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Check out the author interview index HERE.
Interview with Robin E. Mason #4
About the Author
I've always had voices—er, stories in my head. I once said I should write them all down so someone could write them someday. I had no idea at the time that someone was me!
I have been writing since 1995, and began working in earnest on my debut novel, Tessa in 2013. Meanwhile, I cranked out a few dozen poems, made countless notes for story ideas, and earned my BFA in Interior Design. I lived with depression for many years, and the inherent feelings of worthlessness and invisibility; I didn't want to be who I was and struggled with my own identity for many years. My characters face many of these same demons.
I write stories of identity conflict. My characters encounter situations that force the question, "Who am I really?" For all who have ever wondered who you are or why you're here, my stories will touch you in a very real—maybe too real—and a very deep way. I know, I write from experience.
GOODREADS | FACEBOOK | TWITTER | PINTEREST | INSTAGRAM
GOOGLE+ | AMAZON | BLOG | WEBSITE
1. How frequently do you hear from reader fans? Do you have any fun and/or weird stories?
It still feels surreal to me that I have "fans" aka avid readers!! I have several friends, some of whom I've known for years and some who are "newbies" who post and comment regularly on Facebook, especially in anticipation of the next story!! I've only just decided to start a page for fans—Robin's Reader Flock!!! #IMTOTALLYTRIPPING
Last summer, a friend of mine, and avid reader, "L," was going for some medical treatment. I commented on her post that I would be praying for her in the coming weeks and days. She replied that she'd like copies of all of my books to read during her recovery. A mutual friend private messaged me that she would purchase the books for our friend. "L" has now read all of my books thus far, some twice even!! Authors LOVE avid readers like "L." PS, "L" has recovered nicely.
2. If you could go to lunch with one of the characters from The Silent Song of Winter, who would you pick and why?
GREAT QUESTION!! I think I'd choose Olivia Divine. She was a seeming minor character who came to me at a random moment—I was in the kitchen—and turned out to have a significant role in Pearl's story!
3. Who was the hardest character to write in The Silent Song of Winter? The easiest?
OY to the VEY! I ask this question, too, and it's kind of a head scratcher! LOL
Pearl was probably the easiest for me because of all my characters thus far, she is the most like me!
Truly, because I get into the head of each of my characters, it flows fairly easy for me. The saddest to write was Odette Marchand, because she is so unhappy in life, and therefore it is her goal to make everyone else miserable.
4. What inspired the idea for The Silent Song of Winter?
Funny thing… I had this idea (still do!) for a totally different story but when I sat down to write it, this series came out!! And it's way different to what I expected! LOL #Imapantzer #notaplotter Seasons turned into a story of friendship, and mystery and suspense. And always the struggle of self-worth and identity / purpose.
By the way, I will write that totally different story—methinks that's the story behind my next series, The Steppe House….
5. What do you want readers to take away from reading The Silent Song of Winter?
That we are loved. That we don't have to prove ourselves to those who love us. That perfectionism is a cruel taskmaster, and no mistake is unforgiveable. That we are loved in spite of our mistakes.
Request an Interview
Hello and welcome to my author interview page. Here there will be interviews with some fun and amazing authors.
If you would like to request an author interview, please contact me via the contact page or email me at singinglibrarianbooks@gmail.com.
Check out the author interview index HERE.
Follow the author interviews blog via email:


Gyprock Acrylic Stud Adhesive is used for adhering plasterboard and fibre cement wallboard to timber and steel frames.
Gyprock Acrylic Stud Adhesive is a simple and convenient way to adhere plasterboard sheets to timber wall studs and ceiling joists as well as metal framing members. It is specially formulated to offer excellent adhesion properties and is easily identified, once installed, by its distinctive blue colour.
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Gyprock Acrylic Stud Adhesive is widely used in residential and commercial wall and ceiling applications. It is not for use in fire rated systems or for walls where tiles will be applied.
For smaller jobs, the Gyprock DIY range includes a handy 1kg DIY Acrylic Stud Adhesive bucket.
Acrylic Stud Adhesive is available in a 5.5kg bucket and 900g sausage both with a shelf life of 1 year from date of manufacture.
Acrylic Stud Adhesive is manufactured by CSR Gyprock in Australia to stringent product specifications.
Store unopened sausages below 30°C in a cool dry place away from direct sunlight. Opened sausages may skin and form a lump near the opening. Discard any lumps or skin and remaining product can be used.


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Husband Clutches His Chest When He Sees Wife'S Short Blonde Makeover
Makeovers can be life-changing – or, they can follow a big life change, making someone look just as different as they feel.
Many of us have dreamt about getting a big makeover, but they're both nerve-wracking and expensive.
There is a way to get your fix, however, if you're obsessed with seeing before-and-afters – The Makeover Guy.
Wikimedia Commons Source: Wikimedia Commons
The Makeover Guy and his "appearance studio"
Tucked away in Minneapolis, Minnesota is a salon belonging to Christopher Hopkins – actually, he prefers to call it an "appearance studio."
Hopkins is a former entertainer-turned-stylist who now specializes in transforming men and women over the age of 40 into their best selves.
With 345,000 subscribers to his YouTube channel, it's safe to say he's become a cyber-star as well.
Hopkins'quick and satisfying videos don't make you wait out the transformation – he shows you right away. And they're always amazing, even in the rare cases when they're subtle.
But most of the time, they're pretty magnificent.
Meeting Laura
Laura came all the way from Washington state for her makeover (which gives you an indication of how in-demand Hopkins' services are).
And she's pretty lucky. At 56, she has 2 grown daughters and a loving husband who treated her to the makeover she wanted since she was feeling drab.
YouTube Screenshot - MAKEOVERGUY Source: YouTube Screenshot - MAKEOVERGUY
We have to admit that we think Laura already looks pretty great. But she's not happy with her hair.
She wanted to project a different image and according to the caption: "Classic, timeless, put together, but feminine was her goal…"
Laura said she no longer recognized herself anymore, especially after going back to her natural hair color 5 years earlier.
It's safe to say her goals were achieved and she swung her new hair and said "I feel like me again!" after looking in the mirror.
The sweet reaction
Laura's reaction is enough, but in this case, we get to see her husband walk into the room for a first glimpse as well.
And let's just say his reaction brought a tear to more than a few eyes.
His reaction is so genuine and he's utterly taken aback by her beauty the minute he lays eyes on her.
"This is what it was like when I saw her at our wedding," the emotional husband said.
Putting his hand on his heart – to stop it from fluttering away, we imagine – he gave Laura a big hug (though one that wouldn't mess up her hair and makeup! Smart man!).
When asked if he liked her new blond hair, he said "Oh my gosh, yes."
The two shared a sweet moment, with Laura flashing a big smile and her husband full of emotion.
"You guys bring what's on the inside to the outside, and that's a very special gift," he said, before turning to Laura and saying "And so are you. You look great."
Awwww.
Commenters couldn't hold in their emotions.
"Who needs to sit through an entire cheesy Hallmark special? That man's reaction to his gorgeous wife's new look is enough to make me feel cozy inside," one said.
The new Laura
Laura's makeover wasn't drastic. Her skin is already beautiful and brightening up her hair and cutting off the unhealthy ends was really all that was needed.
She did get a pretty big chop though!
An ashy blond base with golden highlights gave her hair new dimension. and wrangling her curls was easy business for the pros.
Some bronzer, eyeliner, and a soft mauve lip color completed the look that should be easy to maintain.
It was a home run yet again for Hopkins. And commenters – of which there are hundreds despite the video being posted just a few days ago – were largely in favor of the whole thing.
But what matters most is that Laura and the man she loves will get a chance to enjoy the gift.
Be sure to scroll down below to see the new Laura!
Please SHARE this with your friends and family.
Source: YouTube – MAKEOVERGUY , Facebook – The Makeover Guy
According To Experts, Here Are The Top Beauty Products Available For $10 Or Less
Jan 20 at 19:03
Andrea Bocelli'S Daughter Joins Him For Heart-Melting 'Hallelujah'
Famous Deer That Visits Same Town Every Christmas Shows Up With An Arrow Through His Head
Poor Student With 'Nothing To Give' Comes Up With Free Gift For Teacher She'Ll Never Forget
Boston Dynamics-Built Robots Dance It Out To 'Do You Love Me?'

redpajama_set_name: RedPajamaCommonCrawl


text:
Dhangmari Wildlife Sanctuary () is a wildlife sanctuary located at Bagerhat District of Bangladesh. The area of the sanctuary is . It was officially declared as a wildlife sanctuary by the government of Bangladesh on 29 January 2012.

It is one of the safe zones for vultures as per the Vulture Safe Zone-2 Schedule of the government of Bangladesh in 2012. Dhangmari wetland is one of the dolphin sanctuaries in Bangladesh.

