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Create peaceful environment for sustainable devt – MMDCEs urged
1st Dec 2021 | Source: Ghanaian Times
Participants after the workshop
The Western Regional Chairman of the Peace Council, Bishop Emmanuel Botwey, has charged Metropolitan, Municipal and District Chief Executives (MMDCEs) to create peaceful environment towards sustainable socioeconomic development.
"Though the region is relatively peaceful compared to other regions, pockets of violence, mostly from chieftaincy disputes, land litigation and resource conflicts occur.
The phenomenon did not only affect peaceful co-existence but total development in the affected communities," he cautioned.
Bishop Botwey noted that as political heads, MMDCEs ought to ensure there was peace, unity, harmony and cohesion in their communities to engender the needed progress, growth and development.
He was addressing a two-day capacity building workshop on conflict management and reporting for MMDCEs, with support and assistance from the Danish International Development Agency (DANIDA), among other issues, sought to build capacity of MMDCEs to manage and report on conflicts.
It was also to equip the participants with knowledge and skills to analyse conflicts, identify peace connectors, early warning signs and initiate responses to them and
were taken through topics such as 'Concepts of Conflicts and Peace and their Implications for Development', 'Tools for Conflict Prevention, Management, Resolution and Transformation', 'Conflicts, Early Warning and Response Mechanisms', 'Managing and Resolving Conflicts in Metropolitan, Municipal and District Assemblies (MMDAs)', 'Vigilantism, and Related Offences', and 'Integrating Peace in Development and Planning'.
Bishop Botwey underscored the importance of training, Saying that, equipping MMDCEs with conflict resolution tools, knowledge and skills was necessary to ensure effective and efficient management and resolution of conflicts within their jurisdictions.
"It will also help you create peaceful environment for sustainable development in partnership with Assemblies to build capacities of members of justice and security committees to assist and support you in your work, and the Peace Council will avail itself to assist and support Assemblies in dealing with conflicts that arise," he said.
Dr Patrick Osei-Kuffour, a Research Fellow at the Department of Peace Studies, School for Development Studies, University of Cape Coast, educated and sensitised participants on the link between peace, development, and violence and reminded them who were agents of development at the local level to consider human aspects of development through socioeconomic growth was equally important.
He asked them to endeavour to identify peace connectors in their communities, work with them to keep and sustain cohesion, harmony and peace of their communities and
admonished them not to rush into resolving conflicts, rather analyse drivers of conflicts and design appropriate remedies to address them.
Kwabena Darko-Mensah, the Western Regional Minister, noted that vast land and natural resources in the region was recipe for conflicts, and mentioned causes of conflicts to include chieftaincy disputes, land litigation, unemployment and mining.
He was hopeful the training would broaden the understanding of MMDCEs to be able to resolve such issues in their jurisdictions, however, expressed worry about numerous disputes and litigation, numbering 60, depicting energy with which people would compete to gain control over resources.
"There cannot be development without peace and you must work towards protecting and operating in an environment devoid of conflicts," Mr Darko-Mensah said. -GNA
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As a teacher I get a lot of emails which are for "The teachers of…." but most of the time I don't actually teach this student. The person sending the email is sending to the entire staff email list to make sure they get their message to a selected group of teachers.
For common groups (such as Form Teachers, Departments) the easiest way to avoid this is to create Google Groups – but for teachers of an individual student, in my schools that would require over 1,500 groups (one for each student). So I decided to come up with a system that could pick out the teacher and send them an email, in this case using Awesome Table.
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How to create this for use in your school..
1. You can download the template spreadsheet here.
If your system exports teachers names rather than their email – you can either use Find and Replace or vlookup in Google Sheets to add the email addresses. I prefer vlookup as that makes it easy to change email addresses.
3. Once you get your data in the correct format – you can drop it into the sheet, which gives you the data in the correct format for Awesome Table to use.
4. Now to set up the Awesome Table – visit Awesome Table and log in with your Google Account.
5. Go to "Create a New Blank View" and then select the spreadsheet you created with the teacher email data in.
6. In the settings area, you need to add a value for the template – add Template!A1:B2 in this field.
7. Update the view and you should see your table ready to use or embed in a site (using the share button).


