Despite one cancelled flight, one unplanned night in Downtown Chicago and switching planes three times until boarding one that was safe, I finally landed in Lexington, KY – 24 hours later than scheduled, but excited for the last leg of my Sunline Scholarship adventure.
I was welcomed by temperatures consistently in the mid-30s with high humidity. It was a big shock to the body coming from an 'Irish summer. I am single-handedly keeping Powerade in business.
After a quick farm tour of Taylor Made on my first day, it was apparent that this was another massive operation. With over 1,600 acres, the Taylor family's farm boards broodmares, stands stallions, preps yearlings, and delivers foals into the hundreds every year. Then, I was told how many horses we would be selling at Keeneland in September – up to 500! There isn't a consignor in the world that sells this many horses at a single auction.
It was straight to work for me at Yearling Complex C – once the home of Triple Crown hero American Pharoah. Taylor Made was chosen to prep the champ and consign him in the 2013 Fasig-Tipton Select Yearling Sale where he was bought back by his owners for $300,000 - I bet that is one horse the owners are glad they retained!
It is a bit surreal working in the same barn that a Grand Slam* winner was raised in. The people that I work with tell me stories of him as a yearling, making me feel closer to a legend of the track.
A majority of the farm staff are Hispanic, creating a language barrier. I have some trouble understanding what they are saying and they have a few troubles as well, mostly with pronouncing my name. For the first few weeks, I was just known as 'bro', a word I think my predecessor Benji King used a lot.
In the morning, we muck out first thing, a great workout to start the day. The day to day work involves putting every horse on the walker, washing them daily, grooming and treatments. All yearlings are turned out at night, usually at 7:00 pm.
The yearlings we prepare are offered at the Fasig-Tipton Saratoga Sale and Keeneland's September Sale. I was lucky enough to get the call to go to Saratoga, which led to a cool 16-hour trip in a horse float (or horse van as they call them over here), travelling in the back with the horses. I was literally travelling cattle class, but I wasn't complaining. I was going to the famous Saratoga that I had heard so much about.
Saratoga is considered a boutique sale and attracts yearlings with early birth dates, but also a standard of quality. One horse at the Sale broke seven figures and the average price for the Sale was $292,115.
Taylor Made had 24 horses at the Select Sale in Saratoga. I was a bit shocked when I was told I had one horse to look after. I did everything with this one horse; from showing it, feeding and mucking its stall out. It was a big change from back home where I am used to running around like a headless chook, going from one horse to another, trying to keep up.
I was thoroughly impressed by Taylor Made in the way they look after their clients. They offer so much information and make a point to be on hand to everyone that comes to look at the horses. They are great sales people and it became quickly evident why they have become the largest sellers in the world.
From all accounts, we had a very good sale – the bosses seemed very happy. There were no star seven-figure horses from our draft, but an even bunch of horses that took a lot people's eye and attracted good competition.
Besides the high caliber horse flesh, Saratoga is also famous for its social scene. Located in upstate New York, Saratoga attracts a large population for the summer and big crowds attend the race meetings. With a convergence of horse-loving people, there is always someone to have a drink with.
After 10 days in Saratoga, I am back in Kentucky now, gearing up for the big one – three weeks of selling at Keeneland. On each horse's box (or stall as they call it over here) is their Hip Number (Lot number). To give some perspective, the highest Hip Number in my barn is in the mid-4000s.
Driving around the outskirts of Lexington, it is amazing to see all of the picturesque and famous farms around the area - farms steeped in history such as Claiborne Farm, Lane's End, Gainsway, Three Chimneys, along with farms that are making a presence in the Southern Hemisphere such as WinStar, Spendthrift and Stonestreet.
On my first spare day, I headed out to Coolmore's Ashford Stud for a farm tour and to meet the stallions. The big attraction was American Pharoah, and what a star he is. He has the most extraordinary demeanor and handles his many visitors like a true professional.
I also saw the champion sire Giant's Causeway, former shuttler Declaration of War, young sire Shanghai Bobby and my favourite, Uncle Mo – an imposing individual who has made an incredible start to his stallion career, siring this year's Kentucky Derby winner Nyquist.
A future Taylor Made stallion and active racehorse that everyone knows is California Chrome, who is part-owned by Taylor Made. He not only won the Kentucky Derby and the Preakness as a three-year-old, this amazing racehorse has trained on to be fantastic at four, and now, five years old winning major races like the Dubai World Cup which secured his spot as North America's number one horse in earnings with over $13.2 million. It is great to see a champion entire train on and race as an older horse. I am sure that Taylor Made will reap the rewards when he finally retires to stud as he has gained a cult following by people proudly known as 'Chromies'.
As with the UK and Ireland, I have met incredible people and made great contacts. The thoroughbred industry truly is global. It has been great to see old friends and make new ones.
I prepare myself for the next month and I am excited at the challenge and all the fun that Keeneland will bring.
*The Grand Slam of American horse racing consists of four races; the Kentucky Derby, the Preakness Stakes, the Belmont Stakes, and the Breeders' Cup Classic. American Pharoah is the first horse in history to win these four races.


text:
National League NorthNational League SouthFA Community ShieldEFL TrophyPlayoffsPremier League 2 Div. 1Premier League 2 Div. 2Professional Development League
Women's Super League [Wo.]FA Women's Cup [Wo.]
Premier League [U18]Premier League Cup [U18]FA Youth Cup [U18]Professional Development League [U18]Youth Alliance [U18]
EnglandEngland Amat.England BEngland [Wo.]England [U21]England [U20]England [U20 (W)]England [U19]England [U19 (W)]England [U18]England [U17]England [U17 (W)]England [U16]England [U16 (W)]England [U15]England [U23 (W)]
Liverpool FC » Manager history
Period Manager Country born
08/10/2015 - 30/06/2026 Jürgen Klopp 16/06/1967
01/06/2012 - 04/10/2015 Brendan Rodgers 26/01/1973
08/01/2011 - 16/05/2012 Kenny Dalglish 04/03/1951
01/07/2010 - 07/01/2011 Roy Hodgson 09/08/1947
16/06/2004 - 03/06/2010 Rafa Benítez 16/04/1960
16/07/1998 - 24/05/2004 Gérard Houllier 03/09/1947
31/01/1994 - 12/11/1998 Roy Evans 04/10/1948
16/04/1991 - 28/01/1994 Graeme Souness 06/05/1953
22/02/1991 - 15/04/1991 Ronnie Moran 28/02/1934
02/07/1983 - 29/05/1985 Joe Fagan 12/03/1921
26/08/1974 - 01/07/1983 Bob Paisley 23/01/1919
01/12/1959 - 12/07/1974 Bill Shankly 02/09/1913
05/05/1956 - 17/11/1959 Phil Taylor 18/09/1917
23/03/1951 - 04/05/1956 Don Welsh 25/02/1911
06/08/1936 - 31/01/1951 George Kay 21/09/1891
07/03/1928 - 05/08/1936 George Patterson 00/00/1887
13/02/1923 - 15/02/1928 Matt McQueen 18/05/1863
17/12/1919 - 12/02/1923 David Ashworth 02/06/1867
17/08/1896 - 06/05/1915 Tom Watson 09/04/1859
15/02/1892 - 16/08/1896 William Barclay 14/06/1857
15/02/1892 - 16/08/1896 John McKenna 03/01/1855
Liverpool Football Club
stadium: Anfield
Homepage: www.liverpoolfc.com/
Liverpool FC (R)
Liverpool FC U23
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Liverpool FC [Youth]
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Tweets by @LFC
Brighton dumped FA Cup holders Liverpool out of the competition on Sunday as Hollywood star and Wrexham co-owner Ryan Reynolds saw his non-league side denied a dramatic win by Sheffield United in a six-goal thriller.... more »
Jurgen Klopp has urged Liverpool to learn the lessons of their Brighton "horror show" as they return to the south-coast in the FA Cup fourth round.... more »
Manchester City and Arsenal take a break from an intense Premier League title race this weekend -- only to face each other in a heavyweight FA Cup clash at the Etihad.... more »
Sadio Mane returns to training with Bayern Munich
Bayern Munich forward Sadio Mane returned to training on Tuesday, as the Senegalese international continued his recovery from a leg injury which forced him out of the World Cup.... more »
Can Klopp halt Liverpool slump?
Jurgen Klopp admitted this week the time for change at Anfield is coming, but he is going nowhere as the revered German coach tries to turnaround an alarming dip in form.... more »

redpajama_set_name: RedPajamaCommonCrawl


Owning and operating your own business is a difficult thing to master, especially if your business relies on field service. Between scheduling service calls, managing an on call staff, and handling customer service, the paperwork involved on a daily basis can present an overwhelming challenge. If your pest control business is currently suffering from an invasion of paperwork, maybe what you need is a paperwork exterminator: field service management software from Call of Service.
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Pest control is a challenging business to manage. Your clients are already in a difficult position by the time they call an exterminator, and scheduling delays are sure to result in unhappy customers. With Call of Service field service management software, you'll be able to quickly schedule appointments and retain customer information all within our cloud-based service. Register for your free 21-day trial today, and eliminate the paperwork causing problems of your pest control business today.
Low cost, high return and efficient job management software - what all service-oriented businesses need.
Centralized work order management with remote access from any internet connected device.
Dispatch detailed work orders to your contractors with ease, Alerts for your contractors when their work orders have been rescheduled or updated with new information.
Two-way updating of work orders to ensure both your work order coordinators AND your deployed workforce are on the same page at all times.
Call of Service provides the latest In-Field Service workforce scheduling software. Specifically designed for ease of use, our cloud-based service will shave hours off your valuable supervisor's schedules, optimize labor costs, and keep your client's information a few clicks away.
© Copyright 2018 by Ninety Nine PTY. LTD. All Rights Reserved.


Rosie's Daughters flung wide the doors of employment opportunity that Rosie had unlocked. Their stories show how the post-war education boom, sexual revolution, civil rights and gender equality, the Vietnam War, consciousness raising, Roe V. Wade, no-fault divorce and other events influenced their lives and shapes their journeys.
Authors Matilda Butler and Kendra Bonnett have rewritten the rules for book design. They push beyond conventional format to give the first-person stories of the more than 100 women interviewed a multi-dimensional context within the historical narrative that spans six decades of change- social, cultural, economic and political. It's a feast for mind and eye.