See also
 List of wildlife sanctuaries of Bangladesh

References

Wildlife sanctuaries of Bangladesh

redpajama_set_name: RedPajamaWikipedia


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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">

<title>SWI-Prolog 8.3.28 Reference Manual</title><link rel="home" href="index.html">
<link rel="contents" href="Contents.html">
<link rel="index" href="DocIndex.html">
<link rel="summary" href="summary.html">
<link rel="previous" href="acknowledge.html">
<link rel="next" href="quickstart.html">

2 Overview

Section Index

2.1 Getting
started quickly

2.1.1 Starting
SWI-Prolog

2.1.1.1 Starting
SWI-Prolog on Unix

2.1.1.2 Starting
SWI-Prolog on Windows

2.1.2 Adding
rules from the console

2.1.3 Executing
a query

2.1.4 Examining
and modifying your program

2.1.5 Stopping
Prolog

2.2 The
user's initialisation file

2.3 Initialisation
files and goals

2.4 Command
line options

2.4.1 Informational
command line options

2.4.2 Command
line options for running Prolog

2.4.3 Controlling
the stack sizes

2.4.4 Running
goals from the command line

2.4.5 Compilation
options

2.4.6 Maintenance
options

2.5 UI
Themes

2.5.1 Status
of theme support

2.6 GNU
Emacs Interface

2.7 Online
Help

2.7.1 library(help):
Text based manual

2.7.2 library(explain):
Describe Prolog Terms

2.8 Command
line history

2.9 Reuse
of top-level bindings

2.10 Overview
of the Debugger

2.10.1 The
Byrd Box Model And Ports

2.10.2 Trace
Mode Example

2.10.3 Trace
Mode Options: leash/1 and visible/1

2.10.4 Trace
Mode Commands When Paused

2.10.4.1 Control
Flow Commands

2.10.4.2 Informational
Commands

2.10.4.3 Formatting
Commands

2.10.5 Trace
Mode vs. Trace Point

2.10.6 Spy
Points and Debug Mode

2.10.7 Breakpoints

2.10.8 Command
Line Debugger Summary

2.10.8.1 Trace
Mode

2.10.8.2 Trace
Points

2.11 Compilation

2.11.1 During
program development

2.11.2 For
running the result

2.11.2.1 Using
PrologScript

2.11.2.2 Creating
a shell script

2.11.2.3 Creating
a saved state

2.11.2.4 Compilation
using the -c command line option

2.12 Environment
Control (Prolog flags)

2.13 An
overview of hook predicates

2.14 Automatic
loading of libraries

2.15 Packs:
community add-ons

2.16 The
SWI-Prolog syntax

2.16.1 ISO
Syntax Support

2.16.1.1 Processor
Character Set

2.16.1.2 Nested
comments

2.16.1.3 Character
Escape Syntax

2.16.1.4 Syntax
for non-decimal numbers

2.16.1.5 Using
digit groups in large integers

2.16.1.6 Rational
number syntax

2.16.1.7 NaN
and Infinity floats and their syntax

2.16.1.8 Force
only underscore to introduce a variable

2.16.1.9 Unicode
Prolog source

2.16.1.10 Singleton
variable checking

2.17 Rational
trees (cyclic terms)

2.18 Just-in-time
clause indexing

2.18.1 Deep
indexing

2.18.2 Future
directions

2.18.3 Indexing
for body code

2.18.4 Indexing
and portability

2.19 Wide
character support

2.19.1 Wide
character encodings on streams

2.19.1.1 BOM:
Byte Order Mark

2.20 System
limits

2.20.1 Limits
on memory areas

2.20.1.1 The
heap

2.20.2 Other
Limits

2.20.3 Reserved
Names

2.21 SWI-Prolog
and 64-bit machines

2.21.1 Supported
platforms

2.21.2 Comparing
32- and 64-bits Prolog

2.21.3 Choosing
between 32- and 64-bit Prolog

2.22 Binary
compatibility

redpajama_set_name: RedPajamaGithub


Hi Sal….just curious what was the source for Lake Ronkonkoma? Thanks.
Nice job! I believe I found almost everything filmed in Manhattan, Queens LI and Brooklyn (including Henry's real home and the cab stand on Pine Street in East New York), NJ & New Rochelle from other sources, Except I am unable to find the location where Tommy (J. Pesci) was picked up to at his mother's house to be made (not the house he was whacked – filmed in Brooklyn.) I am almost positive it is in Queens Village in the very same area within blocks of the location of the scene at the end of the movie when Henry (R. Liotta) drops of silencers for Jimmy (R. De Niro) which is 94-24 220th Street at 94th Road – off Jamaica avenue near and not too far from Springfield Blvd. If anyone knows, please let me know. If anyone needs any locations, please let me know as well. Yes, Prospect Park is the location of teh scene where Batts was buried and the lion scene. Thanks.
I once used to have that house listed in my initial post, but later decided to remove it since we are never shown a clear shot of it. Nevertheless, according to a visitor, it was located on 242nd St. in Bellerose, Queens.
1.Houses in background of the made man scene pick up – both (yellow and blue) 2 story colonial with rounded roof – all over the area with Jimmy and Henry scene with silencers at 220th Street.
2. Cement driveways with the grass center- all over area – although a lot since cemented or paved.
But if you have a pic that would e great! As I have mentioned previously, I have located ~ 95% of all scenes that I could find except this one. I located 46 locations in total with 29 from the film and 17 that are the actual location of the event. Of the 46 locations identified with addresses, the breakdown geographically is as follows: Queens – 20, Brooklyn – 11, Manhattan – 7, Nassau Co. – 4, Other (New Rochelle, Staten Island & Fort Lee, NJ) = 3 , I have pics and addresses of 46 if you need any. I also have a video from the scene at the 220th street location from the owner of the house when it was filmed in 1989. Thank you again!
Hii again Alexandre….I tried that road in Belelrose but it does not look to be a match. Do you have a cross street for your 242nd street location- it can any section: Bellerose,, or even Larelton or Rosedale areas (other 242nd streets)? Better yet, do you have a street address? And if you still have the old pic that would also be great as well. Thanks again and let me know if you want any locations – i could even email you my spread sheet of info.
The address I was given on 242nd St. is 88-41. There are certainly similar-looking houses as the ones seen in the background.. by any chance, would you know where it was when Henry is seen leaving prison?
That is great thank you very much. That was the closest street that I could find (88th Drive and 89th Avenue – both in Bellerose Queens (as opposed to Bellerose Nassau it is actually in both Counties) that was a close match. The leaving prison scene is one of six that I can not find I am sorry. Any others you need?
Hi Alexandre…..I am sorry to be a "pill". But I am still having problems matching the address of the location to the the film. Do you still have the picture the visitor had sent to you? Once again thanks for all your help.
it was a house on 241st street in bellerose, queens owned by the Valentines. the scene with the yellow volkswagon driving down the block and the old lady walking with the shopping cart and it was joe pesci's moms house where they went to get the knife because he hit a "deer".
BTW, the one little scene that takes place at the end of movie when "Frenchy" and "Buddha" bodies turn up in the dumpster after Lufthansa. This scene was filmed at Jackson Hole Diner (Airlin Diner) in the back. This can be verified by the old yellow/brown shed on the adjacent property behind the fence which is still there today. And also by the brick facade and windows on the apt match the apt in the background of truck heist scene earlier in the movie which was filmed there.
Hi Alexandre…By the way, another little known location. All the stills hots from Karen's voice over after the wedding scene "We always did everything together…" "Little Jimmy" birthday cake -not sill, , playing cards w/ Cutty Shark on table, dinner In yard with wives, water poured over Henry's head, etc. were filmed at 220th Street in Queens Village location where the scene was shot for Henry delivering the rejected guns to Jimmy. Verified with previous owner and also matches the previous owner's home videos posted on Face Book of interview with Joe Pesci (outfits of cast match to the to the film – Pesi brown jacket, DiNiro knit blue short sleve Shirt, etc.).
Could you post a new link to the face book videos. I tried clicking on the one in your post but it doesn't work. Thanks so much!!
The scene when Karen is on a Pay Phone Calling Henry and Crying was filmed in the Parking lot next to Wykagyl Country Club in New Rochelle NY (near the filming of parents House and Bruce House on Alfred Street) as per another posted source – not verified.
Hi Alexandre….I did find the correct address. It was actually on 241st Street vs. 242nd Street. Tommy's mother house is 89-34, the 2 visible houses across the street are 89-35 and 89-37. All houses are located on 241st Street, Bellerose – Queens. Thanks again.
Henry Hill's actual house growing up 391, 394 or 398 Pine Street East New York, Brooklyn (Queens Border). Paulie's Euclid Ave. Cab Stand was across Street at 407 Pine St.. Paulie's nearby businesses were -Presto Pizzeria on Pitkin Ave. & Fontainebleau Florist-Fulton St.(Cypress H.). Have pics of the sights I found and visited but not sure how to post them?
Henry & Karen first date was at Villa Capra Restaurant at 488 Chestnut St. across from LIRR, in Cedarhurst at the time (Owned by Frankie "The Whop" Manzo – Former Lucchese "Member"), It is now Amsterdam Burger & Co.
Greg – where did you get this info from?
After the Villa Capra closed, the location became Mother Kelly's, a popular Italian restaurant that was there for many years. The owners retired and closed the place a few years ago and I guess that's when it became Amsterdam Burger. However, that place closed down in 2016 and is now a restaurant called Doma Land & Sea, a steak and seafood place.
The scene of Jimmy and Henry going to Morrie' at "For Men Only" hair salon was shot at Arsen's UNISEX SALON at 103-39 Queens Blvd, Forest Hills – past Yellowstone Across from Parker Towers.
The actual "The Suite" lounge and super club appears to be at 111-20 Queens Blvd. in Forest Hills in he same strip of stores as the fire house. Currently a health club with pool. There are a few sources and addresses for this location. This is the most reliable and appears on 2006 Henry Hill documentary with appearances and comments by Henry Hill, Ed McDonald, Nick, Pileggi and Martin Scorsese.
5 80th Street, in Bay Ridge, Brooklyn.
Anyone know the location of Paulie's house in the beginning of the movie?
When young Henry and Tuddy deliver the message to Paulie in the rain? Or when Henry is older and he talks to Paulie in the backyard and Paulie talks about Gribbs staying away fro the "junk"? Some other scene?
The scene when young Henry and Tuddy deliver the message in the rain.
I was in that neighborhood the other day so I decided to stop by to see the lobby. The doorman said ithe movie was filmed there but the lobby had been renovated and intercom system was removed but he showed me where it was located when the movie was filmed. Nice find and also nice Bldg!
Being from ridgewood which boarders brooklyn n queens and lived there from 69_80 i see the exact way i remember ny looking with the dirty everything look of that era. I didnt realize at the time that all that was so close to my house! Watch Goodfellas once in a while. A gift from the boys in hollywood. Thanks for a classic.
The very last shot when Henry is in witness protection is filmed in Marlboro, NJ.
It's next door to my wifes grandmothers house.
Hi Just found this site – where in Marlboro, NJ is that house? I live in the area and would love to see it. Thanks!
Where was the cab stand filming location? You have Henry's childhood home filming location address. Is there one for the Pitkin Ave Cab Co?
When Henry gets ready to go to jail, he hugs his wife an enters his car: a corner, Madison Avenue and… ?
Hi guy's. Merry Christmas my husband and I are watching Goodfellas and I've always wondered where did they film Henry and Karen's wedding?