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Home Recipes Dishes & Beverages Beverages
We Found the Perfect Signature Cocktail for Every State
Kelsey Rae DimbergUpdated: Jan. 04, 2023
Travel across the U.S.A.—in your drinking glass! We found the signature cocktail of every state, from well-known historic drinks to tipples known only by the locals. Cheers!
Courtesy of Living Well Kitchen
Alabama: The Yellowhammer
In Tuscaloosa, football is kind of a big deal. When going to cheer on the Crimson Tide, fans sip on the Yellowhammer cocktail, a sweet drink invented in a bar close to the stadium. Named for the bright yellow state bird, the drink is fruity and delicious, made with pineapple and orange juice, Malibu, rum, and vodka. Get the recipe. Want more insider intel? Here are 22 things your bartender won't tell you.
Courtesy of Merchant
Alaska: The Alaska
In 1898, a gold rush sent prospectors northward, to the then-territory of Alaska, in search of riches. Though The Alaska wasn't invented in the state—the miners likely would have sipped whiskey—the cocktail pays homage to the warm glow of gold. Mixed with gin and Yellow Chartreuse, a dash of orange bitters, and lemon, the cocktail is a sunny shade that would be welcome during any long winter. Up next: Learn why Grandma's favorite summer drink deserves a comeback.
Brent Hofacker/Shutterstock
Arizona: Tequila Sunrise
When a guest lounging at the posh Arizona Biltmore Hotel in Phoenix asked bartender Gene Sulit for a surprise, Sulit poured him a Tequila Sunrise: a concoction of crème de cassis, tequila, lime and soda water. The dense cassis sinks to the bottom of the glass, and the lighter ingredients rise above, making a sunrise-in-a-glass. Modern iterations of the drink usually use grenadine rather than the original cassis, and orange juice in place of the lime. Get our Test Kitchen-approved recipe here.
Think your signature cocktail deserves to be best-in-show? Submit your recipe to our Test Kitchen today.
Oleksandra Naumenko/Shutterstock
Arkansas: Bloody Mary
In Arkansas, tomatoes are a booming crop and a celebrated summer fruit; the Bradley County Pink Tomato Festival has run for over 60 years. It feels right to celebrate with a Bloody Mary. This popular morning sipper mixes tomato juice with vodka, lemon, hot sauce, Worcestershire sauce, and an array of garnishes, from celery stalks to lemons. Some Arkansas bars add pickle juice, pickled green beans, olives, and pepperoncini to the adaptable recipe. If you can't make the trip to The Natural State, here are the best Bloody Mary bars across the country.
Joshua Resnick/Shutterstock
California: Moscow Mule
In recent years, the Moscow Mule and its accompanying copper mug have surged in popularity in bars and homes across the country. (Let us show you how to make it!) The relatively simple beverage is fizzy with ginger ale, bracing with vodka, and lifted with a squeeze of lime. Rumor has it the drink was invented at Hollywood's Cock'n'Bull pub in 1941, when three struggling entrepreneurs decided to make a cocktail using their products: One was heiress to a copper mine, another owned the struggling Smirnoff distillery, and the third had developed his own brand of ginger beer. The rest is history.
Courtesy of Boulder Locavore
Colorado: Colorado Bulldog
Coffee shops thrive in snowy Colorado, so it's no surprise that a popular local cocktail uses coffee liqueur as a base. As in a White Russian, the Bulldog combines the liqueur with vodka and tops with cream—but in a twist, a pour of cola tops off the drink. The soda and cream fizz up dramatically, and the resulting concoction is pleasantly effervescent and sweet, sort of like a root beer float. Get the recipe at Boulder Locavore.
Up next: Don't miss these ice cream drinks that are equal parts frosty and boozy.
Courtesy of Karen's Kitchen
Connecticut: The Yale Cocktail
Many Ivy League schools boast signature cocktails. The Yale, which dates back to the early 20th century, is a bit of an odd duck. Rather violet in hue, the drink blends dry gin, Crème Yvette, maraschino liqueur, dry vermouth, and orange bitters. Pleasantly floral and sweet, the drink may go down a bit too easily. Get the full recipe from Karen's Kitchen Stories. Prefer something simple? We've rounded up 21 easy mixed drinks anyone can master.
VDB Photos/Shutterstock
Delaware: Du Pont Hotel Cocktail
Opened in 1913, Wilmington's Hotel du Pont was intended to be the beautiful hotel in the country. Its opening week alone brought 25,000 visitors to gape at the Renaissance-style building. The hotel's namesake cocktail is still worth sipping today. The ingredients are simple and classic: brandy, dry sherry, Angostura bitters, and an orange twist.
Have brandy to spare? Mix it with honey to make our popular recipe for Brandy-Glazed Carrots.
Salivanchuk Semen/Shutterstock
Florida: Rum Runner
Basically a beach vacation in a glass, the Rum Runner is a potent combination of banana liqueur, rum, brandy, and grenadine. The drink is rumored to originate in the Florida Keys in 1971, when the owner of the Holiday Isle Tiki Bar challenged his bartender to make a drink from the excess booze in storage.
Rum lovers should bookmark these recipes.
Courtesy of Greg Dupree
Georgia: Chatham Artillery Punch
Watch out: this punch is seriously potent. According to the Bitter Southerner, it dates back to a military celebration in mid-1800s Savannah. The original recipe calls for lemon juice, plus a full bottle each of cognac, bourbon, and Jamaican-style rum. Finish with four bottles of Champagne (and chase with several glasses of ice water!) Don't miss 40 more old-fashioned Southern foods you'll love.
NadyaEugene/Shutterstock
Hawaii: Piña Colada
The quintessential tropical cocktail, a piña colada mixes Hawaiian fruits pineapple and coconut with rum and lime juice for an all-too-drinkable frozen cocktail that's best enjoyed on a sunny beach. Get the recipe here.
Idaho: Whiskey Sour
Idahoans love their whiskey sours, according to a Versus Reviews study of Google search data by state, and the state's top-selling booze by volume is the Canadian whiskey Black Velvet. A good whiskey's important. since the drink only contains whiskey, simple syrup, and lemon juice. Here's how to make it at home.
Anuwat Pantong/Shutterstock
Illinois: Salty Dog
In the summer of 1952, Chicago hosted both the Democratic and Republican Conventions. In the midst of the fray, entertainer George Jessel supposedly invented the Salty Dog cocktail at the Ambassador East Hotel's famous Pump Room. A potent mix of vodka and grapefruit juice, plus a salted rim, the drink was fortifying—and extremely alcoholic. Get the recipe.
Elena Veselova/Shutterstock
Indiana: Hoosier Heritage
In 2015, IndyStar.com hosted a contest to find Indiana's best cocktail. The popular vote named the Hoosier Heritage the winner. Calling for Knob Creek rye, apple cider, maple syrup, and lemon juice, the drink is deliciously autumnal, and best sipped while watching a football game on a chilly October night. Here are 17 more ways to upgrade your apple cider.
Stwenty3/Shutterstock
Iowa: Blue Water Margarita
Iowa's West Okoboji Lake is one of only four naturally blue lakes in the world. Local watering hole Minerva's Restaurant and Bar salutes the lake with their blue water margarita. Like the classic marg, the cocktail blends tequila, sweet-and-sour, Cointreau, and lime, and adds a dash of Blue Caracao for a stunning color. Get our copycat recipe that comes pretty close to the real deal.
Courtesy of Suzie Sweet Tooth
Kansas: The Amelia Earhart