Using a Dress Form for Knitting on Craftsy!
I've considered buying a dress form for years, but I always thought it would only be helpful for my sewing projects. Like a lot of other supplies we think of as strictly for sewers — seam rippers, buttons, thread — dress forms can actually be super helpful for knitting, too!
I discovered just how helpful they could be when my boyfriend bought me one for my birthday in December. Since then, I've used it for both sewing and knitting, and it's equally helpful for both. I'm not just talking about fitting here, either. There are lots of ways a dress form can come in handy.
Don't let the sewers have all the fun. Here are a few ways that dress forms can help you with your knitting.
Well this is obvious, isn't it? The dress form's main purpose is to help us with fitting issues. Trying on a sweater as you knit isn't always possible. For me the issue is that I'm using a circular needle that holds all the stitches but isn't long enough to slip over my shoulders. With a dress form, I can slip the sweater over more carefully and even make the shoulders narrower until the needle is safely over them.
This was the case with the Lace Wedge Yoke Sweater from Eunny Jang's Choose Your Own Sweater Adventure class that I knitted. I was checking some bust measurements, but I still had the sweater on a 16″ circular needle, so I didn't want to risk putting that over my head. After adjusting some of the dials, I was able to put it over the dress form without having any live stitches to deal with afterwards.
You can try on the garment, sure. But can you see that pucker in the back or that gap in the seam of the sleeve? Trying the garment on the dress form lets you see it from every angle and examine it much more closely than when it's on your body. Plus, you can tuck, pin, darn, or do whatever you need to do in order to remind yourself where the issue is. No need to wait for a buddy to pin it for you!
It can be difficult to photograph your finished garments. Even if you have a friend to help, it's a pain to schedule photography time with them, find just the right light — you know how it goes. I fussed with a tripod and a timer on the camera for years, trying to take just the right photo of me wearing the garment, but the photos never turned out as great as I hoped they would.
Instead of messing with all that, just display that garment on the dress form with a nice background behind it. And hey, if you can find just the right light and just the right photographer to photograph you wearing the garment, use that photo too!
I often make larger swatches and pin them to the dressform to see how they're going to drape. This is especially helpful for shawl patterns when I'm using a yarn I'm not familiar with. You can even pin several swatches on there at once to compare. It's really helpful when you're feeling indecisive about a yarn choice. Sometimes it can even help with color choice.
Are you itching to get a better fit for your garments?
Try Knit Lab: Fit Your Knits with Stefanie Japel to get the down low on how to measure your body, plus how to remedy tight busts, boxy sweaters, and more.
Do you use a dressform for your knitting? Do you find it helpful?
I was wondering about using a dress form for blocking a sweater knit in the round. Is that feasible?


The 1st Amendment is about Freedom of Religion, the Freedom of the Press and Freedom of Expression. It is also referred to as the Establishment Clause.
Summary: The 1st Amendment states that people have the right to petition the Government and the right of freedom of religion, freedom of speech, freedom of the press and freedom of assembly (meeting).
George Washington was the 1st American President who served in office from April 30, 1789 to March 4, 1797. One of the key events during his presidency was the ratification of the 1st Amendment to the Constitution. The first 10 Amendments to the Constitution are collectively known as the Bill of Rights. The Bill of Rights, which includes the 1st amendment, was ratified (meaning approved) on December 15, 1791.
The cause or reason for this addition to the Constitution was due to the tyranny of the British who had suppressed free speech and the right to have public meetings. Americans also wanted to ensure their right to religious freedom, a reason why many had travelled thousands of miles to their new home in America.
Meaning: It prevents the government from imposing an official religion in America leaving citizens free to choose the religion of their choice, or to choose not to have a religion.
Meaning: It prevents the government from passing any laws that might stop people from saying what they think.
"Freedom of the press... "
Meaning: It prevents the government from passing any laws that prevent access to information. Although this originally applied to newspapers, leaflets and books, in modern times it also applies to expressing opinions across all media, including TV and the internet.
"Right of People to assemble..."
Meaning: This allows people to meet together in public or in private to express their opinions and their ideas freely and without fear of retribution.
"Petition the Government for a redress of grievances"
Meaning: This allows American citizens to express their opinions to the government, especially in situations where they believe are unjust. It allows people to petition government to 'right a wrong'.
The Right of Petition was highlighted by the 1836 Gag Rule.
Who wrote the 1st Amendment of the Constitution?
James Madison, the "Father of the Constitution", wrote the 1st Amendment in 1789 as one of the first 10 amendments collectively known as the Bill of Rights. The 1st amendment about Freedom of Religion, Press and Expression was ratified on December 15, 1791.
The article on the 1st Amendment of the Constitution provides the text, definition and history of one of the major achievements of his presidential term in office. The following video will give you additional important facts and dates about the political events experienced by the 1st American President whose presidency spanned from April 30, 1789 to March 4, 1797.


text:
class CreateCieloRegularPayments < ActiveRecord::Migration
  def change
    create_table :spree_cielo_regular_payments do |t|
      t.string :tid
      t.string :authentication_url
      t.string :cc_type
      t.string :instalments
      t.string :product_type
      t.integer :status
      t.timestamps
    end
  end
end

redpajama_set_name: RedPajamaGithub


text:
Q: Lazy loading working in non-transactional class/method in Hibernate I am working in Spring-Hibernate application. Flow is as usual: Controller --> Service --> DAO.
I have annotated Service layer class with @Transactional, thereby marking every method transactional in that class.In service class, I made a DAO call to get some domain object and then converting it into DTO/VO object which will be passed to controller.For converting domain object to DTO, I have written another custom static class(class having only static methods) like ObjectMapper which will do this conversion.
Now, domain object has some child object(One to Many) which is lazily loaded. So, when in ObjectMapper, i access that child getter method, an extra database call is issued, which is working fine.
What I don't understand is that since ObjectMapper is not transactional, I was expecting some exception to be thrown like Session is closed while making database call to fetch child object from database.I am using getCurrentSession of Session Factory in DAO.
Can someone please explain me this behavior?

A: I suppose you either call your ObjectMapper from the transactional Service method (you should) or if not, maybe you are enabled  "hibernate.enable_lazy_load_no_trans" which keeps the hibernate session open

A: As long as the call to the static class/method, which transforms your objects, is made in the transactional method of the DAO, the session is still open and will be used for the database calls.

redpajama_set_name: RedPajamaStackExchange


text:
'A Quiet Place' Sequel To Be Written By John Krasinski
By eelyajekiM | @ | October 22nd, 2018 at 2:00 pm
A sequel to A Quiet Place was given the greenlight almost the instant the first film came out in theaters. Paramount CEO Jim Gianopulos said about the announcement, "We're thrilled to say that we're already working on the sequel to A Quiet Place."
While the sequel was confirmed, it wasn't clear if original director and co-writer John Krasinski would return. But, it looks like he will be back in some capacity. He will write the script. More on the report below.
Topics: Movies, News, Sequels
Tags: A Quiet Place, A Quiet Place 2, John Krasinski, Paramount Pictures
'A Quiet Place' Sequel In The Works
By eelyajekiM | @ | April 26th, 2018 at 2:05 pm
John Krasinski's A Quiet Place was already predicted to be a hit with audiences even before it was going to be released. And now that the relatively low-budget film is one, it should come as no surprise that Paramount has already greenlit a sequel.
The studio made the announcement during their panel at CinemaCon in Las Vegas, yesterday. More on the story below.
Tags: A Quiet Place, A Quiet Place 2, Andrew Form, Brad Fuller, Bryan Woods, Jim Gianopulos, John Krasinski, Paramount Pictures, Scott Beck
Amazon Greenlights 'Tom Clancy's Jack Ryan' Season 2 Months Ahead Of Season 1 Arrival
By The Movie God | @ | April 25th, 2018 at 2:00 pm
The first season of the upcoming Amazon series Tom Clancy's Jack Ryan is still four months from release, but Amazon has confidently given the greenlight to a season two already.
The move comes after a Super Bowl ad for the show led to a significant increase in Prime subscribers adding Jack Ryan, which stars A Quiet Place co-writer, director, and star John Krasinski in the title role, to their watchlists.
You can read much more on the announcement and watch the aforementioned Super Bowl spot if you missed it below.
Topics: News, Television
Tags: Amazon, Amazon Prime Video, Carlton Cuse, Graham Roland, Jack Ryan, John Krasinski, Tom Clancy's Jack Ryan, Wendell Pierce
NYCC 2017: 'Tom Clancy's Jack Ryan' Panel With The Cast & Creators
By Maximus Prime | October 8th, 2017 at 9:00 am
The crowd was buzzing at the start of Amazon's panel for their new scripted drama, Tom Clancy's Jack Ryan, as its lead star and executive producer John Krasinski — who plays the titular character — hit the stage. Along with moderator Terry Schwartz, Krasinski was accompanied by co-star Abbie Cornish, and executive producers Carlton Cuse and Graham Roland.
Fans were treated to a couple of scenes from the show, the initial clip being the first seven minutes of the premiere episode.
Topics: Conventions, News, NYCC, Television
Tags: Abbie Cornish, Amazon, Carlton Cuse, Graham Roland, Jack Ryan, John Krasinski, New York Comic Con, Tom Clancy, Wendell Pierce
NYCC 2017: 'Tom Clancy's Jack Ryan' Interview With Stars & Producers
By Pfeff-Bot | October 7th, 2017 at 6:00 pm
Amazon Prime Video has become a competitive powerhouse in online streaming, and Prime members will get another thrilling drama to add to their queues in 2018: the latest iteration of Tom Clancy's Jack Ryan starring executive producer John Krasinski (13 Hours, The Office), Wendell Pierce (Ray Donovan, The Wire), and Abbie Cornish (Limitless). Geeks of Doom had the chance to sit down at New York Comic Con 2017 with Pierce and Cornish, along with showrunner and executive producer Carlton Cuse (Lost, The Strain, Bates Motel) and executive producer Graham Roland (Almost Human).
Topics: Conventions, Features, Interviews, NYCC, Television
Tags: Abbie Cornish, Amazon, Amazon Prime, Carlton Cuse, Graham Roland, Jack Ryan, John Krasinski, Tom Clancy, Tom Clancy's Jack Ryan, Wendell Pierce


Below you can see glyphs 2peas remote control font. You can download this web font for free. Just click to "Download" button. Font has 2peas remote control style. Also you can download related fonts: 2peas Remote Control. Share 2peas remote control font with your friends!