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text:
Category: Smiles
The Coral at Leamington Assembly
As part of a far-reaching UK tour, the 15th March brought The Coral's epic appearance at Leamington Assembly. It was brilliant to have such a gig so close to my…
APRE and Sea Girls at Hare & Hounds, Birmingham
21st March 2019 22nd March 2019
On the 26th February, Hare & Hounds held another indie extravaganza. This time round, the venue accommodated the rising stars Sea Girls, supported by the spirited duo APRE. Here's what…
whenyoung & The Ninth Wave at Hare & Hounds, Birmingham
On the 12th February, whenyoung performed at the iconic Birmingham venue Hare & Hounds. With support from The Ninth Wave, this is how that electric night unfolded. The Support Act:…
Miles Kane at the O2 Institute, Birmingham
On 27th November, Miles Kane performed at the O2 Institute in Birmingham as part of his expansive UK tour. The tour highlighted the great material from his new album Coup…
The light that never went out: Johnny Marr at the O2 Institute, Birmingham
6th November 2018 6th November 2018
Johnny Marr not only found fame through his unparalleled skill as a guitarist in the indie rock scene with The Smiths in the eighties, but his solo career has consistently…
The AM:ZM Exhibition in London
While London's O2 Arena was home to Arctic Monkeys, Great Titchfield Street's Noho Showrooms was home to the AM:ZM Exhibiton. The contents of this free exhibition were created by the…


I like the looks of this RDA, I'm thinking of trying an RDA in a few months and having this would give me a head start. I also like the fact it has squonk capabilities, that is something I also plan to try one day. The only other RDA i've tried is a Buddha, and although it works I always had some kind of problems so it never caught on with me.
Which mod do you wanna pair it with among the items on our site? And also leave the link.
After 9 days of giveaway activities,the Wotofo Profile RDA. is finally comes to an end.
It's a great honor to be here to publish the final result of the giveaway.
If you failed to win this giveaway ,don't feel pity ,we also have several other giveaway activities underway.
Everyone who post the right reply in this thread is in the winner draw.


Posted by Daniel Packer in Saving • 0 Comments - Be The First!
What better way to lose money than forget that you're paying it? Many subscription models bank on two fundamental principles to squeeze extra payments from their customers. First, these companies hope that you'll forget about that recurring credit card charge. Second, they make canceling a hassle, and hope that customers will give up instead of canceling their service.
Subscription services appeal to our love of convenience. Thousands of companies now offer a variety of subscription services. Companies such as Blue Apron, Hello Fresh, and Hungry Harvest deliver ready-to-make meals or produce to your doorstep for a recurring weekly or monthly charge, while other niche companies offer a variety of useful services. Have a dog? Bark Box will deliver treats and chew toys. Need to shave? Dollar Shave Club delivers shaving products straight to you at a discount. Pretty much anything you need can be delivered to your door.
Subscription-based companies hope you'll love their product indefinitely…but that's likely not the case. And when you're ready to cancel, they hope that you'll forget so they can squeeze a few more deliveries and earn those extra few dollars out of you. Here's how to better track and end automatic subscription services.
Before you agree to any subscription service, it's imperative to read the small print. How often will your card be charged? When will it be charged? How do you cancel? When do you need to cancel to avoid an unwanted delivery? Know the rules before you sign up, or review the rules if you're already signed up.
While knowing when the company will charge your card for their services for budget purposes, it's more important to know when you need to cancel. So for example, if the company charges your card on the 15th of each month, but you need to cancel two weeks before this charge, you should make a note on your calendar that you'd need to cancel the service on the 1st of the month. You could also set a helpful reminder with Alexa or Siri. Forgetting this information would lead to an extra delivery or a late termination fee. Highlight this date so it pops and set it as a recurring notice. Review your calendar monthly to decide if you need to cancel any subscriptions.
When you decide to cancel, do it immediately. Don't hem or haw. Be prepared to endure a maze of pressing buttons and waiting to talk to someone on the other end. Unfortunately, many services don't allow customers to simply click and unsubscribe from their services; it's part of their business model.
Trim exists as an AI assistant that works on your behalf to identify and cancel unwanted subscriptions and negotiate lower rates when possible on monthly services like cable. It was developed to help individuals stay on budget. Users must link accounts such as credit cards, so do your research to determine if Trim would be right for you. Reviews span the spectrum from great (saved the user money) to horrible (attempted to negotiate cable bill without user's approval). The program is currently free to download, but will take a percentage of profit if it successfully lowers a bill.
On a final note: many companies offer discounts when you sign up if you subscribe on a three month, six month, or yearly basis. Watch for these subscriptions as the charges are easier to forget when they only happen twice or once a year. You may save on the monthly deliveries, but it's easier to miss the cancelation period.
We never feel like we give enough to charity, and there are great reasons why we should donate more, so this year, we're going to take everything we save and give it to charity.
To clarify, we're not going to take everything we earn and give it away, we're just thinking of a creative way of finding more money to donate to charity.
Any time we save money that we would have spent, we'll tally it up and give it to charity. There's a difference between spending less and actually saving money.
You're simply spending less if you're shopping for a TV and decide to go with a $500 version instead of the $800 version. You still spent money, you just spent less of it. That's not real savings.
Contrast this with the example of bringing that $500 TV to the register and it ringing up for $400 because of a special sale that you weren't aware of. You were fully prepared to pay the $500, so the $100 savings is very real.
The first instance happened this week when I was getting ready to file my taxes. I use TurboTax, which this year would have been $69.99 for the federal return + $36.99 for the state return. I was fully prepared to pay that because what other choice do I have? Luckily, someone at work had a code that made it totally free. So I made a $106.98 contribution to charity on Saturday night.
There are a few categories that I can think of right now where we'll be saving money this year.
Using coupons when we shop online. Anything we were going to buy but a quick Google search results in a price drop is a perfect candidate.
AmEx Offers. Sometimes there are offers for "save $10 when spending $20" at a store. If it's somewhere I shop anyway, that is real savings for me.
There will be others that pop up throughout the year, like random $15 off Google Express orders that we use.
There are charities we support every year, but I'd like this money to go toward new causes. With all of the recent changes to our country, we feel it's important now more than ever to put our money where our mouth is and support social causes that we think are important to the fabric of our society.
I am really excited to start this adventure, and it motivates me to be extra vigilant in finding real opportunities to save money.
What do you think of my plan? Can you think of other situations where I'll find real savings?
For harried singles and families, dining out has become a lifestyle necessity. Drive-through fast food and counter service eliminate food shopping and preparation time by providing hot and tasty, if sometimes unhealthy, ready-to-eat meals for people on the go.
You may be surprised to find out the number of meals you eat at restaurants each week. Include coffee stops and purchased snacks. Set a goal to reduce your total prepared food or meal count by a certain amount. For example, if you dine out twice a week, try to cut back to once weekly. You will soon notice significant savings in your budget if you eat at home more often.
When you calculate money spent on food when tied to other activities, like bowling and video rentals, food costs can skyrocket. Look for cheaper ways to have fun and bring a packed lunch that can be fun and nutritious instead of settling for fast food, which, let's face it, is often greasy and gross. You can also attach restaurant spending to the grocery budget. Set a specific monthly amount for eating out, whether you consider it entertainment or an auxiliary expense to leisure activities, and don't go over the limit. You'll appreciate newfound control over food and money, two indispensable commodities.
Sit-down dining often leads to the temptation of ordering beverages, appetizers, and dessert, along with tax and tip, running up a sizable tab. Instead, make a run to the supermarket for rotisserie chicken or pizza. Eat at home with homemade salad and ice tea as you savor the hefty cost saved by skipping the restaurant.
Rather than eating out, make it a veggie and dip night with a fun dessert or pack a cooler with sandwiches and lemonade for a picnic. Get frozen custard afterward for a special treat. Soup, chili, and casseroles with tasty bread are more great light meals to enjoy.
Most entrees these days are large enough for two, so share a meal by dividing food in half or ordering two different sides while sharing the main item. You can also use this method with the help of coupons that you can find online at sites like Groupon or at some restaurants' websites. Use a coupon and split the remaining cost with a friend or family member. You can also share a huge appetizer with bread or a side and skip the large entree to cut the cost.
Eating out doesn't have to cost a fortune. Try tips like these to continue dining out while reducing the cost of prepared food and perhaps some calories, as well.


Registration for 2019 AP Exams will take place Feb. 25 - March 22, with late registration (+ additional $6) from March 23 - 27. Register at the AP Test Service registration website. Please see detailed information here.
All students taking AP classes received detailed AP exam registration information in early February 2019. They received this form, which details the online exam registration process, along with a guide called Bulletin for AP Students & Parents, which goes into detail about how AP testing works and lists the dates of the exams this May. If your student doesn't have this information packet, they may ask their AP teacher for one, or pick up one in the Counseling office.
The cost of each AP exam is $94. Students who qualify for APS' Free or Reduced Lunch Program may see me for a reduced rate of $3 per test.
Registration runs from Feb. 25 to March 22. Late registration period is March 23 - 27, with an $6 extra charge per test. NO REGISTRATION WILL BE ACCEPTED AFTER MARCH 27, because our test order needs to be placed with College Board at that time.
May 2019 AP exam schedule here.


text:
How we rated each Chesterfield player in big win against Barnet
The men who played for Chesterfield FC in the early to mid noughties - including the last player EVER to score at Saltergate
From left to right, John Seasman, Phill Walker, Brian Scrimgeour, Jim Brown, Steve Kendal, Mick Henderson and Ernie Moss, parading the Division Four title in front of the fans in May 1985.
How many of these faces do you remember playing for Chesterfield in the early to mid 1980's?
Chesterfield had some memorable moments and players in the early 1980's, including a team who won the Anglo Scottish Cup and the Fourth Division.
By Stephen Thirkill
Thursday, 13th January 2022, 10:11 am
Updated Thursday, 13th January 2022, 10:20 am
But how many of these players can you remember? If you have pictures or memories of your time supporting Spireites during this period, email [email protected]
5. Chesterfield players Training
This picture shows Chesterfield manager Arthur Cox (right) watching Colin Prophett and Andy Kowalski training when the Saltergate team reported back for training.
Photo: DT
6. The new boys at Saltergate.
Chesterfield FC manager John Duncan (centre) with new signings, from left, Ollie Bernardeau, Andy Kowalski, Lee Coombes and Tony Coyle.
7. Steve Kendall
Steve Kendall looks to beat his man during a match on 4 May 1985.
8. Bob Newton - 1984/85 season
Bob Newton is pictured scoring in a 3-1 win over Crewe in the 1984/85 season. It proved to be a great season with Spireites winning the Fourth Division title with 91 points, five clear of Blackpool.
Photo: Submitted
ChesterfieldSpireites
BackNext Page


Here I am testing one of the Art Surfaces (Meko). Notice how it adds light as well as shadow.
While working in 16 or 32 bits per channel would give me more flexibility in tweaking these but these templates are already so large a higher channel bit rate would be impractical. They are already averaging about 85 megabytes per PSD. They are seamless tiles, but they start at 2048 by 2048 pixels, at 300 DPI.
A question for you – what would you pay for one of these and what would you pay for 10 of them? Let me know in the comments.