Kansas-born Earhart made aviation history, and her namesake cocktail is a fruity, sweet, summery sipper. The hero ingredients? Blackberries muddled with sugar, fresh lemon juice, and elderberry-flavored St. Germaine liqueur. Just add vodka and a mint garnish, and enjoy on the porch swing. While you're at it, check out these other recipes from Kansas.
Kentucky: Mint Julep
The mint julep is over 200 years old, though it's only been the official drink of the Kentucky Derby since 1938. The cocktail calls for just three ingredients—bourbon, sugar syrup, and fresh mint—so the proportions are crucial. A perfect julep is an unbeatably refreshing drink for a hot day, at the races or in the backyard. Find more classic Derby recipes right here.
LightField Studios/Shutterstock
Louisiana: Sazerac
One of the oldest drinks in the country, the Sazerac is steeped in New Orleans history. Early recipes called for a very specific cognac, Sazerac de Forge et Fils, imported to the Big Easy since the 1800s. A local apothecary produced Peychaud's bitters, then considered a health tonic. Over time, the cocktail recipe grew more layered and complex, eventually including absinthe, sugar, and rye in place of the cognac. Learn more about the Big Easy's drink of choice.
GANNA MARTYSHEVA/Shutterstock
Maine: Moxie Beach Bum
If you live outside New England, you may never have heard of Moxie, a soda pop dating back to the 1800s, when it was sold as a cure-all. Named the official state drink of Maine in 2005, the cult-favorite soda has a love-it-or-hate-it flavor described as bitter, rusty, or battery-like, thanks to a base of gentian root. Mix Moxie Hunk o' Lime with a dash of rum for a twist on a rum-and-Coke.
We rounded up the best regional sodas around the country.
Courtesy of Putney Farm
Maryland: Diamondback Cocktail
The delicious three-ingredient Diamondback cocktail was invented in the Diamondback Lounge of the Lord Baltimore Hotel, an impressive French Renaissance-style structure built in 1928. A blend of rye whiskey, Applejack, and Yellow Chartreuse, the "snappy" drink is probably named for the diamondback turtle, which is native to the Chesapeake Bay region and which appeared (in cartoon form) on the Diamondback Lounge's advertisements in the 1950s. Next up: Read up on the best recipes from Maryland.
Shyripa Alexandr/Shutterstock
Massachusetts: The Boston Flip
Flips are a very old category of cocktail, one made rich and creamy with the addition of a raw egg, shaken until frothy. (Actually, the name "flip" comes from the method of flipping the drink from container to container to shake it.) The Boston Flip calls for bourbon or rye, Madeira, sugar, and an egg. Here are 10 more classic cocktails you should know.
Maksim Fesenko/Shutterstock
Michigan: Detroit Old Fashioned
Love a classic old fashioned? Just add a dash of Vernons, a ginger ale invented in Detroit in 1866, to the glass to make it a Detroit Old Fashioned. Missing Michigan? Try these local recipes.
Courtesy of Cookie & Kate
Minnesota: The Bootleg
Like many cocktails, the Bootleg's origins lie around Prohibition. The drink was invented at a hotel near Lake Minnetonka, where the city's upper crust traveled for summer outings. Supposedly enjoyed by F. Scott Fitzgerald and Al Capone, the Bootleg is similar to a mojito, but with a lemonade twist. The ingredient list includes a healthy sprinkle of mint leaves, an herbal jolt of gin, lots of fresh lime, and a tangy-sweet lemon-agave syrup. Find the full recipe at Cookie & Kate. Have a bumper crop of mint? Here are 63 ways to use it.
Mississippi: Milk Punch
A Southern take on eggnog, milk punch is usually made with milk or half-and-half, simple syrup, vanilla, and brandy, bourbon, or rum. The punch originated on the east coast in the colonial era, when milk and brandy were considered medicinal. It even appeared in one of the oldest cocktail guides, the 1862 tome How to Mix Drinks. Get more punch recipes for your next party.
Missouri: Golden Glow
This classic cocktail was served at a celebration of the repeal of Prohibition at the New Hotel Jefferson's posh Club Continental. The distinctive bright yellow drink is a potent potion of bourbon, Jamaican rum, orange juice, lemon juice, sugar, and a drop of grenadine. Get 18 other classic recipes from Missouri.
M. Unal Ozmen/Shutterstock
Montana: Jack and Coke
The bestselling booze in Montana? Jack Daniels whiskey. The secret to a good one? Lots of ice. And, if you don't like drinks too sweet, try it with diet Coke. Find more 2-ingredient cocktails here.
Bernulius/Shutterstock
Nebraska: Kool-Aid Cocktail
Kool-Aid was invented in Hastings, Nebraska in 1927, when inventor Edwin Perkins found a way to dehydrate fruit juice concentrate. The fruity, super-sweet drink mixes well with liquor…try tropical punch flavor with a dash of rum and a cherry garnish. Surprised? Here are 21 other regional favorites you've never heard of.
Nevada: Scotch Old Fashioned
Famed Vegas crooner Dean Martin was known for his love of Scotch old fashioneds. The drink combines a healthy pour of scotch, a dash of bitters muddled with sugar, and orange and cherry fruits.
Peek back in time with this round-up of the Rat Pack's favorite foods.
Courtesy of PINE at The Hanover Inn Dartmouth
New Hampshire: Apple in the Dark
Another cocktail with university ties, the Apple in the Dark was invented at the historic Hanover Inn Dartmouth. It's similar to an old fashioned, but with an autumnal taste thanks to apple-infused Scotch and pine-flavored bitters.
Shulevskyy Volodymyr/Shutterstock
New Jersey: The Jersey Devil
According to an old Garden State ghost story, one stormy night in the 1700s, a woman gave birth to an infant she described as the devil, with hooves, a goat's head and bat wings. Though the creature flew out the chimney, legend says he continues to haunt the Pine Barrens area even today. The bright red cocktail named for the monster mixes applejack (once known as "Jersey lightning"), triple sec and cranberry juice. Find more great food from the Garden State.
Courtesy of Kate Ramos/holajalapeno
New Mexico: Chimayó
This enduringly popular cocktail stars tequila in an unusual way: as a sweet rather than sour concoction. Created in 1965 at the Rancho de Chimayó in New Mexico, the cocktail pairs gold tequila, apple juice, fresh lemon juice, and Crème de Cassis. Take a closer look at the recipe.
For another cocktail with Crème de Cassis, try out this recipe for a Kir Royale.
Here are a few more tequila drink ideas that aren't margaritas.
Suzi Pratt/Shutterstock
New York: Manhattan
So many cocktails were invented in New York City; unsurprisingly, the city is a magnet for talented bartenders and those who enjoy strong beverages. That said, we tip our hats to the cocktail named for the city's glitziest borough, the Manhattan. Simple, potent, delicious, the recipe calls for sweet vermouth, rye whiskey, Angostura bitters, a twist of orange, and a maraschino cherry. Get our recipe here.
Yulia Furman/Shutterstock
North Carolina: The Cherry Bounce
The Cherry Bounce dates back to the 1600s and is reputed to have been one of George Washington's favorite tipples. Since 1769, denizens of Raleigh have imbibed the sweet sipper, which contains brandy or bourbon, cherries, and sugar. The trick? The fruit and sugar ferment together to form a rich syrup; then booze is added and left to stew longer, until a potent brew forms. Not even a mouthful of false teeth could keep George Washington from these favorite foods.
Courtesy of The Five O'Clock Cocktail
North Dakota: Smith & Kearns