text:
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In which I am a finalist for an award of which I was previously unaware — My novella "A Long Walk Home" was a finalist for the Hawthorne Citation. Their Web site says, Welcome to the Hawthorne Citation, which recognizes Excellence in Short Fiction, English-language. Now in our third year (2012) of the annual prize, we've cited some of the great short works in those years, from both well-known writers and yet-to-be-known writers.
Timothy Anderson Art — ZOMG, such retro art reconcepting for some of my favorite books and movies. I lust for the Argonath travel poster. (Thanks to Lisa Costello.)
Raymond Chandler's Private Dick — Ta-Nehisi Coates with an incredible essay on misogynist and masculinity, using Raymond Chandler's work.
Apple fires Maps manager — They waited this long? I cannot wait til Google Maps comes back to the iPhone and I can dump the ridiculously flawed Apple Maps. Apple's biggest failure of both delivery and consumer trust in years, at least that affected me personally.
Can You Crack The Pigeon's Code? — A WWII mystery comes to light. Or not, as the case may be.
Time for the U.S. to Partner with China in Space?
Earth's Core & Carbon: Planetary Center Holds Largest Store Of Common Element, Computer Simulation Suggests — Talk about sequestration.
Tobacco companies ordered to admit they lied over smoking danger — US judge says tobacco firms must spend their own money on a public campaign admitting deception about the risks of smoking. Let's see if this sticks.
Can Culture Create Mental Disease? The Rise of "Hikikomori" in the Wake of Economic Downturn in Japan — Can culture create mental disease? Look at gun fetishism and religious mania in the U.S. Both are heavily culturally influenced, and both have broad swathes of adherents who go way beyond reasonable and well-socialized behavior. (Snurched from Daily Idioms, Annotated.)
The Simpsons' take on Obamacare — Hahahah. (Thanks to Lisa Costello.)
Enclaves, Long Gated, Seek to Let In Storm Aid — So the wealthy who wanted to go Galt in their gated communities need public assistance after all? I wonder if Tea Partiers in the tri-state area are expecting to have the government help repave their roads and restore their utilities. Pretty tough for me to find much sympathy here.
'Change is a mofo': The GOP and white evangelicalism are in the same situation — The Christian Right rendered themselves unto Caesar, and in doing so lost the respect of younger generations for the church as a whole.
30+ Examples of Christian Privilege — Yep. And like most forms of privilege, completely invisible to the vast majority of its beneficiaries. Because with 73% of the population, and 89.3% representation in Congress, many Christians and Christianists are pleased to fancy themselves as a persecuted minority. It's a comforting and self-valorizing narrative which is absolutely laughable from outside their own framework. (Thanks to Lisa Costello.)
Dying for the Right to Choose — Ta-Nehisi Coates on the conservative view of abortion. Walsh was lying in his refusal to admit that women actually do die during the work of pregnancy. But his position — "without exceptions" — strikes me as the honest one. The problem here isn't packaging. There is no way to honestly modify its import. Either you believe that women who have sex should run the risk of being remanded to potentially lethal labor, or you don't. No exceptions.
Fox News Exec VP: Guest Who Slammed Network Apologized – But Not Publicly — Or maybe not. Looks like another Fox lie. Unlike Ricks, who stated a simple, obvious fact when he said on the air that Fox News was "operating as a wing of the Republican party". As we have long seen proven over and over again, in conservative America you lie boldly (as FOX has previously sued to preserve their right lie under the First Amendment) but apologize for telling the truth. FOLLOWUP: "[The FOX News] MO is that when the facts aren't on their side, they attack the person." Hahah. Ad hominem attacks are the conservative MO in general, given that the facts are rarely on their side in liberally biased reality. Ever listen to Rush Limbaugh? Or John McCain on Susan Rice? Mocking people is much more fun than responding substantively, and it helps generate more angry white men, which in their own words is the GOP's key electoral strategy.
RINO Hunters Fire Warning Shots At Republican Senate Candidates — That Tea Party farce the GOP dreamed up to help mobilize their base against the threat of Kenyan Muslim socialism is biting them in the ass again, as it did in this election cycle just past. I'd say it couldn't happen to a nicer bunch, except sometimes the far right gets their people elected, and that is a major disaster for everyone, including the oblivious chumps in the Tea Party themselves.
Notes on the Decline of a Great Nation — A European view of our country. The United States is frittering away its role as a model for the rest of the world. The political system is plagued by an absurd level of hatred, the economy is stagnating and the infrastructure is falling into a miserable state of disrepair. Gee, which party has made hatred a key electoral strategy? Which party has blocked almost all efforts at economic stimulus? Which party refuses to allow the government spend money on infrastructure? Would that be the party whose highest priority these past four years was to make Obama a one-term president, and thus spent all their political capital denying him anything that might make him look successful? Rather than, say, fixing this country's problems? Nope, can't quite put my finger on it. Must be Kenyan Muslim socialism at work. Ask any of Sarah Palin's "Real Americans", they'll tell you that. (Via Pharygula.)
Revenge of the Reality-Based Community — A formerly prominent Republican laments discovering reality about three decades too late. So here we are, post-election 2012. All the stupidity and closed-mindedness that right-wingers have displayed over the last 10 years has come back to haunt them. It is now widely understood that the nation may be center-left after all, not center-right as conservatives thought. Overwhelming losses by Republicans to all the nation's nonwhite voters have created a Democratic coalition that will govern the nation for the foreseeable future.
Mandate with Destiny — Obama's electoral mandate. In 2004, the editorial board of the Wall Street Journal, conservatism's Congregation for the Doctrine of the Faith, congratulated President Bush for "what by any measure is a decisive mandate for a second term" and exulted, "Mr. Bush has been given the kind of mandate that few politicians are ever fortunate enough to receive." This year, examining similar numbers with different labels, the Journal came up with a sterner interpretation. "President Obama won one of the narrower re-elections in modern times," its editorial announced. Kind of reminds me of Dick Morris predicting a 325-213 Romney "landslide" in the Electoral College, then proclaiming that Obama "eked out" a narrow 332-206 victory in the Electoral College. More of that vaunted conservative intellectual consistency all around. Or in shorter words: "The conservative stupid, it burns." (Snurched from Ta-Nehisi Coates.)
?otD: If I leave here tomorrow would you still remember me?
Writing time yesterday: 1.0 hours (revisions to "Rock of Ages")
Body movement: 0.5 hours (30 minutes on stationary bike)
Hours slept: 8.0 hours (solid)
Weight: 222.0
Number of FEMA troops on my block forcing people to accept healthcare and committing voter fraud: 0
Currently reading: The Color of Magic by Terry Pratchett
Posted on November 28, 2012 in Links
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text:
\section{Introduction}
\label{sect:intro}

It is within Giant Molecular Clouds (GMCs) that galaxies form the vast majority of their stars \citep{Myers:1986aa,Shu:1987aa,Scoville:1989aa,McKee:2007aa}. In local spiral and dwarf galaxies, star formation on galactic scales is known to be a slow process \citep[e.g.][]{Bigiel:2008aa}, with gas depletion time scales on the order of billions of years. This inefficiency is also found on scales of individual GMCs \citep{Krumholz:2007aa}, with a median star formation efficiencies per cloud free-fall time $\epsilon_{\rm ff} \sim1\%$ \citep[][]{Myers:1986aa,Murray:2011aa,Krumholz:2012aa}.
	While most observations of $\epsilon_{\rm ff}$ in GMCs find very similar median value, different surveys find spreads in $\epsilon_{\rm ff}$ of different sizes. Using the median absolute deviation to robustly estimate the standard deviations in $\epsilon_{\rm ff}$ the observational data presented in \cite{Evans:2014aa,Heyer:2016aa,Lee:2016aa,Vutisalchavakul:2016aa,Ochsendorf:2017aa} and \cite{Utomo:2018aa} yields values ranging from $\sim0.21$ to $\sim0.83\,{\rm dex}$.
	 In their recent review \cite{Krumholz:2018aa} compiled data from 13 papers over the last decade (see their Fig.~10) and discuss the impact of the method used to estimate $\epsilon_{\rm ff}$ on measurements. The exact size and distribution of the spread in $\epsilon_{\rm ff}$ is therefore still a debated topic, particularly as the origins of this diversity is not yet understood.

As the dispersion in $\epsilon_{\rm ff}$ is found independently of the method of observation, it is likely physical. Analytical models of star formation in supersonic turbulent flows \citep[e.g.][]{Padoan:2011aa,Hennebelle:2011aa,Federrath:2012aa} have successfully explained the low mean $\epsilon_{\rm ff}$ in GMCs and provided insight into how it can scale with global cloud properties, such as density and virial parameter. \cite{Krumholz:2005aa}, for example, postulate that GMCs are turbulent gas structures which are characterised by a log-normal distribution (determined by the Mach number) and only regions within the cloud with density above some threshold are able to form stars. In their model the threshold density is determined by both the virial parameter of the cloud and its Mach number. \cite{Lee:2016aa} \citep[henceforth L16, see also][]{Ochsendorf:2017aa} have been critical of such models for failing to predict a sufficiently large dispersion in $\epsilon_{\rm ff}$. Furthermore, observations indicate a decreasing $\epsilon_{\rm ff}$ with increasing cloud mass ($M_{\rm GMC}$). \cite{Ochsendorf:2017aa} argued that this is a result of massive clouds having a diffuse, non-star forming outer envelopes. This observed $M_{\rm GMC}$-$\epsilon_{\rm ff}$ relationship presents an additional constraint on any model attempting to explain the distribution in $\epsilon_{\rm ff}$. However it is worth noting that the $M_{\rm tot}$-$\epsilon_{\rm ff}$ relation could be, at least in part, the result of limitations in current observations to detect low mass clouds.

\cite{Feldmann:2011aa} developed a toy model where the growth of stellar mass in a GMC is determined only by the mass and free-fall time of available gas and an intrinsic efficiency per free-fall time ($\epsilon_{\rm ff,0}$), meanwhile the change in gas mass results from a combination of mass converted into stars, gas removed by feedback and accretion of new gas from the surrounding environment. Using this model they found that a $\sim 2$ dex spread in the measured $\epsilon_{\rm ff}$ can be explained by cloud evolution by adopting a \emph{constant} input $\epsilon_{\rm ff}$. L16 explored the same effect but found that a fixed input $\epsilon_{\rm ff}$ fails to reproduce their observed distribution  of $\epsilon_{\rm ff}$, with too few clouds predicted at high ($\gtrsim 10\%$) and low ($\lesssim 0.01\%$) efficiencies at any given time. By assuming that all GMCs evolve in a similar fashion, but observed at a different stage of evolution and by allowing for a time dependent $\epsilon_{\rm ff}$, this  problem was mitigated (see their figure 8). While each GMC observed is at a different stage of its life, it is currently unclear if \emph{all} clouds follow the same evolution, and how (or if) the galactic environment plays a role.

A number of authors have studied the dispersion in $\epsilon_{\rm ff}$ using simulations of individual GMCs as well as global disc simulations: the former looking for an explanation in the internal properties of clouds, while the latter allows for the impact of environment to be studied.  \cite{Semenov:2016aa} studied the impact of an explicit treatment of small scale gas turbulence, using simulations of entire Milky Way-like galaxies, on the parsec-scale star formation efficiency. From the local properties of the gas, they used the simple parametrisation of \cite{Padoan:2012aa}, calibrated on magneto-hydrodynamical simulations of star formation in supersonic turbulence, to compute $\epsilon_{\rm ff}$. They found that their simulations produced values of $0.01\%\lesssim\epsilon_{\rm ff}\lesssim10\%$. While this is an encouraging result, we will in this work demonstrate that the mapping between the $\epsilon_{\rm ff}$ computed from local properties and what is actually derived from observations is complex and depends on the star formation history of the cloud, not just its instantaneous properties.

Recently \cite{Grudic:2018aa} carried out 17 magnetohydrodynamic simulations of isolated GMCs of varying mass, radii and feedback models but identical surface density. They found that the spread in $\epsilon_{\rm ff}$ seen in observations is similar to the spread in $\epsilon_{\rm ff}$ measured throughout a cloud's lifetime. However, as pointed out by the authors, their GMCs have ``fairly artificial'' initial conditions and lack the effects of the larger galactic environment in which GMCs are found. As shown by observations \citep[e.g.][]{Rosolowsky:2003aa,Heyer:2009aa}, GMCs have a large range of properties that can not be captured by 17 overlapping models. Therefore full galactic (disc) simulations, which produce self-consistent GMCs with a range of properties, are needed to better model and investigate the entire evolution of such clouds.

In this study we will go beyond previous work by using parsec resolution simulations of entire disc galaxies and investigate the emerging GMC star formation efficiencies and how they evolve. This allows us to explore whether such a diversity in $\epsilon_{\rm ff}$ and the observed mass--$\epsilon_{\rm ff}$ relation is an artefact of observational methods or a physical result. The large number of GMCs found in our simulations allows us to look for correlations between different properties of a GMC and its $\epsilon_{\rm ff}$, thus determining if a single property is responsible for the observed scatter in $\epsilon_{\rm ff}$. Furthermore, taking advantage of the high temporal resolution of our simulations, we explore how GMCs evolve over their lifetime in a number of different properties and how this contributes to the diversity in $\epsilon_{\rm ff}$. Finally, combining our simulated data with analytical models we determine whether a single model is able to explain our simulations or observations.