Sixty years after they last ran through the streets of the city, trams could be on the verge of returning to Preston.
And some of the ground work has already begun.
Previous Post Preston set for trams before Christmas Next Post Phase One Ready to Go!


text:
Q: How do I have one user define fields for another user to fill out? I have one type of user creating form fields for a job posting. They can define the field type and name (eg. type:"string" and name:"job_title").
My question is how do I store this data so that the job form for the applicant user has all of these defined fields?
Currently the job and form template is organized like this:

*

*job BELONGS TO employer_template

*employer_template HAS MANY jobs

*employer_template HAS MANY template_fields

I've tried to search for solutions, but haven't seen anything that answers it clearly enough.

A: I would avoid to create all those tables, it sounds an overengineered solution.
You could simply serialize the fields and the contents, and store them in just one field.
That is automatically done by serialize:
class employer < ActiveRecord::Base
  serialize :custom_fields
end

Simple and fast :)

redpajama_set_name: RedPajamaStackExchange


text:
"""
Blaze integration with the Pipeline API.

For an overview of the blaze project, see blaze.pydata.org

The blaze loader for the Pipeline API is designed to allow us to load
data from arbitrary sources as long as we can execute the needed expressions
against the data with blaze.

Data Format
-----------

The blaze Pipeline API loader expects that data is formatted in a tabular way.
The only required column in your table is ``asof_date`` where this column
represents the date this data is referencing. For example, one might have a CSV
like:

asof_date,value
2014-01-06,0
2014-01-07,1
2014-01-08,2

This says that the value on 2014-01-01 was 0 and so on.

Optionally, we may provide a ``timestamp`` column to be used to represent
point in time data. This column tells us when the data was known, or became
available to for use. Using our same CSV, we could write this with a timestamp
like:

asof_date,timestamp,value
2014-01-06,2014-01-07,0
2014-01-07,2014-01-08,1
2014-01-08,2014-01-09,2

This says that the value was 0 on 2014-01-01; however, we did not learn this
until 2014-01-02. This is useful for avoiding look-ahead bias in your
pipelines. If this column does not exist, the ``asof_date`` column will be used
instead.

If your data references a particular asset, you can add a ``sid`` column to
your dataset to represent this. For example:

asof_date,value,sid
2014-01-06,0,10
2014-01-06,1,20
2014-01-07,1,10
2014-01-07,2,20
2014-01-08,2,10
2014-01-08,3,20

This says that on 2014-01-01, the asset with id 10 had a value of 0, and the
asset with id 20 had a value of 1.

One of the key features of the Pipeline API is the handling of adjustments and
restatements. Often our data will be amended after the fact and we would like
to trade on the newest information; however, we do not want to introduce this
knowledge to our model too early. The blaze loader handles this case by
accepting a second ``deltas`` expression that contains all of the restatements
in the original expression.

For example, let's use our table from above:

asof_date,value
2014-01-06,0
2014-01-07,1
2014-01-08,2

Imagine that on the fourth the vendor realized that the calculation was
incorrect and the value on the first was actually -1. Then, on the fifth, they
realized that the value for the third was actually 3. We can construct a
``deltas`` expression to pass to our blaze loader that has the same shape as
our baseline table but only contains these new values like:

asof_date,timestamp,value
2014-01-06,2014-01-09,-1
2014-01-08,2014-01-10,3

This shows that we learned on the fourth that the value on the first was
actually -1 and that we learned on the fifth that the value on the third was
actually 3. By pulling our data into these two tables and not silently updating
our original table we can run our pipelines using the information we would
have had on that day, and we can prevent lookahead bias in the pipelines.

Another optional expression that may be provided is ``checkpoints``. The
``checkpoints`` expression is used when doing a forward fill query to cap the
lower date that must be searched. This expression has the same shape as the
``baseline`` and ``deltas`` expressions but should be downsampled with novel
deltas applied. For example, imagine we had one data point per asset per day
for some dataset. We could dramatically speed up our queries by pre populating
a downsampled version which has the most recently known value at the start of
each month. Then, when we query, we only must look back at most one month
before the start of the pipeline query to provide enough data to forward fill
correctly.

Conversion from Blaze to the Pipeline API
-----------------------------------------

Now that our data is structured in the way that the blaze loader expects, we
are ready to convert our blaze expressions into Pipeline API objects.

This module (zipline.pipeline.loaders.blaze) exports a function called
``from_blaze`` which performs this mapping.

The expression that you are trying to convert must either be tabular or
array-like. This means the ``dshape`` must be like:

``Dim * {A: B}`` or ``Dim * A``.

This represents an expression of dimension 1 which may be fixed or variable,
whose measure is either some record or a scalar.

The record case defines the entire table with all of the columns, this maps the
blaze expression into a pipeline DataSet. This dataset will have a column for
each field of the record. Some datashape types cannot be coerced into Pipeline
API compatible types and in that case, a column cannot be constructed.
Currently any numeric type that may be promoted to a float64 is compatible with
the Pipeline API.

The scalar case defines a single column pulled out a table. For example, let
``expr = bz.symbol('s', 'var * {field: int32, asof_date: datetime}')``.
When we pass ``expr.field`` to ``from_blaze``, we will walk back up the
expression tree until we find the table that ``field`` is defined on. We will
then proceed with the record case to construct a dataset; however, before
returning the dataset we will pull out only the column that was passed in.

For full documentation, see ``help(from_blaze)`` or ``from_blaze?`` in IPython.

Using our Pipeline DataSets and Columns
---------------------------------------

Once we have mapped our blaze expressions into Pipeline API objects, we may
use them just like any other datasets or columns. For more information on how
to run a pipeline or using the Pipeline API, see:
www.quantopian.com/help#pipeline-api
"""
from __future__ import division, absolute_import

from abc import ABCMeta, abstractproperty
from collections import namedtuple, defaultdict
from functools import partial
from itertools import count
import warnings
from weakref import WeakKeyDictionary

import blaze as bz
from datashape import (
    Date,
    DateTime,
    Option,
    isrecord,
    isscalar,
)
import numpy as np
from odo import odo
import pandas as pd
from six import with_metaclass, PY2, itervalues, iteritems
from toolz import (
    complement,
    compose,
    flip,
    groupby,
    identity,
    memoize,
    merge,
)
import toolz.curried.operator as op

from zipline.pipeline.common import (
    AD_FIELD_NAME,
    SID_FIELD_NAME,
    TS_FIELD_NAME
)
from zipline.pipeline.data.dataset import DataSet, Column
from zipline.pipeline.loaders.utils import (
    check_data_query_args,
    normalize_data_query_bounds,
    normalize_timestamp_to_query_time,
)
from zipline.pipeline.sentinels import NotSpecified
from zipline.lib.adjusted_array import AdjustedArray, can_represent_dtype
from zipline.lib.adjustment import Float64Overwrite
from zipline.utils.input_validation import (
    expect_element,
    ensure_timezone,
    optionally,
)
from zipline.utils.numpy_utils import bool_dtype, categorical_dtype
from zipline.utils.pandas_utils import sort_values
from zipline.utils.pool import SequentialPool
from zipline.utils.preprocess import preprocess

valid_deltas_node_types = (
    bz.expr.Field,
    bz.expr.ReLabel,
    bz.expr.Symbol,
)
traversable_nodes = (
    bz.expr.Field,
    bz.expr.Label,
)
is_invalid_deltas_node = complement(flip(isinstance, valid_deltas_node_types))
get__name__ = op.attrgetter('__name__')

class ExprData(namedtuple('ExprData', 'expr deltas checkpoints odo_kwargs')):
    """A pair of expressions and data resources. The expresions will be
    computed using the resources as the starting scope.

    Parameters
    ----------
    expr : Expr
        The baseline values.
    deltas : Expr, optional
        The deltas for the data.
    checkpoints : Expr, optional
        The forward fill checkpoints for the data.
    odo_kwargs : dict, optional
        The keyword arguments to forward to the odo calls internally.
    """
    def __new__(cls, expr, deltas=None, checkpoints=None, odo_kwargs=None):
        return super(ExprData, cls).__new__(
            cls,
            expr,
            deltas,
            checkpoints,
            odo_kwargs or {},
        )

    def __repr__(self):
        # If the expressions have _resources() then the repr will
        # drive computation so we take the str here.
        cls = type(self)
        return super(ExprData, cls).__repr__(cls(
            str(self.expr),
            str(self.deltas),
            str(self.checkpoints),
            self.odo_kwargs,
        ))

class InvalidField(with_metaclass(ABCMeta)):
    """A field that raises an exception indicating that the
    field was invalid.

    Parameters
    ----------
    field : str
        The name of the field.
    type_ : dshape
        The shape of the field.
    """
    @abstractproperty
    def error_format(self):  # pragma: no cover
        raise NotImplementedError('error_format')

    def __init__(self, field, type_):
        self._field = field
        self._type = type_

    def __get__(self, instance, owner):
        raise AttributeError(
            self.error_format.format(field=self._field, type_=self._type),
        )

class NonNumpyField(InvalidField):
    error_format = (
        "field '{field}' was a non numpy compatible type: '{type_}'"
    )

class NonPipelineField(InvalidField):
    error_format = (
        "field '{field}' was a non Pipeline API compatible type: '{type_}'"
    )

_new_names = ('BlazeDataSet_%d' % n for n in count())

def datashape_type_to_numpy(type_):
    """
    Given a datashape type, return the associated numpy type. Maps
    datashape's DateTime type to numpy's `datetime64[ns]` dtype, since the
    numpy datetime returned by datashape isn't supported by pipeline.