During North Dakota's 1950s oil boom, oil men Wendell Smith and James Curren, regularly stopped by the Blue Blazer Lounge in Bismarck. One night, tired of the usual whiskey, they asked their bartender for something "restorative." He poured a sweet, fizzy, chocolate-tinged cocktail made with creme de cacao, cream, and soda water. Over time, the cocktail became popular across the country, and Curran morphed into "Kearns." Whatever you call it, it's delicious. Up next: Try our 50 most decadent chocolate desserts.
EXZOZIS/Shutterstock
Ohio: Boozy Buckeye
Chocolatey, peanut buttery buckeyes are Ohio's official dessert. This boozy take mimics the decadent flavor. Mix hot cocoa mix with Bailey's and a spoonful of melted peanut butter. Delicious! Before you start sipping, check out the traditional Buckeye recipe.
Oklahoma: The Roy Rogers
Roy Rogers, "The King of the Cowboys," was married at the Flying L Ranch in Davis, Oklahoma, and filmed popular flicks like "Home in Oklahoma" in the state. Rogers was said to be a teetotaler, spurring the invention of an alcohol-free Roy Rogers drink, made with grenadine, Cola, and a Maraschino cherry. Get 10 more recipes inspired by America's most-loved cowboys.
Courtesy of Blossom to Stem
Oregon: Spanish Coffee
The oldest restaurant in Portland, Oregon, Huber's Cafe, claims to sell more Kahlua than any other independent restaurant in the country. Where do they pour it? Into their famous Spanish coffee recipe: a fiery concoction that's as much a show as it is a drink. Rum and triple sec are poured into a sugar-rimmed glass and set aflame tableside by an expert bartender. After the flames caramelize the sugar, they're extinguished with a dash of Kahlua. Hot coffee and whipped cream finish the over-the-top drink. Get our Test Kitchen's simplified version here.
Courtesy of Alexandra's Kitchen
Pennsylvania: Fish House Punch
In 1732, the Schuylkill Fishing Company of Pennsylvania—informally known as the Fish House—was founded. Their signature punch, a potent bowl of rum, brandy, peach brandy, and lemon, may be the oldest punch in the English-speaking world. Get the recipe from Alexandra's Kitchen. Next up: Try these sweet rum recipes.
Oxana Denezhkina/Shutterstock
Rhode Island: Rhode Island Red Cocktail
The state bird of Rhode Island is the Rhode Island Red, a rather pretty chicken with thick, auburn feathers. The cocktail that shares its name is reddish pink, and it's a sophisticated sipper with tequila, Chambord, lemon juice, agave syrup, orange bitters, and ginger beer. Top with raspberries and fresh mint for a summery drink. Not your style? Try one of our favorite spiked lemonade recipes instead.
South Carolina: Gin Fizz
With over 160 bars (not counting restaurants with bars), Charleston is a great town for cocktails. Tour company Bull Dog Tours recommends ordering a classic gin fizz while you're in town. Labor-intensive but oh-so delicious, the gin fizz must be shaken for up to 12 minutes in order to create that fluffy, ethereal froth of egg whites atop the drink. Discover more famous cocktails from around the country.
Shebeko/Shutterstock
South Dakota: The Roosevelt
Raise a drink to that iconic national landmark, Mount Rushmore. The Roosevelt is strong and bold: dark rum, dry Vermouth, fresh orange juice, and sugar—shaken, strained, and poured. Find more wonderful recipes from South Dakota.
petereleven/Shutterstock
Tennessee: Lynchburg Lemonade
Lynchburg, Tennessee is the home of the Jack Daniel's distillery, and this cocktail celebrates the city and the famous whiskey. The refresher contains Jack Daniel's Tennessee whiskey, triple sec, lemon juice or sour mix, and lemon-lime soda. Did you know that whiskey tastings were once banned in Tennessee? Learn about the weirdest wine and liquor laws from across the country.
Scruggelgreen/Shutterstock
Texas: Margarita
The traditional margarita dates back to the '30s, when cocktail lore has it that a bartender invented it to impress Rita Hayward. (Get our best tips for making one at home today!) What transformed the cocktail from a west coast beverage to one of the most popular cocktails in the country? Mario Martinez's invention of the frozen margarita machine in Dallas in 1971, propelling both the frosty tequila drink and Tex-Mex food in general, into popular culture.
Utah: Shirley Temple
Utah is known for some of the strictest liquor laws in the US, from restrictions on alcohol levels of tap beers to limits on the amount of booze that can be poured into a cocktail. The sweet Shirley Temple is a nod to a delicious cocktail alternative, no booze necessary. Learn more about the surprising origins of your most beloved food.
Andrei Mayatnik/Shutterstock
Vermont: Old Vermont
Vermont produces about 47% of the maple syrup in the country. The Old Vermont cocktail celebrates the sweetness: maple syrup is mixed with an equal amount of citrus juice (orange and lemon), a dash of bitters, and herbal gin. Get more recipes from Vermont.
Ivan Mateev/Shutterstock
Virginia: Gin Rickey
Now an established classic drink, the gin rickey was invented around Washington D.C. by Civil War veteran Colonel Joe Rickey. Rickey usually drank whiskey with soda water, until one day he tried adding lime juice and the shell of the squeezed citrus. The cocktail is bracing and not-at-all sweet, which marks it as distinctive amongst many other drinks on this list. Here are 15 more gin cocktails you're missing out on.
Kseniya Maruk/Shutterstock
Washington: Irish Coffee
Though coffee shops are ubiquitous from coast-to-coast these days, coffeehouse fever started in Seattle. We'll drink to that! Enjoy a strong brew in the classic Irish Coffee: Irish whiskey, freshly brewed coffee, a spoonful of sugar, and heavy cream. The trick? Combine the first three ingredients in your glass and stir. Lightly whip or shake the cream. Hold a spoon over the glass, and carefully pour the cream over it to "float" it over the drink, topping every sip with a froth of cream. Get more tips on how to make the perfect Irish coffee here.
etorres/Shutterstock
West Virginia: Gin and Tonic
According to the West Virginia tourist board, the gin and tonic at Tin 202 in Morgantown is one of the most popular in the state, despite its simplicity. The secret? The bar uses West Virginia-made Smooth Ambler barrel-aged gin, a mix of tonic and seltzer so it's fizzy but not too sweet, and the oil from a twist of lemon. Only true G&T fans will appreciate this recipe for Gin & Tonic cupcakes.
Wisconsin: Brandy Old Fashioned
Wisconsinites spend more money on brandy than any other state in the union. Visit any dinner club worth its salt, and you're sure to find a brandy old fashioned featured prominently on the menu. Using brandy instead of whiskey, the old fashioned is a bracing, sweet drink that's dangerously easy to sip. The brandy is combined in a rocks glass with maraschino cherries, orange juice, a dash of bitters, a bit of lemon-lime soda, and plenty of ice cubes.
Is Wisconsin cheese really the best?
Courtesy of MixThatDrink
Wyoming: The Grizzly Bear
While grizzly bears are pretty ferocious, the cocktail named for them is mild and sweet. A creamy blend of Kahlua and milk gets a bit of a bite from Amaretto, and a chomp from Jägermeister. Did you know that you can make Kahlua at home? Here's how.
Kelsey Rae Dimberg
A former in-house editor at Taste of Home, Kelsey now writes articles and novels from her home in Milwaukee. She's an avid cook, reader, flâneur, and noir fanatic. Her debut novel, Girl in the Rearview Mirror, will be published in June 2019 by William Morrow.