This paper is organised as the following: in \S\ref{sect:meth} we summarise our simulations and methods for identifying and tracking GMCs, in \S\ref{sect:results} we present the measured values of $\epsilon_{\rm ff}$ and how it relates to global GMC properties, in \S\ref{sect:disc} we explore the source of the dispersion in $\epsilon_{\rm ff}$ and finally we present our conclusions in \S\ref{sect:con}.

\section{Method}
\label{sect:meth}

\subsection{Simulations}
\label{sect:meth:sims}
We make use of the two Milky Way-like galactic disc simulations in \cite{Grisdale:2016aa}, henceforth G17. The simulations are identical, apart from one being run with stellar feedback (our fiducial simulation) and one without. The simulations account for a dark matter halo, stellar and gaseous disc and a bulge. The initial conditions of both simulations are identical to the AGORA disc initial conditions described in \cite{Kim:2016aa}. They were run using the hydro+$N$-body, Adaptive Mesh Refinement (AMR) code {{\small RAMSES} } \citep{Teyssier:2002aa}. A cell is refined if it reaches a threshold mass of $9300\Msol$ and the minimum allowed cell size is $\Delta x\sim 4.6 \pc$.

The adopted cooling, feedback and star formation models are outlined in G17 and \cite{Grisdale:2018aa}, hence forth G18 \citep[see also][]{Agertz:2013aa,Agertz:2015aa}. Briefly, the feedback model accounts for the injection of momentum, energy, mass loss and enrichment from stellar winds, supernovae (II and Ia) and radiation pressure from young stars. Star formation occurs on a cell-by-cell basis according to the star formation law:
\begin{equation}
	\dot{\rho}_{\star}= \epsilon_{\rm ff,SF} f_{\rm H_2}\frac{ \rho_{\rm g}}{t_{\rm ff}},
	\label{eq:schmidtH2}
\end{equation}
where $f_{\rm H_2}$ is the local mass fraction of molecular hydrogen (H$_2$), $\rho_{\rm g}$ is the gas density, $t_{\rm ff}=\sqrt{3\pi/32G\rho_{\rm g}}$ is the local free-fall time and $\epsilon_{\rm ff,SF}$ is the local star formation efficiency per free-fall time of gas in the cell. For all star forming cells $\epsilon_{\rm ff,SF}$ is set to $10\%$ in the simulation with feedback and $1\%$ in the simulation without. As shown in G17, these choices lead to comparable galactic star formation histories in the two simulations. All star particles form with an initial mass of $300\Msol$.

G17 demonstrated that the simulation with stellar feedback give rise to a supersonically turbulent ISM, with a density and velocity structure in close agreement with local spiral galaxies. Furthermore, the resulting GMC population has masses, sizes, velocity dispersions and scaling relations (`Larson's relations') closely matching that of the Milky Way \citep[e.g.][]{Heyer:2009aa}, as shown in G18. This makes our simulations a suitable platform for investigating the evolution and star formation properties of GMCs.

\subsection{Cloud identification and analysis}
\label{sect:meth:id}

We identify GMCs in two separate ways: 1) in projection (2D) using the CLUMPFIND algorithm \citep{Williams:1994aa} as implemented in the clump finding identification and analysis package {\small CUPID}\footnote{part of the Starlink Project \citep[see][for details]{Starlink2,Starlink}} and 2) in 3D using the on-the-fly clump finding module PHEW \citep[Parallel HiErarchical Watershed,][]{Bleuler:2014aa} built into {\small RAMSES}. The methods yield similar distributions of GMC properties, albeit with the 3D method giving slightly larger masses and sizes. The adopted cloud finding parameters and the resulting GMC properties are discussed in detail in G18. We adopt both methods when comparing simulated GMC star formation efficiencies in \S\ref{res:dist} but restrict all other analysis in this work to the 3D approach. This allows for higher time resolution, hence allowing for cloud tracking as well as better statistics (due to the number of clouds identified).
\begin{figure*}
	\begin{center}
		\includegraphics[width=0.98\textwidth]{2D_eff_KMT.pdf}
		\caption[]{Left: $\epsilon_{\rm ff}$ as function of total mass ($M_{\rm tot}$) for clouds identified using the 2D clump finder. GMCs from the simulation with feedback are shown by the red 2D-histogram, while those from the simulation without are given by the blue contours ($\geq1,\,\geq5$ GMCs). The sampling limit of the 2D clump finder is given by the dashed black line (assuming $t_{\rm ff}/t_{\rm\star,y}=1$) see text for details.
		 Middle and right: Histograms showing the fraction of GMCs ($f_{\rm GMC}$) with a given $\epsilon_{\rm ff}$ for clouds identified using the 2D and 3D clump finders respectively. GMCs from the simulation with feedback are shown in red, while those from the simulation without are shown in blue.
		 The dashed lines show the median efficiency ($\langle\epsilon_{\rm ff}\rangle_{\rm med}$) for each data set. The values of the $\langle\epsilon_{\rm ff}\rangle_{\rm med}$ and the standard deviation in $\log{\epsilon}$ ($\sigma_{\log\epsilon}$) are given. For comparison with observations, data from Table 3 of L16 is included in the left and middle panels (black points and black histogram respectively) and $\langle\epsilon_{\rm ff}\rangle_{\rm med,\,Lee16}$ is shown (dashed black line).
		}
		\label{fig:2deff}
	\end{center}
\end{figure*}
Throughout this work, data from the 2D clump finder were obtained from simulation snapshots between $t=150-450\,{\rm Myr}$, separated by  $\Delta t=25\,{\rm Myr}$. For the 3D clump finder, data was obtained at $t=325-380\,{\rm Myr}$, with a temporal spacing of, on average, $\Delta t\sim 25,000$ years (see G17 for more details). Clouds that lie within the central kiloparsec of the galaxy are removed from the analysis. In total, during the period of analysis we identify 8,201 (3,434) GMCs using 2D clump finding in the simulation with(out) feedback and 655,499 (212,056) clouds with the 3D clump finder.

To accurately compare simulations to observations (e.g. L16), we use the estimator
\begin{equation}
	\epsilon_{\rm ff}=\frac{t_{\rm ff}}{t_{\rm\star,y}}\frac{M_{\rm\star,y}}{(M_{\rm GMC} + M_{\rm\star,y})}
	\label{eq:sfeff}
\end{equation}
for each GMC, where $M_{\rm\star,y}$ is the mass of stars with the GMC which has an age less than $t_{\rm\star,y}$,  $M_{\rm GMC}$ is the (molecular) gas mass of the GMC, $t_{\rm ff}=\sqrt{3\pi/32G\rho_{\rm GMC}}$ is the mean free fall time across the GMC and $\rho_{\rm GMC}$ is $M_{\rm GMC}$ divided by the GMC's volume (i.e. its mean density). Stellar masses are calculated by considering only stars that overlap with gas belonging to a GMC\footnote{Other methods for matching stars to GMCs were explored and found to have little impact on the results presented in this work.}, either in projection (2D method) or in 3D.

To allow for a comparison to the stellar clusters detected by free--free emission (e.g. L16), we adopt $t_{\rm\star,y}=4\,{\rm Myr}$. We emphasise that $\epsilon_{\rm ff}$ should not be confused with $\epsilon_{\rm ff,SF}$ in Eq.~\ref{eq:schmidtH2}. The former is the efficiency per free fall time averaged over the whole GMC ($\sim10-70\pc$), while the latter is the efficiency at which an \emph{individual computational cell} converts gas into stars ($\sim4.6\pc$).

\subsection{Cloud tracking}
\label{sect:meth:track}
All GMC quantities are followed over time by employing the cloud tracking algorithm described in  \cite{Tasker:2009aa}. Briefly, for clouds found using the 3D\footnote{We only apply the tracking routine to clouds identified with the 3D clump finding method, as the time resolution in the 2D method is insufficient for accurate tracking.}, we use the position ($\mathbf{x}$) and velocities ($\mathbf{v}$), of each GMC at a time $t_{0}$, we adopt a linear approximation, where the change in position vector over one tracking step is $\Delta \mathbf{x}=\mathbf{v}\Delta t$, to predict where the cloud should be at the next cloud finder output, $t_{1}=t_0+\Delta t$. Next, separations ($\mathcal{S}$) between a cloud's predicted position at $t=t_{1}$ and the positions of all clouds found at this time are calculated. Cloud identities are matched for clouds with the smallest $\mathcal{S}$ and satisfying either $\mathcal{S} \leq2R_{{\rm GMC},t_{0}}$ or $\mathcal{S} \leq\langle R_{{\rm GMC},t_{1}}\rangle$, where $R_{{\rm GMC},t_{0}}$ is the radius of the cloud at $t_{0}$ and  $\langle R_{{\rm GMC},t_{1}}\rangle$ is the mean cloud radius at $t_{1}$. In the case of multiple clouds from $t_{0}$ being linked to the same cloud at $t_{1}$, the cloud at $t_{1}$ inherits the identity of the most massive cloud from $t_{0}$, while the other progenitor cloud(s) are considered to have merged and are not tracked further.

To ensure that only clouds with a complete life-cycle are considered we exclude those formed in (or before) the first snapshot of our analysis, or those destroyed after the last.

Furthermore, it is important to note that because we are detecting clouds based on a fixed density threshold ($100{\,\rm {cm^{-3}}}$, see G18) it is possible for a cloud to drop below the detection limit but remain a coherent structure and then, at later times, pass back above the threshold. In such situations, our methods would register the cloud as having been destroyed and a new cloud forming. A ``new'' cloud of this type may be detected with a significant young stellar mass ($M_{\rm\star,y}$), hence giving the appearance of beginning its life with a high $\epsilon_{\rm ff}$. To mitigate this, we only considered GMCs with an initial $M_{\rm\star,y}\leq1500\Msol$, equivalent to five (or less) star particles. An alternative method, which would better reflect the complex cycle of gas ending up in GMCs, would be to employ tracer particles to track the gas of each GMC \citep[e.g.][]{Semenov:2016aa}, or to identify stellar clusters and their associated molecular gas. We leave this for a future investigation.

Finally, clouds with lifetimes shorter than a million years are also discarded. After the tracking is complete, and the above criteria are applied, 1879 unique GMCs evolutionary tracks remain, which we focus on in \S\ref{res:evol}.

\section{Results}
\label{sect:results}
\subsection{Distribution of star formation efficiencies}
\label{res:dist}

\subsubsection{The GMC Mass - Star Formation Efficiency Relation}
\label{res:masssffe}
We begin our analysis by calculating $\epsilon_{\rm ff,2D}$ (Eq.~\ref{eq:sfeff}) for the clouds identified in projection, as outlined in \S\ref{sect:meth:track}. In the left hand panel of Fig.~\ref{fig:2deff} we show how $\epsilon_{\rm ff,2D}$ varies with total cloud mass ($M_{\rm tot}=M_{\rm GMC}+M_{\rm\star,y}$, as defined in L16) in our fiducial simulation (i.e. including feedback) and compare these to the observational data in L16. The simulated GMC population has a wide range of $\epsilon_{\rm ff,2D}$ values which agree well with observations, see Fig.\ref{fig:2deff}.