    Parameters
    ----------
    type_: datashape.coretypes.Type
        The datashape type.

    Returns
    -------
    type_ np.dtype
        The numpy dtype.

    """
    if isinstance(type_, Option):
        type_ = type_.ty
    if isinstance(type_, DateTime):
        return np.dtype('datetime64[ns]')
    else:
        return type_.to_numpy_dtype()

@memoize
def new_dataset(expr, deltas, missing_values):
    """
    Creates or returns a dataset from a pair of blaze expressions.

    Parameters
    ----------
    expr : Expr
        The blaze expression representing the first known values.
    deltas : Expr
        The blaze expression representing the deltas to the data.
    missing_values : frozenset((name, value) pairs
        Association pairs column name and missing_value for that column.

        This needs to be a frozenset rather than a dict or tuple of tuples
        because we want a collection that's unordered but still hashable.

    Returns
    -------
    ds : type
        A new dataset type.

    Notes
    -----
    This function is memoized. repeated calls with the same inputs will return
    the same type.
    """
    missing_values = dict(missing_values)
    class_dict = {'ndim': 2 if SID_FIELD_NAME in expr.fields else 1}
    for name, type_ in expr.dshape.measure.fields:
        # Don't generate a column for sid or timestamp, since they're
        # implicitly the labels if the arrays that will be passed to pipeline
        # Terms.
        if name in (SID_FIELD_NAME, TS_FIELD_NAME):
            continue
        type_ = datashape_type_to_numpy(type_)
        if can_represent_dtype(type_):
            col = Column(
                type_,
                missing_values.get(name, NotSpecified),
            )
        else:
            col = NonPipelineField(name, type_)
        class_dict[name] = col

    name = expr._name
    if name is None:
        name = next(_new_names)

    # unicode is a name error in py3 but the branch is only hit
    # when we are in python 2.
    if PY2 and isinstance(name, unicode):  # pragma: no cover # noqa
        name = name.encode('utf-8')

    return type(name, (DataSet,), class_dict)

def _check_resources(name, expr, resources):
    """Validate that the expression and resources passed match up.

    Parameters
    ----------
    name : str
        The name of the argument we are checking.
    expr : Expr
        The potentially bound expr.
    resources
        The explicitly passed resources to compute expr.

    Raises
    ------
    ValueError
        If the resources do not match for an expression.
    """
    if expr is None:
        return
    bound = expr._resources()
    if not bound and resources is None:
        raise ValueError('no resources provided to compute %s' % name)
    if bound and resources:
        raise ValueError(
            'explicit and implicit resources provided to compute %s' % name,
        )

def _check_datetime_field(name, measure):
    """Check that a field is a datetime inside some measure.

    Parameters
    ----------
    name : str
        The name of the field to check.
    measure : Record
        The record to check the field of.

    Raises
    ------
    TypeError
        If the field is not a datetime inside ``measure``.
    """
    if not isinstance(measure[name], (Date, DateTime)):
        raise TypeError(
            "'{name}' field must be a '{dt}', not: '{dshape}'".format(
                name=name,
                dt=DateTime(),
                dshape=measure[name],
            ),
        )

class NoMetaDataWarning(UserWarning):
    """Warning used to signal that no deltas or checkpoints could be found and
    none were provided.

    Parameters
    ----------
    expr : Expr
        The expression that was searched.
    field : {'deltas',  'checkpoints'}
        The field that was looked up.
    """
    def __init__(self, expr, field):
        self._expr = expr
        self._field = field

    def __str__(self):
        return 'No %s could be inferred from expr: %s' % (
            self._field,
            self._expr,
        )

no_metadata_rules = frozenset({'warn', 'raise', 'ignore'})

def _get_metadata(field, expr, metadata_expr, no_metadata_rule):
    """Find the correct metadata expression for the expression.

    Parameters
    ----------
    field : {'deltas', 'checkpoints'}
        The kind of metadata expr to lookup.
    expr : Expr
        The baseline expression.
    metadata_expr : Expr, 'auto', or None
        The metadata argument. If this is 'auto', then the metadata table will
        be searched for by walking up the expression tree. If this cannot be
        reflected, then an action will be taken based on the
        ``no_metadata_rule``.
    no_metadata_rule : {'warn', 'raise', 'ignore'}
        How to handle the case where the metadata_expr='auto' but no expr
        could be found.

    Returns
    -------
    metadata : Expr or None
        The deltas or metadata table to use.
    """
    if isinstance(metadata_expr, bz.Expr) or metadata_expr is None:
        return metadata_expr

    try:
        return expr._child['_'.join(((expr._name or ''), field))]
    except (ValueError, AttributeError):
        if no_metadata_rule == 'raise':
            raise ValueError(
                "no %s table could be reflected for %s" % (field, expr)
            )
        elif no_metadata_rule == 'warn':
            warnings.warn(NoMetaDataWarning(expr, field), stacklevel=4)
    return None

def _ad_as_ts(expr):
    """Duplicate the asof_date column as the timestamp column.

    Parameters
    ----------
    expr : Expr or None
        The expression to change the columns of.

    Returns
    -------
    transformed : Expr or None
        The transformed expression or None if ``expr`` is None.
    """
    return (
        None
        if expr is None else
        bz.transform(expr, **{TS_FIELD_NAME: expr[AD_FIELD_NAME]})
    )

def _ensure_timestamp_field(dataset_expr, deltas, checkpoints):
    """Verify that the baseline and deltas expressions have a timestamp field.

    If there is not a ``TS_FIELD_NAME`` on either of the expressions, it will
    be copied from the ``AD_FIELD_NAME``. If one is provided, then we will
    verify that it is the correct dshape.

    Parameters
    ----------
    dataset_expr : Expr
        The baseline expression.
    deltas : Expr or None
        The deltas expression if any was provided.
    checkpoints : Expr or None
        The checkpoints expression if any was provided.

    Returns
    -------
    dataset_expr, deltas : Expr
        The new baseline and deltas expressions to use.
    """
    measure = dataset_expr.dshape.measure
    if TS_FIELD_NAME not in measure.names:
        dataset_expr = bz.transform(
            dataset_expr,
            **{TS_FIELD_NAME: dataset_expr[AD_FIELD_NAME]}
        )
        deltas = _ad_as_ts(deltas)
        checkpoints = _ad_as_ts(checkpoints)
    else:
        _check_datetime_field(TS_FIELD_NAME, measure)

    return dataset_expr, deltas, checkpoints

@expect_element(
    no_deltas_rule=no_metadata_rules,
    no_checkpoints_rule=no_metadata_rules,
)
def from_blaze(expr,
               deltas='auto',
               checkpoints='auto',
               loader=None,
               resources=None,
               odo_kwargs=None,
               missing_values=None,
               no_deltas_rule='warn',
               no_checkpoints_rule='warn'):
    """Create a Pipeline API object from a blaze expression.

    Parameters
    ----------
    expr : Expr
        The blaze expression to use.
    deltas : Expr, 'auto' or None, optional
        The expression to use for the point in time adjustments.
        If the string 'auto' is passed, a deltas expr will be looked up
        by stepping up the expression tree and looking for another field
        with the name of ``expr._name`` + '_deltas'. If None is passed, no
        deltas will be used.
    deltas : Expr, 'auto' or None, optional
        The expression to use for the forward fill checkpoints.
        If the string 'auto' is passed, a checkpoints expr will be looked up
        by stepping up the expression tree and looking for another field
        with the name of ``expr._name`` + '_checkpoints'. If None is passed,
        no checkpoints will be used.
    loader : BlazeLoader, optional
        The blaze loader to attach this pipeline dataset to. If None is passed,
        the global blaze loader is used.
    resources : dict or any, optional
        The data to execute the blaze expressions against. This is used as the
        scope for ``bz.compute``.
    odo_kwargs : dict, optional
        The keyword arguments to pass to odo when evaluating the expressions.
    missing_values : dict[str -> any], optional
        A dict mapping column names to missing values for those columns.
        Missing values are required for integral columns.
    no_deltas_rule : {'warn', 'raise', 'ignore'}, optional
        What should happen if ``deltas='auto'`` but no deltas can be found.
        'warn' says to raise a warning but continue.
        'raise' says to raise an exception if no deltas can be found.
        'ignore' says take no action and proceed with no deltas.
    no_checkpoints_rule : {'warn', 'raise', 'ignore'}, optional
        What should happen if ``checkpoints='auto'`` but no checkpoints can be
        found. 'warn' says to raise a warning but continue.
        'raise' says to raise an exception if no deltas can be found.
        'ignore' says take no action and proceed with no deltas.