redpajama_set_name: RedPajamaCommonCrawl


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Donations are tax deductible and can be doubled or even tripled with company matching gifts programs.
A "Letter of Intent" (LOI) for Gift of Securities and Gifts from IRA can be found here. Please complete the LOI along with any forms that your broker or plan manger requires and send them to your broker or plan manager along with a copy of the completed LOI to Corpus Christi Catholic School, Advancement Office.
If you would like to make a gift of appreciated securities or mutual funds and/or would like more information about remembering Corpus Christi Catholic School in your Estate Plan, please contact the Director of Advancement, Susan Miller at 713-664-3351.
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Clay Love!
The studio will be open for clay lovers on February 15th (Saturday) between 2:30-5:30 p.m. Please pre-register with Diane to attend. Open studio fees apply. Clay, glazes and instruction not included. Course Fees: Drop-in $15 Clay (b-mix)…
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eARTh Studios is Open in its NEW studio!!!
We have a brand new studio for you to create in! Custom built for eARTh Studios needs plus a gas fire place! Join us for Open Studios on Wednesday 6pm-9pm or check our calendar to see what classes are running! [ngg src="galleries"…
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2019 Wheel World Intensives!
We are pleased to host new WHEEL Intensives in Jan/Feb/March! These wheel Worlds are INTENSE and hands on! 3 Saturdays = 18 hours of instruction. First Workshop 6 hour days! Saturday Jan 12/10-4 Throwing Saturday Jan 19th/10-4…
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Hand-building Classes are BACK!
We are EXCITED, I can tell you, to announce that weekly classes are BACK for Hand building in the New Year! Class will occur every Tuesday morning Jan 8th 9a.m. -12 p.m. and run until Feb 5th. A 2 week hiatus to allow for full drying…
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Clay Therapy! Open Studio 2019!
WE are ALMOST READY ! Open Studios will start on Wednesday January 16th!! 6-9! First come first served! Our new location is also a tiny home design so we will be cozy! $15 drop in....clay glazes and firings extra. Clay $35 B Mix Cone Six Firing…
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New Studio in the Works!
eARTh Studios is building a whole new home! But due to this massive undertaking we will be closed until January for workshops. Here is a photo of the process of the new Studio. We hope to see you in the new year!
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eARTh's Tile and Pinch Hand-building Workshop!
NEW: eARTh's Tile and Pinch Hand-building Workshop eARTh Studios is hosting a hand-building workshop on April 8th, 9th and 22nd. The delicate craft of pinching handmade bowls will be introduced and developed using a variety of techniques…
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Wheel Throw Down!
eARTh Studios hosted our first beginner and intermediate Wheel World Throw Down in January 2017. The results spoke for themselves, as new and old potter's alike threw cups and bowls in series challenging themselves to stay with the process…
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It's the "movie" that everyone's talking about — and it hasn't even been made yet.
"What if a people that share a common and rich heritage can find a common future. Their story is well known. But what will be their sequel?" the narrator asks as scenes from North Korea fill the screen.
"Featuring President Donald Trump and Chairman Kim Jong Un in a meeting to remake history, to shine in the sun. One moment. One choice. What if? The future remains to be written," the narrator continues.
"Will he shake the hand of peace?" the narrator asks.
"It is maybe the most propagandistic four minutes that I have ever seen," Travers added.
"Who made this? Destiny Pictures? Did Trump hire them to make the movie? I think that's yet to be decided, but what we're seeing on screen is nothing but people saying, 'Look, let's cheer. Let's propagandize,'" said Travers.
"We had it made up. I showed it to him today, actually during the meeting, toward the end of the meeting and I think he loved it," Trump said of the video during a news conference.
The video was also shown to a wider audience at the start of Tuesday's press conference before making the rounds on social media.
"It's the scariest four minutes I've seen on screen this year," he said.
Watch Peter Travers' review of the movie trailer in the video above.


