of the other two coefficients pc1:pc2
and vor:pc1
also have small magnitudes relative to their SE. This increases our confidence that nesting effects are responsible for the model selection uncertainty. Ignoring the model selection uncertainty might make our predictions a bit too precise, but the parameters vor
, pc2
and vor:pc2
are very similar across the four models.
Finally, I want to make a plot showing the prediction from the top model that demonstrates the interaction with pc2
. To do this, I’ll make predictions from the lower quartile, median and upper quartile of pc2
for a range of vor.
nd = crossing(vor=seq(2,9,0.1),
pc2=quantile(hanson_birds$pc2))
nd$pc2Labels <- factor(rep_len(c("Min","25%","50%","75%","Max"), nrow(nd)))
pred.dick = augment(fits[[6]], newdata = nd, type.predict="response")
ggplot(hanson_birds, aes(x = vor, y = DICK)) +
geom_count() +
scale_size_area(max_size = 4) +
geom_line(aes(y=.fitted, colour=pc2Labels), data=pred.dick, size=2) +
my_theme +
guides(size=FALSE) +
theme(legend.text=element_text(size=16))+
scale_color_discrete(breaks=c("Min","25%","50%","75%","Max"), name="PC2") +
scale_y_continuous(limits=c(0,25)) +
xlab("Visual Obstruction Reading") +
ylab("Dickcissel Abundance")
I spent a long time fussing around to make that graph look like I wanted. The defaults are often nice but sometimes you want something specific. To see the “quick and dirty” starting point for that graph run the first 4 lines leaving out the final ‘+’.
Working with binary data is similar, but back-transforming the link function is a bit more involved. Also the global goodness of fit test doesn’t work when the data are zero/one. The data are isolation
, which has three variables, incidence (presence/absence of a species), area of island (km^2), and isolation (distance to mainland in km). This exercise is modified from pg. 595 of Michael Crawley’s “The R Book”.
data(isolation)
names(isolation)
geom_boxplot()
).Ecological Thought provoker - Do you expect there to be effects of area and isolation, and in which direction should they go?
Next we’ll fit a binomial GLM, and then simplify using backwards selection.
isolation.1 = glm(incidence~area * isolation, data=isolation,family=binomial)
isolation.2 = glm(incidence~area + isolation, data=isolation,family=binomial)
anova(isolation.2,isolation.1,test="Chisq")
So the simpler model is not worse than the more complex model. Check the summary to find the weakest single term, and continue with the backwards selection.
summary(isolation.2)
# area has the smallest z score
isolation.3 = update(isolation.2,.~.-area)
anova(isolation.3,isolation.2,test="Chisq")
So removing area
does make a significant difference, so we stop with isolation.2
.
Ecological thought provoker - Does the sign of the coefficients for area and isolation match your expectations from the boxplots?
Next we want to plot the effects.
nd = data.frame(area=seq(0,9,0.01),isolation=median(isolation$isolation))
pred.p <- augment(isolation.2,newdata=nd, type.predict = "response")
ggplot(data = pred.p,
mapping = aes(x=area)) +
geom_line(mapping = aes(y = .fitted)) +
geom_rug(data = isolation[isolation$incidence>0,], sides="t") +
geom_rug(data = isolation[isolation$incidence==0,], sides="b") +
xlab("Island Area [km^2]") +
ylab("Probability of Presence")
The “rug plots” are a way to show where the data points are located along the x-axis without cluttering up the graph too much.
Make a plot of the effect of isolation on incidence when island area is held at the median value.
Adding confidence limits to this sort of plot requires making the prediction on the linear predictor scale, constructing the confidence limits, and then back-transforming the predictions to the response scale using the inverse link function. There are several ways to write the inverse link function for a logit link model, but the easiest to use in my opinion is \[ \hat{p} = \frac{1}{1+e^{-\beta X}} \] where \(\beta X\) is the prediction on the link scale.
nd = data.frame(area=seq(0,9,0.01),isolation=median(isolation$isolation))
pred.p <- augment(isolation.2,newdata=nd,type.predict="link",se_fit=TRUE) %>%
mutate(p = 1/(1+exp(-.fitted)),
lowerci = 1/(1+exp(-(.fitted-2*.se.fit))),
upperci = 1/(1+exp(-(.fitted+2*.se.fit)))
)
ggplot(data = pred.p,
mapping = aes(x=area)) +
geom_line(mapping = aes(y = p)) +
geom_ribbon(mapping = aes(ymin=lowerci, ymax=upperci), alpha=0.5) +
geom_rug(data = isolation[isolation$incidence>0,], sides="t") +
geom_rug(data = isolation[isolation$incidence==0,], sides="b") +
xlab("Island Area [km^2]") +
ylab("Probability of Presence")
What would happen if we used AIC model selection on this dataset?
models = list(incidence~1,
incidence~area,
incidence~isolation,
incidence~area + isolation,
incidence~area + isolation + area:isolation)
fits = lapply(models,glm,family=binomial,data=isolation)
aictab(fits,as.character(models))


| Table 1 |
The average growth period of apple tree in different subarea ... |
| Table 2 |
Growth stages and crop coefficients of apple trees. |
| Table 3 |
Summary of 21 general circulation models from coupled model ... |
| Table 4 |
The statistics of water balance and water suitability (1990~ ... |
| Table 5 |
The statistics of water balance and water suitability (2019~ ... |
| Table 6 |
Contribution rate and sensitivity coefficient of four meteor ... |


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