    Returns
    -------
    pipeline_api_obj : DataSet or BoundColumn
        Either a new dataset or bound column based on the shape of the expr
        passed in. If a table shaped expression is passed, this will return
        a ``DataSet`` that represents the whole table. If an array-like shape
        is passed, a ``BoundColumn`` on the dataset that would be constructed
        from passing the parent is returned.
    """
    if 'auto' in {deltas, checkpoints}:
        invalid_nodes = tuple(filter(is_invalid_deltas_node, expr._subterms()))
        if invalid_nodes:
            raise TypeError(
                'expression with auto %s may only contain (%s) nodes,'
                " found: %s" % (
                    ' or '.join(
                        ['deltas'] if deltas is not None else [] +
                        ['checkpoints'] if checkpoints is not None else [],
                    ),
                    ', '.join(map(get__name__, valid_deltas_node_types)),
                    ', '.join(
                        set(map(compose(get__name__, type), invalid_nodes)),
                    ),
                ),
            )
    deltas = _get_metadata(
        'deltas',
        expr,
        deltas,
        no_deltas_rule,
    )
    checkpoints = _get_metadata(
        'checkpoints',
        expr,
        checkpoints,
        no_checkpoints_rule,
    )

    # Check if this is a single column out of a dataset.
    if bz.ndim(expr) != 1:
        raise TypeError(
            'expression was not tabular or array-like,'
            ' %s dimensions: %d' % (
                'too many' if bz.ndim(expr) > 1 else 'not enough',
                bz.ndim(expr),
            ),
        )

    single_column = None
    if isscalar(expr.dshape.measure):
        # This is a single column. Record which column we are to return
        # but create the entire dataset.
        single_column = rename = expr._name
        field_hit = False
        if not isinstance(expr, traversable_nodes):
            raise TypeError(
                "expression '%s' was array-like but not a simple field of"
                " some larger table" % str(expr),
            )
        while isinstance(expr, traversable_nodes):
            if isinstance(expr, bz.expr.Field):
                if not field_hit:
                    field_hit = True
                else:
                    break
            rename = expr._name
            expr = expr._child
        dataset_expr = expr.relabel({rename: single_column})
    else:
        dataset_expr = expr

    measure = dataset_expr.dshape.measure
    if not isrecord(measure) or AD_FIELD_NAME not in measure.names:
        raise TypeError(
            "The dataset must be a collection of records with at least an"
            " '{ad}' field. Fields provided: '{fields}'\nhint: maybe you need"
            " to use `relabel` to change your field names".format(
                ad=AD_FIELD_NAME,
                fields=measure,
            ),
        )
    _check_datetime_field(AD_FIELD_NAME, measure)
    dataset_expr, deltas, checkpoints = _ensure_timestamp_field(
        dataset_expr,
        deltas,
        checkpoints,
    )

    if deltas is not None and (sorted(deltas.dshape.measure.fields) !=
                               sorted(measure.fields)):
        raise TypeError(
            'baseline measure != deltas measure:\n%s != %s' % (
                measure,
                deltas.dshape.measure,
            ),
        )
    if (checkpoints is not None and
        (sorted(checkpoints.dshape.measure.fields) !=
         sorted(measure.fields))):
        raise TypeError(
            'baseline measure != checkpoints measure:\n%s != %s' % (
                measure,
                checkpoints.dshape.measure,
            ),
        )

    # Ensure that we have a data resource to execute the query against.
    _check_resources('expr', dataset_expr, resources)
    _check_resources('deltas', deltas, resources)
    _check_resources('checkpoints', checkpoints, resources)

    # Create or retrieve the Pipeline API dataset.
    if missing_values is None:
        missing_values = {}
    ds = new_dataset(dataset_expr, deltas, frozenset(missing_values.items()))

    # Register our new dataset with the loader.
    (loader if loader is not None else global_loader)[ds] = ExprData(
        bind_expression_to_resources(dataset_expr, resources),
        bind_expression_to_resources(deltas, resources)
        if deltas is not None else
        None,
        bind_expression_to_resources(checkpoints, resources)
        if checkpoints is not None else
        None,
        odo_kwargs=odo_kwargs,
    )
    if single_column is not None:
        # We were passed a single column, extract and return it.
        return getattr(ds, single_column)
    return ds

getdataset = op.attrgetter('dataset')
getname = op.attrgetter('name')

def overwrite_novel_deltas(baseline, deltas, dates):
    """overwrite any deltas into the baseline set that would have changed our
    most recently known value.

    Parameters
    ----------
    baseline : pd.DataFrame
        The first known values.
    deltas : pd.DataFrame
        Overwrites to the baseline data.
    dates : pd.DatetimeIndex
        The dates requested by the loader.

    Returns
    -------
    non_novel_deltas : pd.DataFrame
        The deltas that do not represent a baseline value.
    """
    get_indexes = dates.searchsorted
    novel_idx = (
        get_indexes(deltas[TS_FIELD_NAME].values, 'right') -
        get_indexes(deltas[AD_FIELD_NAME].values, 'left')
    ) <= 1
    novel_deltas = deltas.loc[novel_idx]
    non_novel_deltas = deltas.loc[~novel_idx]
    cat = pd.concat(
        (baseline, novel_deltas),
        ignore_index=True,
        copy=False,
    )
    sort_values(cat, TS_FIELD_NAME, inplace=True)
    return cat, non_novel_deltas

def overwrite_from_dates(asof, dense_dates, sparse_dates, asset_idx, value):
    """Construct a `Float64Overwrite` with the correct
    start and end date based on the asof date of the delta,
    the dense_dates, and the dense_dates.

    Parameters
    ----------
    asof : datetime
        The asof date of the delta.
    dense_dates : pd.DatetimeIndex
        The dates requested by the loader.
    sparse_dates : pd.DatetimeIndex
        The dates that appeared in the dataset.
    asset_idx : tuple of int
        The index of the asset in the block. If this is a tuple, then this
        is treated as the first and last index to use.
    value : np.float64
        The value to overwrite with.

    Returns
    -------
    overwrite : Float64Overwrite
        The overwrite that will apply the new value to the data.

    Notes
    -----
    This is forward-filling all dense dates that are between the asof_date date
    and the next sparse date after the asof_date.

    For example:
    let ``asof = pd.Timestamp('2014-01-02')``,
        ``dense_dates = pd.date_range('2014-01-01', '2014-01-05')``
        ``sparse_dates = pd.to_datetime(['2014-01', '2014-02', '2014-04'])``

    Then the overwrite will apply to indexes: 1, 2, 3, 4
    """
    if asof is pd.NaT:
        # Not an actual delta.
        # This happens due to the groupby we do on the deltas.
        return

    first_row = dense_dates.searchsorted(asof)
    next_idx = sparse_dates.searchsorted(asof.asm8, 'right')
    if next_idx == len(sparse_dates):
        # There is no next date in the sparse, this overwrite should apply
        # through the end of the dense dates.
        last_row = len(dense_dates) - 1
    else:
        # There is a next date in sparse dates. This means that the overwrite
        # should only apply until the index of this date in the dense dates.
        last_row = dense_dates.searchsorted(sparse_dates[next_idx]) - 1

    if first_row > last_row:
        return

    first, last = asset_idx
    yield Float64Overwrite(first_row, last_row, first, last, value)

def adjustments_from_deltas_no_sids(dense_dates,
                                    sparse_dates,
                                    column_idx,
                                    column_name,
                                    asset_idx,
                                    deltas):
    """Collect all the adjustments that occur in a dataset that does not
    have a sid column.

    Parameters
    ----------
    dense_dates : pd.DatetimeIndex
        The dates requested by the loader.
    sparse_dates : pd.DatetimeIndex
        The dates that were in the raw data.
    column_idx : int
        The index of the column in the dataset.
    column_name : str
        The name of the column to compute deltas for.
    asset_idx : pd.Series[int -> int]
        The mapping of sids to their index in the output.
    deltas : pd.DataFrame
        The overwrites that should be applied to the dataset.

    Returns
    -------
    adjustments : dict[idx -> Float64Overwrite]
        The adjustments dictionary to feed to the adjusted array.
    """
    ad_series = deltas[AD_FIELD_NAME]
    idx = 0, 0
    return {
        dense_dates.get_loc(kd): overwrite_from_dates(
            ad_series.loc[kd],
            dense_dates,
            sparse_dates,
            idx,
            v,
        ) for kd, v in deltas[column_name].iteritems()
    }

def adjustments_from_deltas_with_sids(dense_dates,
                                      sparse_dates,
                                      column_idx,
                                      column_name,
                                      asset_idx,
                                      deltas):
    """Collect all the adjustments that occur in a dataset that has a sid
    column.

    Parameters
    ----------
    dates : pd.DatetimeIndex
        The dates requested by the loader.
    dense_dates : pd.DatetimeIndex
        The dates that were in the raw data.
    column_idx : int
        The index of the column in the dataset.
    column_name : str
        The name of the column to compute deltas for.
    asset_idx : pd.Series[int -> int]
        The mapping of sids to their index in the output.
    deltas : pd.DataFrame
        The overwrites that should be applied to the dataset.

    Returns
    -------
    adjustments : dict[idx -> Float64Overwrite]
        The adjustments dictionary to feed to the adjusted array.
    """
    ad_series = deltas[AD_FIELD_NAME]
    adjustments = defaultdict(list)
    for sid, per_sid in deltas[column_name].iteritems():
        idx = asset_idx[sid]
        for kd, v in per_sid.iteritems():
            adjustments[dense_dates.searchsorted(kd)].extend(
                overwrite_from_dates(
                    ad_series.loc[kd, sid],
                    dense_dates,
                    sparse_dates,
                    (idx, idx),
                    v,
                ),
            )
    return dict(adjustments)  # no subclasses of dict

class BlazeLoader(dict):
    """A PipelineLoader for datasets constructed with ``from_blaze``.

    Parameters
    ----------
    dsmap : mapping, optional
        An initial mapping of datasets to ``ExprData`` objects.
        NOTE: Further mutations to this map will not be reflected by this
        object.
    data_query_time : time, optional
        The time to use for the data query cutoff.
    data_query_tz : tzinfo or str, optional
        The timezeone to use for the data query cutoff.
    pool : Pool, optional
        The pool to use to run blaze queries concurrently. This object must
        support ``imap_unordered``, ``apply`` and ``apply_async`` methods.

    Attributes
    ----------
    pool : Pool
        The pool to use to run blaze queries concurrently. This object must
        support ``imap_unordered``, ``apply`` and ``apply_async`` methods.
        It is possible to change the pool after the loader has been
        constructed. This allows us to set a new pool for the ``global_loader``
        like: ``global_loader.pool = multiprocessing.Pool(4)``.