Copyright © 2018 Meredith A. Ray et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We proposed an approach that has the ability to detect spatial clusters with skewed or irregular distributions. A mixture of Dirichlet processes (DP) was used to describe spatial distribution patterns. The effects of different batches of data collection efforts were also modeled with a Dirichlet process. To cluster spatial foci, a birth-death process was applied due to its advantage of easier jumping between different numbers of clusters. Inferences of parameters including clustering were drawn under a Bayesian framework. Simulations were used to demonstrate and assess the method. We applied the method to an fMRI meta-analysis dataset to identify clusters of foci corresponding to different emotions.
This work was motivated by a study aiming to detect centers of activated foci from a function magnetic resonance imaging (fMRI) metadataset. In summary, fMRI metadata is a collection of fMRI studies identifying areas of the brain that are significantly activated by stimuli to examine a specific outcome. FMRIs are expensive which leads to small sample sizes and therefore can use metadata to increase sample size and power. To identify spatial clusters, finite mixture models are generally implemented [1–3]. Mixture components, representing a cluster, typically share a common parametric family with each component containing respective parameters [1, 2]. Each component also has a mixing proportion or weight that is respective to the frequency of data in that component . Because of the model's ease of implementation, this allows various applications such as pattern recognition, computer vision, signal and image analysis, and machine learning to list a few .
One commonly used distribution in the aforementioned finite mixtures is the normal distribution [1, 5], appreciating its established properties and, in the Bayesian context, conjugacy. However, when it comes to clustering, assuming normality for each part of the mixture can potentially lead to oversensibility, e.g., when one cluster is formed by a mixture of two normal distributions but with rather close centers. This type of oversensibility in many research fields should be avoided; one example is emotional foci inferred from brain imaging data, where a certain emotion is covered by a wide region.
To infer the number of clusters, under the Bayesian framework, different methods have been proposed. Reversible jump Markov chain Monte Carlo has been commonly used to infer the number of clusters [6, 7]. At each iteration, a decision is made between splitting one cluster to two, combining two clusters to one, or no movement. One potential difficulty of this approach is the risk of being trapped at a local maximum. Recently, the Dirichlet process (DP) has been used often to estimate the number of clusters [8, 9]. This process has the ability to capture irregular patterns. DP has the ability to detect clusters without being burdened about additional clustering parameters. However, this feature of clustering also has an inherit weakness in that it tends to produce more clusters, making interpretations more difficult.
To overcome the aforementioned gaps, we implemented a mixture of Dirichlet processes (DP). These processes have the ability to describe irregular patterns [8, 9] and by using it as our common parametric family allows the model to identify more complex patterns than the normal distribution would be able to identify. Furthermore, motivated from our previous work with the spatial Cox process application in , we elected to incorporate the birth-death process to statistically determine the number of clusters. Compared to other clustering approaches noted earlier, the birth-death process has the advantage of quick convergence and, by controlling birth rate, embraces a potential of generating redundant clusters.
The remainder of the article is organized as follows. In Section 2, we introduce the model structure, notation, and priors and hyperpriors, simulations are presented in Section 3, the application of the model to an fMRI meta-analysis dataset is in Section 4, and conclusions and discussion are in Section 5.
We let denote the coordinate of a spatial point in a three-dimensional space, in particular, point j in group (study) r, and . We have , where is the total number of observed points. It straightforwardly follows that represents all points in the study. We model as where denotes the effect of group , while represents the mean of for the point in group after adjusting for group effects, , and denotes some random error. By modeling the random error as a standard multivariate normal distribution, the distribution of satisfies with where is the covariance matrix.
We start from the prior distribution of . To detect underlying clusters of due to similarities of , we describe the prior of as a mixture of distribution , . Common choices of are normal distributions. To improve flexibility, we relax such normalizing assumptions in the mixture and assume is generated from a DP, i.e., , where is the precision parameter and is a base distribution and taken as . In particular, , where such that . For the number of clusters K, we assign a truncated Poisson prior distribution to , , . We assign a Dirichlet distribution with parameter 1 to , which implies that is k-dimensional uniformly distributed. The prior distribution of from the base distribution is chosen to be , where , which are known and set as the midpoint of observed interval of variation of the data. Parameter is set as where , and are the range of the data for each dimension. This prior setting is adopted from  and we feel it is reasonable for this setting given the fact that the number and location of clusters are unknown. We let with since the range of the observed data is small. For group effect, , we assume it is small with , where with specified later and , where the lower and upper limits are defined as 10% of the absolute range of the data. We let and , with . The variance component of the random error, , is assumed to follow a relatively noninformative Inverse Gamma (IG) distribution, . The precision parameters and are selected by minimizing the deviance information criterion (DIC) [11, 12].
Sampling parameter estimates from their posterior distributions can be achieved via Gibbs sampler, in which the statistical inference on the number of clusters is modeled using the birth-death process. The birth-death process is one type of continuous-time Markov chain originally introduced in . This process is often used to simulate realizations of point processes as they can be difficult to directly sample from . These realizations are further used for likelihood inferences for model parameters . The birth-death scheme allows events to randomly occur throughout the chain; these events are either a "birth" or "death." If a birth occurs, the number of components increases by one, while if a death occurs, the number of components decreases by one.
The joint posterior distribution is proportional to where , is a vector of all estimable parameters. From here, the birth-death algorithm and Markov chain can be described for introducing and assigning clusters for :(1)Starting with the initial model , let represent the mixing proportion and cluster specific parameters for the unique clusters. Let the birth rate be .(2)Calculate the death rate for each component: (3)Calculate the total death rate . To quicken convergence, we elected not to model the time to next jump as exponential and allowed an event to occur at each iteration of the Markov chain.(4)Simulate the type of event, birth or death, with the respective probabilities: (5)Adjust the model to reflect the birth or death by the following:(i)Birth: Simulate a new component from each parameter's respective (independent) prior distributions, from and from base distribution, such that the model becomes . It can be mentioned that is the Beta distribution with parameters and can be easily simulated from and such that .(ii)Death: Select a component to die with the probabilities for such that the model becomes (6)Given the current state of the model at time , simulate values for all remaining parameters.(7)Go to step .
Incorporating the birth-death process into our model, we need to further define the likelihood for removing cluster : where . The birth-death process is conditional on the prespecified birth rate, . By setting this birth rate, which controls how often a "birth" of a new component occurs, equal to as suggested and done in , this computationally allows the death rates to be a likelihood ratio absent of . In other words, the likelihood of the data drives the death rates and ultimately the decision of a new cluster. Given that decision is a "birth," the new cluster's parameters ,, and are sampled from their prior distributions: The mixing proportions are adjusted by multiplying all current proportions by if a birth occurs or dividing by if a death occurs.
The sampling of unique values for and can be performed using Neal's algorithm 8 . It works by introducing auxiliary parameters that are independent to other parameters to represent potential values for and . Algorithm 8 for updating clustering assignments, denoted as , is as follows:(i)The state of the Markov chain consists of and with denoting cluster parameters, e.g., in our application. Repeatedly sample as follows:(ii)For , let be the number of distinct for , and . Label these with values in . If for some , draw values independently from base distribution for those for which . If for all , let have the label , and draw values independently from for those for which . Draw a new value for from using the following probabilities: where is the likelihood with and observation , , involved. In our case, it is ,(iii)where is the number of for that are equal to and is the appropriate normalizing constant. Change the state to contain only those that are now associated with one or more observation.(iv)For all , draw new values from such that , or perform some other update to that leaves this distribution invariant .
Thus, the value for for those foci in cluster and subcluster may be sampled from where are the number of foci in some cluster and subcluster and denotes the mean of the observed data after adjusting for group effect. The value for for those groups (studies) in group cluster (different notation than subclusters above) may be sampled from where is the number of groups in cluster and similarly denotes the mean of the observed data after adjusting for individual effect.
To estimate the number of clusters and the center of each cluster and cluster assignment, we implement the same least-squared Euclidean distance method introduced in  and used previously in our work in  and reiterated below. This method draws inferences on clusters based on a set of converged MCMC iterations and chooses one iteration as the final estimates for the clusters and related parameters. This final MCMC iteration is selected due to its smallest Euclidean distance to the expected cluster assignments estimated based on a set of independent converged MCMC iterations. This approach incorporates all clustering information in the MCMC sampling process :(1)After the prespecified number of MCMC burn-ins, let the MCMC simulations continue for an additional iterations. An averaged clustering matrix is then created, denoted as , and is an matrix with each block or entry denoting how often foci and () are in the same cluster. Specifically, each entry is the proportion of the W iterations that two foci are in the same cluster.(2)Let the MCMC run additional iterations, where, for each iteration,(a)create an matrix using indicators to denote which foci are clustered together; i.e., let the entry denote a 1 if foci and are in one cluster and 0 otherwise.(b)use Euclidean distance to determine the similarity between this indicator matrix and the averaged clustering matrix .(3)Among the iterations, select the iteration and respective clustering pattern, number of clusters, and parameters that produce the smallest Euclidean distance.