Both simulated and observed $\epsilon_{\rm ff,2D}$ decrease with increasing $M_{\rm tot}$ \citep[see also][]{Ochsendorf:2017aa}, raising the question as to whether this is due to a physical process, or a result of the cloud identification method. In our simulation $M_{\rm\star,y}=N\cdot M_{\star}$, where $M_{\star}=300\Msol$\footnote{Particles in the feedback simulation lose mass, however during the first $4\,{\rm Myr}$ of their evolution they only lose a maximum of $10\%$ of their initial mass.}
 is the mass resolution of the star particles and $N$ is the number of star particles in the GMC. This defines a lower limit of the estimated $\epsilon_{\rm ff,2D}$ in our simulation,
\begin{equation}
	\epsilon_{\rm ff,lim}  = \frac{t_{\rm ff}}{t_{\rm\star,y}}\frac{1}{(1+ M_{\rm GMC}/300\Msol)},
	\label{eq:quanta}
\end{equation}

below which our simulation cannot sample star formation. This `sampling limit' is shown in Fig.~\ref{fig:2deff} for $t_{\rm ff}=t_{\rm\star,y}$, illustrating how this introduces a bias in how $\epsilon_{\rm ff,2D}$ relates to $M_{\rm tot}$. Star clusters identified via free--free emission, as done by L16, have a similar bias; such identification requires the presence of UV-emitting massive stars to ionise the surrounding ISM, hence setting a lower limit to the detectable star formation efficiency for all cloud masses, which scales in a similar fashion as Eq.~\ref{eq:quanta} \citep[see also][]{Murray:2011aa,Grudic:2018aa}. This is likely a contributing factor as to why the simulation and observations agree on the low $\epsilon_{\rm ff,2D}$ end of the distribution. Indeed, with the exception of a single GMC,\footnote{The exception, found with $\epsilon_{\rm ff}=1.8\times10^{-4}$ and $M_{\rm tot}=2.8\times10^{4}\Msol$, only has  $16\Msol$ in stellar mass. }  the observed GMCs shown in Fig.~\ref{fig:2deff} (from L16) have $M_{\rm\star,y}\gtrsim100\Msol$, close to the mass resolution of the simulation.

\subsubsection{Dispersion of Star Formation Efficiencies}
\label{res:disp}
To quantify the spread of $\epsilon_{\rm ff,2D}$ we show the normalised distribution, the median value of $\epsilon_{\rm ff,2D}$ ($\langle\epsilon_{\rm ff,2D}\rangle_{\rm med}$) and the standard deviation of $\log{\epsilon_{\rm ff,2D}}$, $\sigma_{\log{\effp}}$\footnote {The standard deviation ($\sigma_{\log{\effp}}$) is robustly estimated via the median absolute deviation (MAD): $\sigma_{\log{\effp}}= 1/0.6745$ MAD \citep{Muller:2000aa,Romeo:2016aa}.}, for both the simulation and L16's observations in the middle panel of Fig.~\ref{fig:2deff}.
\begin{figure}
	\begin{center}
		\includegraphics[width=0.4\textwidth]{2D_mass_v_eff_med.pdf}
		\caption[]{$\langle\epsilon_{\rm ff}\rangle_{\rm med}$ as a functions of $M_{\rm tot}$. Shown are measurements for GMCs identified using the 2D clumpfinder in the simulation with feedback, without feedback and from L16, shown using red, blue and black points respectively. The error bars show $\sigma_{\log{\eff}}$ for each mass bin. The solid red, blue and black lines show a $\chi^{2}$ least squares fit to their corresponding data set. We note the fits do not account of the sampling limit shown by the dashed-black line (see text).
		}
		\label{fig:effmass}
	\end{center}
\end{figure}
\begin{figure*}
	\begin{center}
		\includegraphics[width=0.90\textwidth]{3D_eff_v_tff_prop_all.pdf}
		\caption[]{2D  Histograms showing how the distribution of $\epsilon_{\rm ff}$ is correlated with $M_{\rm GMC} $, $R_{\rm gal,2D}$, $\sigma_{\rm v,GMC}$, $R_{\rm GMC}$, $\rho_{\rm GMC}$ and $\alpha_{\rm vir,GMC}$ of the GMCs identified with the 3D clump finder in the simulation with feedback. All histograms use the same colour scale (shown on the right), which is normalised to the total number of GMCs included in the data. Additionally, each panel shows the evolution tracks of eight randomly selected GMCs.
		}
		\label{fig:effprop}
	\end{center}
\end{figure*}
The shape of the $\epsilon_{\rm ff,2D}$ distribution in our simulation is in excellent agreement with observations. We find a median star formation efficiency per free-fall time of $\sim 2~\%$, also matching observations. The simulated $\sigma_{\log{\effp}}$ is not as large a value as L16's data ($0.72\,{\rm dex}$ compared to $0.83\,{\rm dex}$) but is within the range of values reported in the literature (see \S\ref{sect:intro}). This is likely due to the simulated population of GMCs being better sampled at low masses compared to observation (see also G18), where the spread in $\epsilon_{\rm ff,2D}$ is smaller, as well the observed population having a couple of extremely inefficiently star forming clouds ($\epsilon_{\rm ff}\sim10^{-4}$). From Fig.~\ref{fig:effmass}, which shows how $\langle\epsilon_{\rm ff,2D}\rangle_{\rm med}$ and $\sigma_{\log{\effp}}$ vary with $M_{\rm tot}$, we find that $\langle\epsilon_{\rm ff,2D}\rangle_{\rm med}$ in the simulation is compatible with observations at almost all cloud masses: low mass clouds ($\sim$ few $\times 10^4 \Msol$) reaching almost $\epsilon_{\rm ff}\sim 10\%$, whereas clouds in excess of $10^6\Msol$ have $\epsilon_{\rm ff}\lesssim 1\%$, on average. This result can be summarised as $\epsilon_{\rm ff}\propto M_{\rm tot}^{\beta}$ with $\beta\sim-0.34$ and $-0.36$ for our simulation and L16's observations respectively. Finally, a qualitative agreement is found for $\sigma_{\log{\effp}}-M_{\rm tot}$, with an increasing scatter with increasing cloud mass.

It is important to note that Fig.~\ref{fig:effmass} and the values of $\beta$ given above do not account for the sampling limit (see \S\ref{res:masssffe}). As shown by the L16's data in Fig.~\ref{fig:2deff}, clouds below this sampling limit do exist and therefore need to be accounted for when calculating true value of $\beta$. For example, if we ignore clouds in our feedback simulation that sit on or close to the sampling limit we find $\beta$ decreases, i.e. the relationship become steeper. The steeping in due to the fact that sampling limit preferentially impacts lower mass ($<10^{5}\Msol$ ) clouds, indeed $\langle\epsilon_{\rm ff,2D}\rangle_{\rm med}$ at these masses is only $\sim 1\sigma_{\log{\effp}}$ from the sampling limit. Given that $\beta$ will be biased in both simulation and observation, determining the impact of the sampling limit is of significant importance. One way to assess the how the sampling limit biases measurements of $\beta$ would be to compare data with a simple Bayesian model. Such a comparison is beyond the scope of this study and we leave it for future work.

We calculate the efficiency per free-fall time ($\epsilon_{\rm ff}$) for GMCs identified using the 3D clump finder and repeat the above analysis, see right most panel of Fig.~\ref{fig:2deff}. We find similar median efficiencies as before ($\langle\epsilon_{\rm ff}\rangle_{\rm med}\sim 1\%$), but the spread in $\epsilon_{\rm ff}$, which still covers several orders of magnitude, is smaller: $\sigma_{\log{\eff}}=0.37$.

In summary, the \emph{measured} star formation efficiency per free-fall time in a cloud tells us little, if anything at all, about the \emph{input} efficiency on smaller scales, i.e. $\langle\epsilon_{\rm ff}\rangle_{\rm med}\ne\epsilon_{\rm ff,SF}$. The former depends not only on the turbulent density substructure (as discussed below) and global properties of the cloud but also on its evolution and therefore  the rate of star formation, that results in the young stellar population observed at any instance.

\subsubsection{Role of Feedback on $\epsilon_{\rm ff}$}
Fig.~\ref{fig:2deff} and \ref{fig:effmass} include analysis from the simulation without feedback. We find very similar trends as before, i.e. $\epsilon_{\rm ff,2D}\propto M_{\rm tot}^{\beta}$ with $\beta\sim-0.69$ and a smaller scatter in 3D compared to 2D (i.e. $\sigma_{\log{\effp}}=1.1\rightarrow\sigma_{\log{\eff}}=0.2$). As with simulation with feedback we emphasise that the value of $\beta$ given above does not account for the sampling limit and therefore may not accurately represent the correlation between $\epsilon_{\rm ff,2D}$ and $M_{\rm tot}$. Given that the simulation without feedback has a propensity to produce unphysical (see G18), massive ($M_{\rm GMC}>10^{7}\Msol$), low $\epsilon_{\rm ff,2D}(\lesssim10^{-5})$ clouds, we expect that clouds will be below the sampling limit at all masses, but particularly at high mass (see left panel of Fig.~\ref{fig:2deff}) which makes predicting the impact of the sampling limit on $\beta$ much more difficult in this case.

The primary difference between the GMCs from the simulation with feedback and the simulation without is the latter has a population of long lived massive ($\gtrsim 10^7\Msol$) and very inefficiently star forming  ($\epsilon_{\rm ff}\lesssim10^{-3}$) clouds. Interestingly, we find that  in 3D the simulation with feedback yields GMCs able to reach higher $\epsilon_{\rm ff}$ values than clouds in the simulation without, which is a result of stellar feedback removing gas from the clouds. From these results we conclude that, while stellar feedback plays a role in determining the shape of the distribution of measured $\epsilon_{\rm ff,2D}$ and $\epsilon_{\rm ff}$, it is not the source of the dispersion in either.

Given the reasonable match between GMCs in the simulation without feedback and the observations, it might seem just as reasonable to use this simulation as the simulation with feedback in further analysis. However, as shown in both G17 and G18, this simulation fails to produce a realistic neutral ISM and distribution of GMC properties, therefore we focus all further analysis on the simulation with feedback.