    See Also
    --------
    :class:`zipline.utils.pool.SequentialPool`
    :class:`multiprocessing.Pool`
    """
    @preprocess(data_query_tz=optionally(ensure_timezone))
    def __init__(self,
                 dsmap=None,
                 data_query_time=None,
                 data_query_tz=None,
                 pool=SequentialPool()):
        self.update(dsmap or {})
        check_data_query_args(data_query_time, data_query_tz)
        self._data_query_time = data_query_time
        self._data_query_tz = data_query_tz

        # explicitly public
        self.pool = pool

    @classmethod
    @memoize(cache=WeakKeyDictionary())
    def global_instance(cls):
        return cls()

    def __hash__(self):
        return id(self)

    def __call__(self, column):
        if column.dataset in self:
            return self
        raise KeyError(column)

    def __repr__(self):
        return '<%s: %s>' % (
            type(self).__name__,
            super(BlazeLoader, self).__repr__(),
        )

    def load_adjusted_array(self, columns, dates, assets, mask):
        return merge(
            self.pool.imap_unordered(
                partial(self._load_dataset, dates, assets, mask),
                itervalues(groupby(getdataset, columns)),
            ),
        )

    def _load_dataset(self, dates, assets, mask, columns):
        try:
            (dataset,) = set(map(getdataset, columns))
        except ValueError:
            raise AssertionError('all columns must come from the same dataset')

        expr, deltas, checkpoints, odo_kwargs = self[dataset]
        have_sids = (dataset.ndim == 2)
        asset_idx = pd.Series(index=assets, data=np.arange(len(assets)))
        assets = list(map(int, assets))  # coerce from numpy.int64
        added_query_fields = [AD_FIELD_NAME, TS_FIELD_NAME] + (
            [SID_FIELD_NAME] if have_sids else []
        )
        colnames = added_query_fields + list(map(getname, columns))

        data_query_time = self._data_query_time
        data_query_tz = self._data_query_tz
        lower_dt, upper_dt = normalize_data_query_bounds(
            dates[0],
            dates[-1],
            data_query_time,
            data_query_tz,
        )

        def collect_expr(e, lower):
            """Materialize the expression as a dataframe.

            Parameters
            ----------
            e : Expr
                The baseline or deltas expression.
            lower : datetime
                The lower time bound to query.

            Returns
            -------
            result : pd.DataFrame
                The resulting dataframe.

            Notes
            -----
            This can return more data than needed. The in memory reindex will
            handle this.
            """
            predicate = e[TS_FIELD_NAME] <= upper_dt
            if lower is not None:
                predicate &= e[TS_FIELD_NAME] >= lower

            return odo(e[predicate][colnames], pd.DataFrame, **odo_kwargs)

        if checkpoints is not None:
            ts = checkpoints[TS_FIELD_NAME]
            checkpoints_ts = odo(ts[ts <= lower_dt].max(), pd.Timestamp)
            if pd.isnull(checkpoints_ts):
                materialized_checkpoints = pd.DataFrame(columns=colnames)
                lower = None
            else:
                materialized_checkpoints = odo(
                    checkpoints[ts == checkpoints_ts][colnames],
                    pd.DataFrame,
                    **odo_kwargs
                )
                lower = checkpoints_ts
        else:
            materialized_checkpoints = pd.DataFrame(columns=colnames)
            lower = None

        materialized_expr = self.pool.apply_async(collect_expr, (expr, lower))
        materialized_deltas = (
            self.pool.apply(collect_expr, (deltas, lower))
            if deltas is not None else
            pd.DataFrame(columns=colnames)
        )

        if materialized_checkpoints is not None:
            materialized_expr = pd.concat(
                (
                    materialized_checkpoints,
                    materialized_expr.get(),
                ),
                ignore_index=True,
                copy=False,
            )

        # It's not guaranteed that assets returned by the engine will contain
        # all sids from the deltas table; filter out such mismatches here.
        if not materialized_deltas.empty and have_sids:
            materialized_deltas = materialized_deltas[
                materialized_deltas[SID_FIELD_NAME].isin(assets)
            ]

        if data_query_time is not None:
            for m in (materialized_expr, materialized_deltas):
                m.loc[:, TS_FIELD_NAME] = m.loc[
                    :, TS_FIELD_NAME
                ].astype('datetime64[ns]')

                normalize_timestamp_to_query_time(
                    m,
                    data_query_time,
                    data_query_tz,
                    inplace=True,
                    ts_field=TS_FIELD_NAME,
                )

        # Inline the deltas that changed our most recently known value.
        # Also, we reindex by the dates to create a dense representation of
        # the data.
        sparse_output, non_novel_deltas = overwrite_novel_deltas(
            materialized_expr,
            materialized_deltas,
            dates,
        )
        sparse_output.drop(AD_FIELD_NAME, axis=1, inplace=True)

        def last_in_date_group(df, reindex, have_sids=have_sids):
            idx = dates[dates.searchsorted(
                df[TS_FIELD_NAME].values.astype('datetime64[D]')
            )]
            if have_sids:
                idx = [idx, SID_FIELD_NAME]

            last_in_group = df.drop(TS_FIELD_NAME, axis=1).groupby(
                idx,
                sort=False,
            ).last()

            if have_sids:
                last_in_group = last_in_group.unstack()

            if reindex:
                if have_sids:
                    cols = last_in_group.columns
                    last_in_group = last_in_group.reindex(
                        index=dates,
                        columns=pd.MultiIndex.from_product(
                            (cols.levels[0], assets),
                            names=cols.names,
                        ),
                    )
                else:
                    last_in_group = last_in_group.reindex(dates)

            return last_in_group

        sparse_deltas = last_in_date_group(non_novel_deltas, reindex=False)
        dense_output = last_in_date_group(sparse_output, reindex=True)
        dense_output.ffill(inplace=True)

        # Fill in missing values specified by each column. This is made
        # significantly more complex by the fact that we need to work around
        # two pandas issues:

        # 1) When we have sids, if there are no records for a given sid for any
        #    dates, pandas will generate a column full of NaNs for that sid.
        #    This means that some of the columns in `dense_output` are now
        #    float instead of the intended dtype, so we have to coerce back to
        #    our expected type and convert NaNs into the desired missing value.

        # 2) DataFrame.ffill assumes that receiving None as a fill-value means
        #    that no value was passed.  Consequently, there's no way to tell
        #    pandas to replace NaNs in an object column with None using fillna,
        #    so we have to roll our own instead using df.where.
        for column in columns:
            # Special logic for strings since `fillna` doesn't work if the
            # missing value is `None`.
            if column.dtype == categorical_dtype:
                dense_output[column.name] = dense_output[
                    column.name
                ].where(pd.notnull(dense_output[column.name]),
                        column.missing_value)
            else:
                # We need to execute `fillna` before `astype` in case the
                # column contains NaNs and needs to be cast to bool or int.
                # This is so that the NaNs are replaced first, since pandas
                # can't convert NaNs for those types.
                dense_output[column.name] = dense_output[
                    column.name
                ].fillna(column.missing_value).astype(column.dtype)

        if have_sids:
            adjustments_from_deltas = adjustments_from_deltas_with_sids
            column_view = identity
        else:
            # If we do not have sids, use the column view to make a single
            # column vector which is unassociated with any assets.
            column_view = op.itemgetter(np.s_[:, np.newaxis])

            adjustments_from_deltas = adjustments_from_deltas_no_sids
            mask = np.full(
                shape=(len(mask), 1), fill_value=True, dtype=bool_dtype,
            )

        return {
            column: AdjustedArray(
                column_view(
                    dense_output[column.name].values.astype(column.dtype),
                ),
                mask,
                adjustments_from_deltas(
                    dates,
                    sparse_output[TS_FIELD_NAME].values,
                    column_idx,
                    column.name,
                    asset_idx,
                    sparse_deltas,
                ),
                column.missing_value,
            )
            for column_idx, column in enumerate(columns)
        }

global_loader = BlazeLoader.global_instance()

def bind_expression_to_resources(expr, resources):
    """
    Bind a Blaze expression to resources.

    Parameters
    ----------
    expr : bz.Expr
        The expression to which we want to bind resources.
    resources : dict[bz.Symbol -> any]
        Mapping from the loadable terms of ``expr`` to actual data resources.

    Returns
    -------
    bound_expr : bz.Expr
        ``expr`` with bound resources.
    """
    # bind the resources into the expression
    if resources is None:
        resources = {}

    # _subs stands for substitute.  It's not actually private, blaze just
    # prefixes symbol-manipulation methods with underscores to prevent
    # collisions with data column names.
    return expr._subs({
        k: bz.data(v, dshape=k.dshape) for k, v in iteritems(resources)
    })

def ffill_query_in_range(expr,
                         lower,
                         upper,
                         odo_kwargs=None,
                         ts_field=TS_FIELD_NAME,
                         sid_field=SID_FIELD_NAME):
    """Query a blaze expression in a given time range properly forward filling
    from values that fall before the lower date.

    Parameters
    ----------
    expr : Expr
        Bound blaze expression.
    lower : datetime
        The lower date to query for.
    upper : datetime
        The upper date to query for.
    odo_kwargs : dict, optional
        The extra keyword arguments to pass to ``odo``.
    ts_field : str, optional
        The name of the timestamp field in the given blaze expression.
    sid_field : str, optional
        The name of the sid field in the given blaze expression.

    Returns
    -------
    raw : pd.DataFrame
        A strict dataframe for the data in the given date range. This may
        start before the requested start date if a value is needed to ffill.
    """
    odo_kwargs = odo_kwargs or {}
    filtered = expr[expr[ts_field] <= lower]
    computed_lower = odo(
        bz.by(
            filtered[sid_field],
            timestamp=filtered[ts_field].max(),
        ).timestamp.min(),
        pd.Timestamp,
        **odo_kwargs
    )
    if pd.isnull(computed_lower):
        # If there is no lower date, just query for data in the date
        # range. It must all be null anyways.
        computed_lower = lower

    raw = odo(
        expr[
            (expr[ts_field] >= computed_lower) &
            (expr[ts_field] <= upper)
        ],
        pd.DataFrame,
        **odo_kwargs
    )
    raw.loc[:, ts_field] = raw.loc[:, ts_field].astype('datetime64[ns]')
    return raw


text:
Q: different frame for each cell of UICollectionView what I want to do is,
I am using UICollectionView to show my stuff.
What I want is, the stuff that I want to show is coming from server,
It may be in single line or some may be multiple.
I wanted to show the cell frame of UICollectionView differently.
Means If data is big then bigger frame of UICollectionViewcell
and if data is less then smaller frame of UICollectionViewcell.
I tried a lot,
but I am succeed only in having fixed size of frame for UICollectionViewcell
How to achieve different frame for each cell according to need.
Is it possible....?
@note :- I am not using any xib.
All is done programatically.