Simulations were utilized to illustrate and assess the proposed method. We assumed an fMRI metadata study setting including 50 studies, with each study containing 10 foci. Collectively these foci were simulated from three clusters centered as (x,y,z) talairach coordinates at (, , and containing 150, 150, and 200 foci, respectively. It was also assumed that half the data, 250 foci or half from each individual cluster, came from one study cluster centered at and the remaining data from a second study cluster centered at . These study clusters were linear shifts to cluster centers. For example, 75 foci in cluster one were centered at ( and the other half were centered at (; the study effect was a linear shift to all three dimensions from the cluster center. We made various alterations to this general setting:(1)Normal setting: We simulated data from a multivariate normal for each cluster with respective means described above and a variance of . This creates spheres with little variation and we expect the method to have the ability to correctly identify the clusters.(2)Chi-squared (skewed) setting: The method's ability to cluster in the presence of abnormal patterns is an important factor in spatial clustering. For this setting, we applied the same scenario as in the normal setting in respect of clusters 1 and 2 but simulated cluster 3 using a chi-squared distribution with 4 degrees of freedom.(3)Large variance setting: The last scenario is designed to assess the robustness of the method with respect to the distance between and among clusters. To this end, we applied the normal setting but considered increasing levels (referred to large1, large2, large3, and large4 settings, resp.) of : , and , representing gradually closer distances among clusters.
For each setting, we implemented a grid search for a single dataset to estimate values of and based on the minimization of DIC. We let precision parameter values be 0.01, 0.05, 0.1, 0.5, 1, 2, and 5. Based on and estimates, 100 MC datasets were generated with 2500 burn-in iterations, 500 working iterations to calculate the probability matrix for determining the clusters, and 100 additional iterations to infer the number of clusters and individual foci cluster centers.
Model assessment consists of three evaluations: sensitivity, specificity, and accuracy. Sensitivity and specificity are defined by their generic definitions, the proportion of foci that are correctly assigned to their simulated cluster, and the proportion of foci that are correctly not assigned their nonsimulated cluster. Accuracy is defined as the percentage of foci that are correctly clustered. Note that the definition of accuracy takes into account both true positive and true negatives. In addition to our methodology, we applied a very common existing clustering approach for continuous data, K-means, to our simulation settings. Although this method cannot adjust for additional covariates, it allows for a comparison to existing methods. Lastly, to highlight the advantage of using a mixture of DPs over existing clustering approaches, we applied our approach, a revised version of our approach using a mixture of multivariate normal distributions rather than DPs, and Kmeans to the normal and chi-squared simulation scenarios. As the emphasis was on clustering performance, the group effect was assumed to be known for these two settings.
Table 1 summarizes the findings on the three foci-level cluster identifications and the quality of the identified clusters. The proposed method gives high sensitivities and specificities across all scenarios. The accuracy of cluster assignment overall is higher than 90% when the variation in the data is relatively small and only dropping once clusters were large enough to overlap (scenario Large 4). The proposed methodology was also accurate at identifying the correct number of clusters as indicated by the median number of individual and study clusters. In comparison, results from the Kmeans approach indicate relatively lower statistics in sensitivity and specificity (Table 2). The accuracy is around 70%, when the variations in the data are relatively small. However, compared to the proposed method, the Kmeans approach often inferred a higher number of clusters as indicated by the larger median number of clusters. The computation time of a single dataset for the mixture of DPs took, on average, 7-8 hours on a high performance computer (Dell cluster with 88 compute nodes, 3120 total central processing unit cores, 20664 Giga-bytes of RAM, and 61440 total graphic processing unit cores).
Table 1: Simulation assessments for three foci-level clusters.
Table 2: Kmeans simulation assessments for three foci-level clusters.
After removing study effects, the comparison between the mixture of DPs, mixture of normal, and Kmeans is as expected (Table 3). Both mixtures performed exceptionally well at identifying the three normally distributed clusters while the Kmeans performance was adequate with an overall accuracy of 80% (compared to 100% for both mixtures). Once the data deviated from normality, the mixture of normals approach was unable to differentiate the clusters resulting in low accuracy (32%). The Kmeans approach performed similarly to the mixture of DPs with both approaches having low sensitivities for the third cluster which was skewed but with the mixture of DPs resulting in superior accuracy. The mixture of DPs was able to differentiate clusters 1 and 2 as indicated by 99% sensitivity and 100% specificity measures but tended to "overcluster" cluster 3 into smaller clusters as indicated by the large median number of clusters, 14% sensitivity, and 100% specificity. In regard to overall accuracy, the mixture of DPs outperformed the mixture of normals and Kmeans when the data are skewed.
Table 3: Comparison of approaches for selected simulated settings assuming study effect is known.
For this application, we applied the proposed method to a meta-analysis dataset. Constructed originally in , this data consists of a total of 162 neuroimaging publications, of which 57 were PET and 105 were fMRI. Among these 162 publications, there were 437 contrasts or studies. Only those foci that were deemed significantly activated by their study specific criteria were included for a total of 2,478 foci. Summary statistics for this data can be seen in Tables 4 and 5.
Table 5: Frequency of emotions.
As with the simulation studies, grid search and DIC were used to estimate values for and . Potential precision parameters values were 0.01, 0.05, 0.1, 0.5, 1, 2, 5, and 7.5. Each combination was performed over 2,600 iterations, 2,000 of those for burn-in, 500 for the probability matrix calculation, and final 100 to infer individual clusters and their centers. To assist with the magnitude of the likelihood calculations, the data was scaled down by 10.
It was found that the precision parameter combination of and produced the smallest DIC. Convergence, with the initial 2,000 discarded, was checked based on trace plots. Based on the proposed method, we identified four study clusters and 14 individual foci clusters. A single DIC setting for this data took, on average, 72 hours to run on the HPC. The break down of the 14 individual foci clusters by center location, brain location, foci frequency, and study frequency can be seen in Table 6. The frequency of each foci-associated emotion within each of the 14 clusters can be seen in Table 7. The affective emotion was dominating in all clusters with fear being the second dominating emotion in clusters 1, 2, 3, 11, and 13, disgust in clusters 5 and 14, sadness in clusters 6 and 10, and a mixture of emotions in the remaining clusters. When only focusing on those foci that fell within known brain regions of interest, as seen in Table 8, the dominating emotion, other than affective, was sadness, fear, and disgust, respectively. When compared to the number of clusters identified by the spatial Cox Point process (53) and Kmeans (20) performed in , fewer clusters were identified with our current application. It should be noted that this particular data does not visually indicate distinct clusters and is closer to a more uniform distribution throughout the brain which may lead to an inaccurate number of identifiable clusters. However, given the results from our previous analysis in  and findings in simulation studies, it is possible that the clusters formed were rather subtle and actually might not be distinct enough.
Table 6: Meta-data cluster results.
Table 7: Breakdown of emotions and their frequencies by individual foci cluster.
Table 8: Breakdown of emotions and their frequencies by individual foci cluster for ROI.
Modeling the realization of observed foci as a linear association of study effect and individual foci cluster effect with a multivariate normal random error was motivated by the limitation of the spatial Cox process to statistically distinguish between a cluster and a mode or peak of a cluster. The overall aim remained to identify activated regions within the brain using fMRI coordinate-based metadata. By modeling the data in this fashion, it was hopeful that the distribution could statistically differentiate between clusters and modes of clusters while retaining the flexibility and robustness to mimick the behavior of the data.
Simulation studies demonstrated that the method can fit data generated from normal or abnormal distributions. Furthermore, it was able to identify clusters within covariates while retaining the integrity to identify individual clusters. Both the proposed method and Kmeans were unable to correctly identify clusters when they were large and overlapped and both the mixture of normals and mixture of DPs performed poorly at identifying a cluster severely skewed.
When applied to a fMRI metadataset, the method identified a relatively low number of clusters. Given the low sensitivity findings in the simulated study with high noise, it can be concluded that this data had a high likelihood of being too broad. When the same data was analyzed with the spatial Cox process, the difference in the results was extreme. Not only were the number of clusters substantially less, but also none of the cluster centers identified from the proposed method came close to those identified in the first method. It is worth mentioning that the meta-analysis data is not distinctly grouped and is more uniformly distributed throughout the brain and perhaps the model used did not provide the best fit.
The primary advantage to this method, besides its flexibility, is its ability to describe irregular spatial patterns and its sampling design to statistically differentiate clusters. Because of its adaptable nature, this model can also adjust for any covariate(s) of interest. However, based on simulation studies and the fMRI metadata application, the proposed method tends to be too insensitive and has a difficult time identifying clusters when data are not distinctly differentiated. A potential limitation in the approach is that each DP within the mixture was assumed to have the same precision parameter. It was noted that, during the simulation studies when the mixture of DPs was attempting to fit the Chi-squared simulations (without study effect), it was overclustering the skewed cluster. However, when the precision parameter was smaller, the identifiability of cluster 3 became more accurate but became more inaccurate for clusters 1 and 2. Thus, to further improve flexibility and accuracy for this approach when data is skewed, each DP potentially requires its own unique precision parameter. Furthermore, this method's clustering ability is limited by the identification of study effects which may be improved by implementing stronger restrictions or could be an effect of having multiple DPs. Our future work will focus on these issues, allowing study effect to be random rather than a fixed effect, and identifying if a large number of DPs within the model is indeed a limitation.
Data is not currently publicly available but available upon request from Professor Tor D. Wager at the University of Colorado.
No conflicts of interest exist for any author.
Dr. Kang's effort was supported by an NIH Grant 1R01MH105561. Dr. Zhang and Dr. Ray's efforts were supported by their start funds provided by the University of Memphis.