Having established that star formation efficiencies in the simulated GMC population closely match observations, we next aim to quantify why this is the case.
\begin{figure}
	\begin{center}
		\includegraphics[width=0.4\textwidth]{3D_eff_v_tff_prop_noFB_single.pdf}
		\caption[]{2D  Histograms showing how the distribution of $\epsilon_{\rm ff}$ is correlated with $\rho_{\rm GMC}$ for the GMCs identified with the 3D clump finder in the simulation without feedback.
		}
		\label{fig:effpropnofb}
	\end{center}
\end{figure}
\subsubsection{Role of Cloud Properties on $\epsilon_{\rm ff}$}
\label{res:evp}
From this point forward we focus our analysis to clouds identified using the 3D clump finder. Fig.~\ref{fig:effprop} shows the relation between $\epsilon_{\rm ff}$ of GMCs and gas mass ($M_{\rm GMC}$), galactocentric radius ($R_{\rm gal, 2D}$), velocity dispersion ($\sigma_{\rm v,GMC}$), size ($R_{\rm GMC}$), gas density ($\rho_{\rm GMC}$) and virial parameter ($\alpha_{\rm vir,GMC}$) in the simulation with feedback (we refer the reader to G18 for discussion on how these quantities are calculated).
\begin{figure*}
	\begin{center}
		\includegraphics[width=1.0\textwidth]{eff_evolution_indi_hm_1d0_55d0.pdf}
		\caption[]{Distribution of $M_{\rm GMC},\,M_{\rm\star,y},$ and $\epsilon_{\rm ff}$ as function of GMC age ($t_{\rm age}$). We normalise the y-axes by the maximum recored gas mass of GMC during its lifetime ($M_{\rm GMC,max}$). The three red lines show the predicted evolution given by the L16 model (see \S\ref{disc:con}) for $\epsilon_{\rm ff,\,L16}\propto t_{\rm age}^{\delta}$ where $\delta=0,\,1$ or $2$, while the blue lines show evolution of clouds with different lifetimes ($10,\,15$ and $20\,{\rm Myr}$) and $\delta=2$. Also shown are the evolution tracks for the same eight GMCs shown in Fig.~\ref{fig:effprop}.
				}
		\label{fig:eff2Dhisttracks}
	\end{center}
\end{figure*}
No strong correlation is found between any cloud property and $\epsilon_{\rm ff}$; clouds with similar physical parameters show a great diversity of $\epsilon_{\rm ff}$. Not surprisingly, there is a trend for dense GMCs to have higher $\epsilon_{\rm ff}$ and likewise, as $t_{\rm ff}\propto \rho^{-0.5}$, a trend for clouds with short free-fall times to be efficient at forming stars. We find a signature of clustering in the different $\epsilon_{\rm ff}$-spaces, e.g. in the $\epsilon_{\rm ff}-M_{\rm GMC}$ space, clouds cluster around $[\epsilon_{\rm ff},M_{\rm GMC}]\sim [-1.25,10^{6}\Msol]$ and $\sim [-2.3,10^{5.4}\Msol]$, suggesting there are some preferential values that clouds are drawn to.

Given that $\epsilon_{\rm ff}$ in the former of these two regions is centred on $\epsilon_{\rm ff}\sim5\%$ and therefore within a factor of 2 from $\epsilon_{\rm ff,SF}$, this region could be an artefact of the star formation model employed in the simulations. To test this we reproduce Fig.~\ref{fig:effprop} using clouds identified in the simulation without feedback (Fig.~\ref{fig:effpropnofb} shows the $\epsilon_{\rm ff}$-$\rho_{\rm GMC}$ panel). While the vast majority ($\sim70\%$) of clouds are highly inefficient at forming stars at higher density, i.e. $\rho_{\rm GMC}\gtrsim10\Msol\pc^{-3}$, there is a tendency  for $\epsilon_{\rm ff}\rightarrow\epsilon_{\rm ff,SF}$. By directly comparing the $\epsilon_{\rm ff}$-$\rho_{\rm GMC}$ panel in Fig.~\ref{fig:effprop} to Fig.~\ref{fig:effpropnofb} we find that general shape of the two distributions is very similar, but that latter is missing most of the $\epsilon_{\rm ff}>10^{-1.5}$ clouds. We therefore conclude that the high $\rho_{\rm GMC}$-high $\epsilon_{\rm ff}$ region is at least partially a result of the star formation prescription employed in our simulations. In future work we explore in detail, to what the degree the star formation prescription drives clouds into this region and if other factors play a role.

Next we explore how cloud properties evolve and the role this plays in establishing the wide range of observed star formation efficiencies.

\subsection{Cloud evolution}
\label{res:evol}
\subsubsection{Individual clouds}

Fig.~\ref{fig:effprop} includes evolutionary tracks of eight randomly selected GMCs, with their positions shown every $0.25\,{\rm Myr}$. Each cloud has a unique path through the seven parameter spaces explored. For example, most clouds tend to become more gravitationally bound over their lifetime ($\alpha_{\rm vir,GMC}$ decreases) while the crimson cloud becomes less bound ($\alpha_{\rm vir,GMC}$ increases).

GMCs are not confined to a single area of parameter space but can move from one region to another and in a variety of ways. Comparing the black and grey clouds with either of the blue clouds shows that some clouds explore only a small fraction of a given parameter space while others might explore a large portion. The one possible exception to this is a cloud's progression in $\epsilon_{\rm ff}$--$R_{\rm gal,2D}$ space, where we see that clouds are ``born'' and ``die'' at approximately the same galactic radius ($R_{\rm gal,2D}$), with very little variations over the clouds lifetime. This is due to the short cloud evolution timescale ($\sim10\,{\rm Myr}$) compared to the galactic dynamical timescale ($\sim100-200\,{\rm yr}$). Cloud evolution in the $\epsilon_{\rm ff}$--$\sigma_{\rm v,GMC}$ space is similar to $\epsilon_{\rm ff}$--$R_{\rm gal,2D}$, i.e. evolution in $\epsilon_{\rm ff}$ occurs while $\sigma_{\rm v,GMC}$ remains largely unchanged.

A visual inspection of the evolution of these eight clouds
(see additional material\footnote{Additional material can be found at \url{https://www.youtube.com/playlist?list=PLO1VCxfFwCjDefBvKCF7-CptcZLn-gGMT}})
reveals that the environment of a GMC is as important as its internal processes. For example, the red cloud is situated in a particularly dense spiral arm which feeds the cloud with gas, allowing $M_{\rm GMC}$ to increase by almost an order of magnitude during the first $0.5\,{\rm Myr}$ of its life.  In contrast the grey cloud forms in a much lower density environment, which is quickly disrupted by shear from galactic rotation. Furthermore only one of the eight clouds (dark blue) is clearly destroyed by supernovae, with the other seven instead appearing to be destroyed by shear, demonstrating that environment plays a role in the evolution of a cloud throughout its life.

\subsubsection{General Trends in Evolution}

To aid in teasing out general trends in cloud evolution, we create 2D histograms of $M_{\rm GMC},\, M_{\rm\star,y}$ and $\epsilon_{\rm ff}$ as functions of GMC age ($t_{\rm age}$) for \emph{all} 1,879 evolutionary tracks (see \S\ref{sect:meth:track}) in Fig.~\ref{fig:eff2Dhisttracks}. There is a wide range of different evolutionary paths taken by GMCs and that most clouds only live for $3$--$4\,{\rm Myr}$.  Furthermore, this demonstrates that the eight randomly selected GMCs overlaid in both Fig.~\ref{fig:effprop} and Fig.~\ref{fig:eff2Dhisttracks} are not the only GMCs with unique evolutionary paths.

The gas mass of a GMC when it is first detected \emph{tends} to be the maximum gas mass ($M_{\rm GMC,max}$) that the cloud reaches. However, some GMCs reach their $M_{\rm GMC,max}$ at $t_{\rm age}\ne0$ and have therefore gained mass through cloud-cloud collisions and accretion. Despite this, the general trend is for GMCs to lose mass as they evolve.

Interestingly, \cite{Kawamura:2009aa} infer from observations that $M_{\rm GMC}(t_{\rm age}=0)\ne M_{\rm GMC,max}$ and that over a period of $\sim30\,{\rm Myr}$ that $M_{\rm GMC}$ increases by a up to a factor of three, even after star formation begins (see their \S4.2 and Table 4). This discrepancy could be due to any number of factors such as: how we account for cloud-cloud interactions, clouds dropping below our detection threshold (see \S\ref{sect:meth:track}), differences in how we define and detect clouds or inaccuracies in the model used to infer evolution of GMCs from observations. We leave further exploration of this difference to future work.

An obvious assumption would be that the lost gas mass is converted into stars. This is at least partially true, as as the young stellar mass ($M_{\rm\star,y}$) tends to increase during the first few million years of a clouds life. At $t_{\rm age}\gtrsim4\,{\rm Myr}$, $M_{\rm\star,y}$ decreases with increasing age, yet clouds continue to lose significant fractions of their gas mass. Therefore,  the lost gas mass is removed from the GMC by other means (e.g. feedback and shear).

In general we find that $\epsilon_{\rm ff}$ tends to increase during the first $4$--$6\,{\rm Myr}$ of a GMC's life after this point we see that $\epsilon_{\rm ff}$ tends to either plateau or decrease. This corresponds to the age at which star particles will experience their first supernova event and therefore is a strong indicator of feedback limiting $\epsilon_{\rm ff}$.

To summarise, at any given $t_{\rm age}$ it is possible for clouds to have a large variety in $\epsilon_{\rm ff}$, $M_{\rm GMC}$ and $M_{\rm\star,y}$ and the exact value of given property at a given age is unique to each cloud.
\begin{figure*}
	\begin{center}
		\includegraphics[width=1.0\textwidth]{eff_evolution_indi_hm_cuts_1d0_55d0.pdf}
		\caption[]{2D histogram of $\epsilon_{\rm ff}$ as function of GMC age ($t_{\rm age}$) for GMCs with different ``birth'' masses ($M_{\rm birth}$). The three red lines show the predicted evolution of GMC as given by the L16 model (see \S\ref{disc:con}) for $\epsilon_{\rm ff,\,L16}\propto t_{\rm age}^{\delta}$ where $\delta=0,\,1$ or $2$,  while the blue lines show evolution of clouds with different lifetimes ($t_{\rm lt}=10,\,15$ and $20\,{\rm Myr}$) and $\delta=2$, as in Fig.~\ref{fig:eff2Dhisttracks} The purple solid and dashed line in each pane shows $\langle\epsilon_{\rm ff}\rangle_{\rm med}$ and $\langle\epsilon_{\rm ff}\rangle_{\rm med}\pm\sigma_{\log{\eff}}$ for each mass range (ignoring GMC age).
		}
		\label{fig:masscuts}
	\end{center}
\end{figure*}
\section{Discussion}
\label{sect:disc}

\subsection{Cloud Conformity or Diversity?}
\label{disc:con}

As shown by the observational data present in L16 and included in Fig.~\ref{fig:2deff} and discussed in \S\ref{sect:intro}, there is a significant dispersion in the values of $\epsilon_{\rm ff}$ for GMCs in the Milky Way. \cite{Feldmann:2011aa} put forward a model that is able to produce a wide spread in measured $\epsilon_{\rm ff}$ values ($1\lesssim\epsilon_{\rm ff}\lesssim100\%$, see their Fig.~2) by adopting a fixed universal efficiency per free-fall time $\epsilon_{\rm ff,0}$ (analogous to $\epsilon_{\rm ff,SF}$ used in our simulations) and allowing the GMC to evolve with time. L16 combined \cite{Feldmann:2011aa}'s model with the star formation prescription given in \cite{Krumholz:2005aa} to allow for a time-dependant $\epsilon_{\rm ff,0}$ which results in a pair of coupled ordinary differential equations: \\
\begin{equation}
	\frac{dM_{\rm GMC}}{dt}= -\epsilon_{\rm ff,0}\left(\frac{t_{\rm age}}{t_{\rm ff,\,fixed}}\right)^{\delta}\frac{M_{\rm GMC}(t_{\rm age})}{t_{\rm ff,\,fixed}} -\alpha M_{\rm\star,tot}(t_{\rm age})+ \gamma,
	\label{eq:lee1}
\end{equation}
and
\begin{equation}
	\frac{dM_{\rm\star,tot}}{dt}= \epsilon_{\rm ff,0} \left(\frac{t_{\rm age}}{t_{\rm ff,\,fixed}}\right)^{\delta}\frac{M_{\rm GMC}(t_{\rm age})}{t_{\rm ff,\,fixed}},
	\label{eq:lee2}
\end{equation}
where $\gamma$ is the rate of gas accretion on to the GMC and $\alpha$ is a parametrisation of the disruption of GMCs due to feedback. Having solved the above equations the young stellar mass
\begin{equation}
	M_{\rm\star,y}(t_{\rm age}) = M_{\rm\star,tot}(t_{\rm age}) - M_{\rm\star,tot}(t_{\rm age}-t_{\rm\star,y}),
	\label{eq:lee3}
\end{equation}
and the model  equivalent of $\epsilon_{\rm ff}$
\begin{equation}
	\epsilon_{\rm ff,\,L16}(t_{\rm age})= \frac{t_{\rm ff,\,fixed}}{t_{\rm\star,y}}\frac{M_{\rm\star,y}(t_{\rm age})}{M_{\rm GMC}(t_{\rm age})+M_{\rm\star,tot}(t_{\rm age})},
	\label{eq:lee4}
\end{equation}
can be calculated at each at every $t_{\rm age}$.
In this model, the evolution of $M_{\rm GMC}/M_{\rm GMC, max},\, M_{\rm\star,y}/M_{\rm GMC, max}$ and $\epsilon_{\rm ff,\,L16}$ is the same for  \emph{all} clouds, for a given choice of $\delta$, $\epsilon_{\rm ff,0}$ and fixed free-fall time ($t_{\rm ff,\,fixed}$).  In essence, this model requires conformity in the evolution of all GMCs and the dispersion in $\epsilon_{\rm ff}$ is produced by observing a population of GMCs, with each cloud at a different stage in its evolution.