A: It seem like you are getting UICollectionView wrongly.
Let me say in short You are not suppose to set frame but you need to implement your own UICollectionViewLayout.
By default UICollectionView use UICollectionViewFlowLayout. Which add cell one by one.
But UICollectionView is very power full UI component than that. To open its true power you have to understand  UICollectionViewLayout.
Here are some good example of that :

*

*RFQUILTLAYOUT

*CHTCollectionViewWaterfallLayout

*CCHexagonFlowLayout

*MSCollectionViewCalendarLayout
If you want to learn how to create your ow than have a look at following link:

*

*Creating Custom Layouts

*Custom Layouts: A Worked Example

*Implementing UICollectionViewLayout

*UICollectionView custom layout tutorial

*A Springboard-Like Layout With the UICollectionView Class
If you need to know overview of how UICollectionView work than read : NSHipster:UICollectionView
One in depth look in Custom Collection View Layouts : objc : Custom Collection View Layouts
I hope I get u correctly. And this will lead you to right direction.


text:
Home News Largest section of Wekiva Parkway to open Monday
Largest section of Wekiva Parkway to open Monday
SEMINOLE COUNTY, Fla. – Traveling between Sanford and Mt. Dora is about to get easier.
As soon as Monday, the Florida Department of Transportation is set to open its longest section of the Wekiva Parkway. The section stretches more than six miles on new, elevated roadway. Drivers can enter or exit State Road 429 near Camp Challenge Road in Lake County and via ramps west of Longwood Markham Road in Seminole County.
[TRENDING: Federal Reserve may push labor-freeze to fight inflation, experts say | Man who killed 2 Kissimmee police officers sentenced to death | Become a News 6 Insider (it's free!)]
This stretch also includes several wildlife crossing bridges to help keep drivers and animals safe in the Wekiva River basin.
Realtor Dan Goodman lives nearby in Heathrow and sells homes in the area. He said traveling around during the construction has been frustrating.
"It's been so congested, crowded. It's been really slow. It's been a set back," Goodman said.
Goodman said he's hopeful that when the new toll road opens it will relieve traffic on State Road 46.
"It's been a little annoying, so we're excited to see the change and finally get these roadways for us," he said.
The new stretch comes more than six years after FDOT opened the first section of the $1.6 billion project. The 25-mile toll road will complete a beltway around the western half of the Orlando metro area.
It is also the first in Central Florida to use all-electronic tolls.
Goodman said as the area is growing, the parkway will help connect more communities and make traveling more convenient.
"It's going to be easier to have a broader area of living. If someone wants to commute, now it won't be such a problem and only one solution, like I-4," Goodman said. "This will open up a lot more traffic opportunities."
FDOT said the entire Wekiva Parkway is set to open in 2023.
Copyright 2022 by WKMG ClickOrlando – All rights reserved.
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'This is the name of a highly toxic group of chemicals found all over the world—both in nature, and also as byproducts of manufacturing—basically, everywhere!
The active ingredient in 'Agent Orange' known as Dioxin is a class of industrial pollutant that can accumulate in the fatty tissues of animals and humans.
90% of our exposure to dioxins is from eating animal products, which is why the EWG recommends limiting consumption of animal products in order to reduce intake.
Dioxins can increase stress hormone levels, hurt your immune system, and they can cause liver damage and skin lesions.


Senior Business Development Manager, Software and Technology Sales, London, £60,000 Basic, £120K OTE. Established for 15 years, this organisation provide software and consultancy solutions to many of the world's largest brands, across a range of vertical sectors. Through personalised and bespoke programmes, they operate in three ownership classes, Large Global organisations, mid-tier FTSE 250 companies and High-growth Aim and PE-backed organisations.
This is a strategic business development role, based from a prestigious City of London HQ, where you will be tasked in targeting a very specific and finite pool of potential clients within a defined niche. The products and services offered increase profitability and efficiency while decreasing risk and you are backed by significant case studies and referral points from within the niche.
* To succeed you will be a consummate corporate level business developer with an eye for commercial opportunities and possess great relationship building skills and a naturally persuasive style.
* You will be an open, credible and supportive team player that has a proven track record in selling either complex software solutions or management consultancy.
* You will be of graduate calibre, credible and possess strong negotiation skills, ideally gained through a structured sales training programme.
* You will be used to winning complex business and be an ambitious team player that is highly success oriented and self-motivated.
* In addition, you will have strong organisation and planning skills and be an excellent corporate presenter.
Supported by a full-time researcher who will conduct detailed pre-sales activity on your behalf, your role will be to build a keen awareness of the challenges leaders face and to network at board-levels to generate substantial long-term relationships.
This role would suit someone from a professional service, sales background, a senior recruitment sales environment or other sophisticated, 'value add' consultancy sales backgrounds.
In return the organisation offers high-level investment in the successful candidate, uncapped earning potential within a supportive and collaborative business culture.


text:
Home news new-transparent-soil-technology-help-fight-nematodes-and-other-soil-pests
New transparent soil technology to help fight nematodes and other soil pests
"Our system provides an extremely useful and rapid tool to help in the search for new, more environmentally acceptable plant protection products"
Scientists from the James Hutton Institute have developed a screening system which uses transparent soil technology to help fight nematodes - microscopic worms that are harmful to plants, damaging about 10% of susceptible crops and causing hundreds of billions of pounds of losses worldwide in a year.
Building on previous transparent soil research, the team reproduced soil conditions in the laboratory, in a drive to examine the responses of various nematode species to chemical compounds and speed up the development of next-generation treatments that could be used to protect plants against nematodes and other pests and diseases.
Study leader Dr Lionel Dupuy, of the James Hutton Institute's Ecological Sciences group in Dundee, said: "This is just the beginning of the development of a new generation of fast and simple laboratory assays for testing the efficacy of soil compounds.
"We have demonstrated that our techniques can detect microscopic biological activity in soil samples, and are now working very hard to extend our techniques to the analysis of other soil-inhabiting organisms."
Dr Stuart MacFarlane, project leader, commented: "We need to find new ways to control harmful nematodes that feed on crop plants in the field.
"Our system provides an extremely useful and rapid tool to help in the search for new, more environmentally acceptable plant protection products."
The project is funded by the by the Biotechnology and Biological Sciences Research Council (BBSRC) Horticulture and Potato Initiative and involved the universities of Leeds and York.
The paper "New live screening of plant-nematode interactions in the rhizosphere" is available from Nature's Scientific Reports journal.
More information from:
Bernardo Rodriguez-Salcedo, Media Manager, Tel: +44 (0)1224 395089 (direct line), +44 (0)344 928 5428 (switchboard) or +44 (0)7791 193918 (mobile).
Cell and Molecular Sciences (Research Page)
Lionel Dupuy (Member of Staff)
Stuart MacFarlane (Member of Staff)
Roy Neilson (Member of Staff)
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postGame: Villanova 88 Louisville 74
By jhulax22 Jan 12, 2011, 9:20pm EST
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While Corey Stokes had another great game, it was Mouphtaou Yarou who stood out in the 88-74 win over Louisville.
Game ended approximately 8:59 PM eastern time. Villanova Wildcats defeat Louisville Cardinals 88-74. Some notes on the game are below.
-For the second straight game, Villanova let their opponent race out to an early lead. Louisville put up the first 7 points of the game on the heels of 3 Villanova turnovers, but the Wildcats quickly responded with a 7 spot of their own.
-Nova tried to use their 1-2-2 press against the Cardinals, but the visitors broke it easily most times, which left Louisville shooters wide open behind the three point arc. As a result, Lousville extended their lead to 7 once again.
-Villanova erased the deficit by the ten minute mark, and the frenetic pace continued throughout the first half, with each team pushing the envelope whenever possible. For each shot that Chris Smith, Rakeem Buckles, and Peyton Siva drained from deep, there was an answer from Corey Stokes or Corey Fisher, who were both in double figures by halftime.
-Halftime score: Villanova 42 Louisville 41
More on the game after the jump...
-The halftime break was definitely needed for both teams, because the pace certainly did not slow down to open the second half. Louisville knocked down some more 3 pointers, but they did miss a couple of mid-range jumpers, while Villanova was not missing from anywhere, especially Corey Stokes. With thirteen minutes remaining in the contest, the Wildcats had amassed a 59-50 advantage.
-A mini spurt by the road squad cut the deficit to 5, but some nice interior play by Mouphtaou Yarou coupled with the driving ability of Fisher and Maalik Wayns aided in re-establishing a comfortable lead of 13 with less than 10 mins. left.
-Just like they did on Sunday against Cincinnati, Villanova had another chance to bury a team in the second half. Three days ago, they let Cincy hang around, but this time, the good guys put the nail in the coffin. Villanova finally played a little defense on the Cardinals shooters, which translated into some easy transition opportunities for Fisher and Wayns. In addition, Yarou could not be stopped down low, and he demanded the ball late in the game, usually a rarity for Villanova big men in the past. In the end, the Cats went on to notch their 15th victory of the year.
-In terms of statistics, Corey Stokes had a team-high 23 points, including 9-9 from the charity stripe. Mouphtaou Yarou put up a double-double with 18 points and 12 boards. The true guards had a solid night as well, as Fisher scored 17 to go along with 8 rebounds, and Wayns had 15 points and 8 dimes. Villanova doubled up Louisville on the glass. For the Cardinals, the trio of Preston Knowles, Peyton Siva, and Chris Smith had stellar performances. Knowles drilled 6 threes, which contributed to his game-high 24 points, while Siva had 14 points and 8 assists, and Smith scored 18.
-General obersvations:
The first half was one of the worse defensive halves of basketball I've ever seen from a Villanova team, and that falls partly on the shoulders of Jay Wright. As mentioned earlier, the Cards seemed to blow right through the Nova press, which had the Wildcat players scrambling to find open shooters. More often than not, Louisville had a wide open player on the outside, who eventually sank a three ball. Jay should've abandoned the press to let Villanova drop back and match up against their guys.
Two players on opposite sides of the spectrum for Villanova were Antonio Pena and Mouphtaou Yarou. To put it nicely, Pena played a horrific game, with numerous turnovers and defensive lapses. On the other hand, Yarou had his best game of the year, truly dominating the paint at both ends of the floor.
I know I've said this before, but if Kemba Walker ever comes back down to Earth, Corey Stokes would be the leading candidate for Big East Player of the Year.
There's no doubt that this is a big win. Lousville is going to win its fair share of games this year if they can get better on the defensive end. With very difficult road games coming up next week in Storrs, CT and Syracuse, NY, it was important to stay undefeated in conference tonight. Maryland is no cake walk on Saturday either.