We adopt the values of $\alpha,\,\gamma$ and $t_{\rm ff,\,fixed}$ given in L16 ($3.5,\,0$ and $6.7$  respectively) and reproduce their model for $\delta=0,1$ and $2$. L16 employed $\epsilon_{\rm ff,0}=0.014$ to ensure that all models produce clouds with lifetimes of $\sim20\,{\rm Myr}$ and argued that $\epsilon_{\rm ff,0}\proptot_{\rm age}^{\delta}$ with $\delta=2$ was required to match observations.  We adopt several different values for $\epsilon_{\rm ff,0}$. Firstly, we use $0.27,\,0.52$ and $0.47$ for the three values of $\delta$ respectively as these values ensure that all models convert $10\%$ of their gas mass to stars by the time the cloud is destroyed ($M_{\rm GMC}/M_{\rm GMC, max}\leq0.01$).  This ensures that all models have the same initial and final conditions (i.e. enforces conformity between models). Thus allowing us to determine if the diversity in our simulated GMCs can be explained by conformity to a single evolutionary path. The second set of values, $0.091,\,0.019$ and $0.006$, produce cloud lifetimes of $10,\,15$ and $20\,{\rm Myr}$ respectively for $\delta=2$ (i.e. assuming that GMCs will have different evolutionary paths). This set of models will allow for a determination on whether simply allowing for different GMC lifetimes is enough to explain the diversity in clouds efficiencies.

Testing this model against the evolutionary tracks of all GMCs in our simulation, i.e the red and blue lines in Fig.~\ref{fig:eff2Dhisttracks}, shows that due to the large spread in our data, the model (independent of $\delta$ and $\epsilon_{\rm ff,0}$) overlaps with our simulated GMCs in each parameter space. However, that to reproduce the diversity seen in the simulated GMCs would require a significant number of models, each with a different values for $\delta$ and $\epsilon_{\rm ff,0}$: therefore a model which produces diversity is required.

In their recent work, \cite{Grudic:2018aa} carried out isolated GMC simulations for three different mass clouds ($2\times10^{4,\,5,\,6}\Msol$). They found that by observing a population of clouds, all with the same mass, at random points during their lifetime they could reasonably reproduce L16's observed distribution in $\epsilon_{\rm ff}$. Furthermore, this was found independently of cloud mass (see their Fig.~4). Their simulations and conclusion support the models presented in L16: i.e. all clouds follow (nearly) identical evolutions and the spread in $\epsilon_{\rm ff}$ is a result of observing a population of different aged clouds.

Fig.~\ref{fig:masscuts} shows how $\epsilon_{\rm ff}$ evolves for clouds with gas mass at ``birth''\footnote{Defined as the first time a cloud is detected by the clump finder} ($M_{\rm birth}$).
The clouds with the largest $M_{\rm birth}$ tend to reach higher $\epsilon_{\rm ff}$ and have a (marginally) higher $\langle\epsilon_{\rm ff}\rangle_{\rm med}$. The most noteworthy result from Fig.~\ref{fig:masscuts} is that birth mass plays only a small role in determining the initial value of $\epsilon_{\rm ff}$ as shown by $\epsilon_{\rm ff}(t_{\rm age}\leq0.8\,{\rm Myr})$ having at least a 2 dex spread in each mass bin. Furthermore, while the dispersion in $\epsilon_{\rm ff}$ decrease as clouds age, it never reaches zero. This implies that knowing the birth mass of a GMC is not enough to predict the efficiency at which it converts gas into stars, and thus its star formation history.
Carrying out a similar experiment using $R_{\rm birth}$ (not shown), yields nearly identical results: clouds are born with a range of different $\epsilon_{\rm ff}$ independent of their initial size.

Unlike the model presented in L16 and the isolated GMCs simulations of \cite{Grudic:2018aa}, the GMCs in this work are simulated in a (realistic\footnote{As shown by the analysis of the simulation in G17 and G18.})
galactic environment which can heavily influence their evolution. In \S\ref{res:evol} we found that different GMCs were effected by their environment in different ways, i.e. some experience mergers and others are sheared apart, etc.  Indeed recent observations of NGC 2276 have found that galactic-scale tidal forces and ram pressure has lead to large variations in molecular content of the galaxy, resulting in some regions with variations in the depletion time scale (the ratio of the molecular gas mass to the star formation rate) as large as several orders of magnitude when measured on scales of $\sim450\pc$ \citep{Tomicic:2018aa}. It is therefore likely that measurements of $\epsilon_{\rm ff}$ on cloud scales in such a galaxy also find large variations. We therefore argue that the initial, intrinsic properties of GMCs ($M_{\rm birth}$ and $R_{\rm birth}$) are not sufficient to set the initial value and evolution of $\epsilon_{\rm ff}$: other factors such as the galactic environment (e.g shear) must be taken into account.  It is the combination of  a wide range of possible cloud properties and the environment in which GMCs live that naturally give rise to the observed and simulated spread in $\epsilon_{\rm ff}$.

Additionally, we note that Fig.~\ref{fig:eff2Dhisttracks} and \ref{fig:masscuts} show that in our simulation $\epsilon_{\rm ff}$ does not follow a smooth, systematic evolution, instead it is able to both increase \emph{and} decrease as clouds age. This is a direct contradiction of the prediction made by the L16 model, which predicts a continually increase in $\epsilon_{\rm ff,\,L16}$ with $t_{\rm age}$. The evolution seen in $\epsilon_{\rm ff}$ for our simulated clouds, as discussed above, result from combination of time dependant factors (e.g. mergers, gas accretion, feedback, shear and galactic tides) that are difficult to model as constant parameter in any given model and thus further evidence that the environment of clouds need to be accounted for when exploring their evolution.

Finally, we note that the analytical model we have adopted from L16 is not the only model. For example, the model present in \cite{Vazquez:2018aa}  predicts different star formation histories (and hence instantaneous $\epsilon_{\rm ff}$) for clouds of different mass. Their results and conclusions support  this work and our conclusion that it is the evolution history of a cloud needs to be know to able to determine its $\epsilon_{\rm ff}$ at any given age. Therefore any model (numerical or analytical) must be able to capture the full range of physical processes that occur within a GMC and its interactions with its environment.

\subsection{Limitations of the Simulations}
\label{dis:limits}

The instantaneous {\rm H}$_2$  fraction calculated at run-time to determine the star formation rate of a computational-cell (see \S\ref{sect:meth:sims}) is not stored or advected through the simulation. As a result we have to determine the molecular content within the simulation in post processing. For simplicity we chose to adapt a density threshold of $\rho_{\rm mol}=100{\,\rm {cm^{-3}}}$, with all gas above this value considered to be molecular. This limits the maximum value of $t_{\rm ff}$, which in turn acts as a limiting factor in determining $\epsilon_{\rm ff}$ from the simulations. If larger values of $t_{\rm ff}$ could be reached smaller values of $\epsilon_{\rm ff}$ maybe detected. A simple solution would be to rerun the simulations but including a treatment of the chemistry and thus allowing the molecular fraction of the gas to be self-consistently determined by the simulation, which could then be used to identify GMCs. However given that the current simulation is able to not only reproduce the median value of $\epsilon_{\rm ff}$ but also the size and distribution of the spread in values (and does so using a universal efficiency on the scale of computational cells, i.e. $\epsilon_{\rm ff,SF}$) we leave re-simulation for future work.

Isolated GMC simulations are able to completely resolve the internal structure of the GMC but at the cost of the galactic environment \citep[e.g. see][]{Padoan:2016aa,Grudic:2018aa}. The simulations used throughout this work have such an environment but they are limited in spatial resolution (i.e. $\Delta x\sim4.6\pc$, see \S\ref{sect:meth:sims}). This resolution results in GMCs being made up of several computational cells and thus stars form and inject feedback into specific regions with the clouds. This allows one generation of stars to alter the gas structure within a GMC and even remove gas, thus determining where the next generation of stars form and how a GMC evolves. Despite the limited resolution, our simulation is able to accurately reproduce the galaxy wide gas probability distribution function (PDF), the range and distribution of cloud properties, including $\epsilon_{\rm ff}$ (see G17 and G18). We therefore argue that accurately fully resolving the internal structure of GMCs is not as important as accurately reproducing the large ($\geq100\pc$) scale galactic environment.

\section{Conclusions}
\label{sect:con}
In this work we explore the efficiency of Giant Molecular Clouds (GMCs) at forming stars in hydrodynamical simulations of Milky Way-like galaxies. The primary goal of this work is to explain the observed spread in the star formation efficiency per free fall time ($\epsilon_{\rm ff}$). To this end we calculate $\epsilon_{\rm ff}$ for each GMC found within two simulations, one with stellar feedback and one without. Using a tracking algorithm we follow the evolution of $\epsilon_{\rm ff}$ (and other properties) of each cloud throughout its lifetime. Our key results are:
\begin{enumerate}
 	\item Galactic disc simulations where star formation is determined by a Schmidt star formation law (applied to molecular gas) are able to produce the observed spread in the measured values of $\epsilon_{\rm ff}$ for GMCs. A large spread in values is found independently of the presence of stellar feedback, however the inclusion of feedback in the simulation prevents highly inefficient ($\epsilon_{\rm ff}<10^{-4}$) massive ($M_{\rm tot}>10^{7}\Msol$) clouds from forming. Stellar feedback is not the main source of the dispersion in $\epsilon_{\rm ff}$.
	\item No single GMC property determines the $\epsilon_{\rm ff}$ of a cloud. Comparing seven key properties (gas mass, free-fall time, galactic radius, velocity dispersion, radius, density and virial parameter) of GMCs with $\epsilon_{\rm ff}$ shows no significant correlation. Instead we find that a cloud with a given value in any of the above properties is able to have a wide range of values in $\epsilon_{\rm ff}$.
	\item Each GMC evolves in a unique way, determined by both its initial properties and its environment. It is therefore not possible to describe the evolution of all clouds by a single analytical model neglecting environmental effects. Furthermore, the evolution of a particular property for a given cloud is not smooth or uniform: a cloud is able to explore a wide range of values during its lifetime. This leads to a natural spread in properties and in particular the value of $\epsilon_{\rm ff}$.
	\item The evolution of $\epsilon_{\rm ff}$ throughout a GMC's lifetime does not follow a systematic increase, contrary to predictions of simple analytical models. Instead the measured value of $\epsilon_{\rm ff}$ for a cloud is driven by a number of time dependant factors, including stellar feedback and galactic environment,  which can cause both increases or decreases as the clouds ages. This allows for a variety of different star formation histories.
\end{enumerate}

In future work we will explore the processes that drive cloud evolution and how this leads to diversity in cloud properties.

\section*{acknowledgments}

We thank the anonymous referee for their valuable and insightful comments.
KG acknowledge support from the Science and Technology Facilities Council (grant ST/N002717/1), as part of the UK E-ELT Programme at the University of Oxford.
KG also thanks and acknowledges support from New College, University of Oxford via the Balzan Fellowship.
OA acknowledges support from the Swedish Research Council (grant 2014- 5791).
OA and FR acknowledge support from the Knut and Alice Wallenberg Foundation.
The research of JD and AS is supported by Adrian Beecroft and the STFC.
This work used the DiRAC Complexity system, operated by the University of Leicester IT Services, which forms part of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment is funded by BIS National E-Infrastructure capital grant ST/K000373/1 and  STFC DiRAC Operations grant ST/K0003259/1. DiRAC is part of the National E-Infrastructure.

\bibliographystyle{mn3e}

redpajama_set_name: RedPajamaArXiv


There are so many individuals that have excelled in the personal development field over the years. Napoleon Hill comes to mind along with Dale Carnegie as the individuals that opened the door for all the others.
Have you read Napoleon Hills's conversational masterpiece Think and Grow Rich yet? It seems to be the starting ground for many entrepreneurs and is one worthy of your attention.
Periodically I like to feature the thoughts and insights from some of the extraordinary thinkers of the ages.
Mr. Jim Rohn was one such man. He ability to take his insights of the necessary characteristics of the successful entrepreneur and explain it with an uncanny philosophical ideal mixed with humor that captivated his audiences around the world was truly remarkable.
hosted by Jim Rohn. I have done so because they have been most inspirational to me. This is the case, not only to me but to so many others as they entered their entrepreneurial world.
Jim Rohn was born in Yakima, Washington on September 17, 1930, and spent his youth working on his family's farm in Caldwell, Idaho. Sadly falling victim to pulmonary fibrosis, he passed away shy of his 80th birthday, December 5, 2009.
Jim Rohn was a pioneer when it came to affiliate MLM marketing, entering this relatively unexplored business world of direct marketing in 1955 with AbundaVita quickly rising to the top. Using his experiences and subsequent successes he chose in 1957 to leave AbundaVita and follow John Shoaff, a former partner in AbundaVita who had formed his own company Nutri-Bio. Not only did Jim succeed beyond his wildest dreams with Nutri-Bio but John Shoaff became his mentor and friend.
As he mentions in many of his lectures he was able to hone the skills required to be very successful businessman coining the phrase, "I went from working full time on my income and part time on my fortune to working fulltime on my fortune".
Now one only has to recognize that Jim Rohn was a man of vision. I say this because the following scenario could have gone two ways.
Although I have been unable to confirm the exact day the Nutri-Bio company closed its doors I can confirm that it was some time in the early 1960's. I am connecting the dots this way believing it would be sometime before September 1965 as this was the date that the founder Jim Shoaff passed away.
Jim Rohn was now faced with being associated with a company that with all its successes was closing its doors.
I bring this up as Jim Rohn was also in the top echelon of productivity in this, his second direct sales company. The news of it closing could not have been easy for him. Nor could the news of his mentor and I venture to say dear friend John Earl Shoaff, CEO/Founder of Nutri-Bio being so close to death.
But a true testament to his teachings, instead of packing his bags and running Jim chose to move forward. He possessed the innate ability to see potential in an opportunity. That allowed him to never be stuck in a situation. Always willing, always prepared to change directions if necessary, Jim was able to move on. He was quite often heard to say success wasn't locked to the direction of the wind but rather the "setting of the sails" and prepared him to do just that time and time again.
He definitely was one that not only preached business philosophy but lived it. I urge you to listen to the audio recording Your Network Marketing Business.
As a member of his local Rotary club during the early 1960's he would be asked to present his business philosophies to the other eager members who recognized the value that they had before them. Then his personal AhHa moment blossomed in 1963 when he was asked to speak before a group of businessmen at the Beverly Hills Hotel.
His new vocation set sail. Jim Rohn became one of America's revered motivational speakers and personal development experts. In the course of his life, he traveled not only all across North America but across the globe training and inspiring millions of eager businessmen.
With his inspiration, a new business model emerged. He mentored other young aspiring entrepreneurs like Tony Robbins opening up a market for those who also wanted to follow their dream of inspiring others to live their dreams. He created an era of motivational speakers that is still with us today. There seemed to be an insatiable appetite for others looking for answers with their own personal journies. And there still is.
I cannot think of any inspirational speaker that has not at some time in their career followed at least some part of Jim Rohn's philosophies.
Always learning, always wary of his message Jim Rohn taught humanity in a business world that at times seemed to lack it. He had people looking at themselves and identify why they were not succeeding and what it is they needed to do to change that direction.
Not only was Jim Rohn a successful speaker, he also published several books detailing his philosophies.
His co-penned fictional novel The Twelve Pillars became a very unique way of demonstrating just how these philosophies work. A refreshing platform from the often dry literature one is faced with when philosophy and business are mixed.


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More New Tunes from Modern Hut
July 18, 2013 nathan.lankford Leave a comment
I can't say enough about how much I am enjoying Modern Hut right now. This new track from the songwriter features the voice of Screaming Females frontwoman, Marissa Paternoster, who just happens to be the most bad ass girl in rock n' roll right now. The duo have written a song that reminds me of a more serious version of The Moldy Peaches, with the focus on the lyrics here much more serious than something like "Who's Got the Crack." It makes for a special listen, providing more anticipation for Generic Treasure, which hits stores on August 6th via Don Giovanni Records.
Smooth Dance with Barbarossa
I'd be remiss if I didn't post this track, as I'm really warming to the sound of Barbarossa. On this song, the bass line works just beneath the vocals, providing a groove that gets your feet moving. However, the track also has this bright pop explosion that bursts forth during the chorus that makes the song more than just a minimal electronic dance number. You should all be on the lookout for the new record from the London producer on August 6th when Memphis Industries releases Bloodlines.
Angular Post Rock from TV Ghost
Listening to this new track from TV Ghost, I'm reminded of what I always expected Interpol to become. Haunting vocal performances haunt the track, and the guitars cut in and out of the forefront of the song, often lurking back into a cave of distortion. There's this driving tension that holds the whole song together; it almost seems to have this beautiful insanity wrapped tightly beneath a blanket of crashing cymbals. The fact that the song sprawls over five minutes long only adds to the feeling of impending doom, which, oddly enough, you'll find yourself enjoying. Embrace it when the band release Disconnect on September 3rd via In the Red Records.
Catchy New Tune from Math and Physics Club
I've already brought you one great tune from Matinee Recordings this week, and as promised, the label has more great music to share with you all. The latest song comes via Math and Physics Club; it's easiest one of the catchiest tracks the band has composed to date. The song opens with some drum work accompanied by hand-claps, just before the rest of the act joins in with infectious guitar lines. Personally, aside from the clapping, I'm really finding myself attached to the lyrical delivery on this tune. You'll be wise to order the Long Drag 7″ straight from Matinee Recordings, and browse while you're there…you'll find tons of hits to order from one of our favorite labels.
[audio:http://austintownhall.com/wp-content/uploads/2013/07/01-Long-Drag.mp3]
Bright Pop from Army Navy
It's raining a ton here in Austin, well, it was, so I need something to lighten up everyone's spirits as we call it a day over here at our office. I figured the latest track from one of our favorites, Army Navy, would do just the trick. This time, there's a bit more energy in the songwriting, though the band still maintains that gentle side that made them so endearing in the first place (see 1.07). If the band can keep writing tracks like this one, we'll all see their popularity continue to grow and grow…that's a good thing! You can find this track on their Crushed EP, which comes out on August 20th.
Check Out New Rock from Joanna Gruesome
A long while ago I posted a hit from Joanna Gruesome, and it appears Slumberland Records were watching, as they just picked up the Cardiff group to release the band's debut album, Weird Sister. On the first single from said album, the group comes out with a rioting rock taste, blasting off noisily. But, one of the reason's I love the band is evident in the track too; they pull back from the barrage of noise and add a sweetness to the track, with softer melodies and a bit of serenity….of course they blast right back off again. You can grab the album from the group on September 10th when it hits stores.
Floating Pop from Money
There's really not too much information floating about for Manchester outfit, Money, although they did get a decent response with their Cillian Murphy directed video. I'm pretty impressed with the group's sound, with the group making stunningly dense pop tunes with various movements within their tracks. You've got to listen carefully to grasp, or hear rather, all the tiny touches the band put into their songwriting. Should make for a remarkable debut when they release The Shadow of Heaven on August 26th via Bella Union.
New Track from Porches
The last month has been pretty kind to New York's Porches, garnering a strong positive response with their early single. They're back already with the newest tune from their upcoming album, Slow Dance in the Cosmos. For my ears, this song has two personalities, both equally enjoyably. The first is sort of an amped up sad-bastard approach to pop structure; it slowly builds until the change up. It's brief, but there's a bouncing step that comes in a couple of times in the track. I like that it gives a different touch, though slight, to the song's overall feeling. Give it a go and see what you think. You can pick up the album on August 27th via Exploding in Sound Records.
Enjoy Rainbow Gun Show
I just picked up this gem of a 7″ from Rainbow Gun Show the other day at the record store, and I'm so hooked that I had to share it with you all. The Chicago act has this lo-fi quality that bursts with catchy melodies. I can' describe the sound perfectly, so I won't even try, but I think that this 7″ has the making of a great future for the group; they clearly have elements that make their music accessible and interesting. If you're into the sound, you can pick the release up from Hozac Records, who I consider pretty dependable.
Exciting New Music from Of Montreal
Okay, so Of Montreal puts out tunes almost every year, so why is this so exciting? Well, for me, this single seems to exemplify the sound from the group that I first fell in love with when I jammed to Satanic Panic in the Attic; it's not all bleeps and bloops, choosing to focus on more traditional guitar based melodies. Still, it's every bit a Kevin Barnes sound, which never ceases to impress me; I really like the vocal tone switch around the 2:15 minute mark. The new record is titled lousy with sylvianbriar, and it'll see a release from Polyvinyl Records on October 8th. Hope it all sounds this great.


Showing results for tags '.44 special'.
I just loaded up a bunch of .44 Special with Badman Bullets 165 RNFP bullets. Manual states 4.9 min to 6.0 Max. I loaded 20 each starting with 5.0 and in .2 grain increments stopping at 5.8 grain with HP-38. Shooting each weight left handed, right handed and gunfighter. I found the best accuracy was a pretty close draw between 5.4 and 5.6 in my Colts. Should I just load up more at 5.5 and call it done? Anyone else using 165 grain bullets in .44 Special using either HP-38 or W231? Pet Load with that powder?