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A Barbie: Delfin varázs (eredeti cím: Barbie: Dolphin Magic) 2017-ben megjelent egész estés amerikai 3D-s számítógépes animációs film, amelynek a rendezője Conrad Helten, a producere Sarah Serata, az írója Jennifer Skelly, a zeneszerzője Rebecca Kneubuhl.

Cselekmény

Barbie és a testvérei úgy döntenek, hogy meglátogatják Ken-t. Búvárkodás közben egy ritka, szivárványszínű, évente csak egyszer látható delfinfajt fedeznek fel a víz alatt. Felfedezésükön felbátorodva elhatározzák, hogy még többet tudnak meg a vízi világról. Ekkor találkoznak Isla-val. A titokzatos új barátról hamar kiderül, hogy egy igazi sellő. Az idő azonban sürget, nincs idő megünnepelni az új barátságot, a delfineknek segítségre van szükségük...

Szereplők

További magyar hangok: Mayer Szonja

Források

 Barbie: Delfin varázs a themoviedb.org oldalon

További információk
 rövid bemutató a play.barbie.com oldalán

Kapcsolódó szócikkek
 A Barbie-filmek listája

2017 filmjei
Amerikai számítógépes animációs filmek
Amerikai kalandfilmek
Amerikai fantasyfilmek
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redpajama_set_name: RedPajamaWikipedia


text:
Companies And Markets
Burj Khalifa home handover from March
The handover will take two to six months. (GETTY IMAGES)
Emaar Properties said it will start handing residents keys to their new homes in Burj Khalifa, the world's tallest building, in March.
"The Residences, comprising 900 apartments, and The Corporate Suites, a collection of high-end offices, will be handed over to customers starting March," the developer said.
"The handover programme can take anywhere from two to six months," it added. Burj Khalifa was opened on January 4.
Emaar Properties has spent $1.5 billion (Dh5.5bn) on the tower since construction began in 2004, and at the peak of construction 12,000 workers were involved.
The developer of the iconic tower is readying to welcome its first residents. An orientation programme for home-owners of Armani Residences located on levels nine to 16 is currently under way.
The interiors of Armani Residences is in the final stages, and finishing work of The Residences in Burj Khalifa is progressing at a fast pace, an official release said yesterday. The hand-over of homes begins first with the Armani Residences comprising 144 private apartments personally designed by Giorgio Armani.
Keep up with the latest business news from the region with the Emirates Business 24|7 daily newsletter. To subscribe to the newsletter, please click here.


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You are at:Home » Other Sports » Horse Racing Tips » Grand National Tips » 2016 Grand National | Horse-By-Horse Guide | 9th April 2016 | Channel 4
2016 Grand National | Horse-By-Horse Guide | 9th April 2016 | Channel 4
By The Voice Of Value on	 Apr 8, 2016 Grand National Tips, Horse Racing Tips, Other Sports, Tips
IT'S one of the biggest races of the year and also one of the hardest to call so let Voice of Value (@TheVoiceOfValue) mark your card ahead of the 2016 Crabbies Grand National with his thoughts on all 40 runners.
1. Many Clouds 8/1 (Bet365)
Age: 9 | Weight: 11st 10lb | Trainer: Oliver Sherwood | Jockey: Leighton Aspell
Last year's winner who I suggested would be better waiting for another year. Oliver Sherwood agreed with me but Trevor Hemmings the owner insisted that he should run and the rest is history.
Anyone who hasn't seen the video from the 'jockey cam' that was attached to Leighton Aspell's helmet should watch it now on You Tube. It makes the hairs stand up on the back of your neck and gives you a great idea of what it's like to ride in the National.
Can Many Clouds do what no horse since Red Rum has managed and win back-to-back Grand Nationals? Whisper it quietly but he might just do it.
His performance last year left me stunned as well as skint. How he managed to carry nearly top weight to victory as an eight year old I just could not fathom and I'm still taken aback by it.
He is clearly a freak and from only 1lb higher this year I can't find a reason why not. He is hard to recommend as a tip at prices like 7/1 but I wouldn't be surprised to see him in the mix again. One word of caution – although he has previous decent form on soft ground I think the combination of quite soft ground and top weight would be too much for him to handle.
It's much more difficult to carry a heavy weight in softer ground but if it ends up being good ground on the day then he has every chance of making history.
Value Rating: 7/10
2. Silviniaco Conti 12/1 (William Hill)
Age: 10 | Weight: 11st 8lb | Trainer: Paul Nicholls | Jockey: Noel Fehily
An old favourite of mine this horse – absolutely top class on his day and a few years ago I would have been surprised if someone said he would be running in the National because he is a Grade 1 horse who many would see as being too high class for this.
He recently bounced back to form in fine style and proved that he could be as good as ever but the only question is will he last out the marathon trip? My instinct says no, that he will tire and be pulled up.
There is always a chance that he could get into a nice rhythm at a pace slightly slower than what he is used to (he is a regular winner of Grade 1 chases over 3 miles including two King George V chases at Kempton) and just cruise into it without exerting too much effort. Either way his price is too short so I don't see any value in backing him.
3. First Lieutenant 40/1 (Betfred)
Age: 11 | Weight: 11st 4lb | Trainer: Mouse Morris | Jockey: TBC
Former top class chaser who isn't quite the force of old and has never suggested that he would stay the distance in this. Finished 19th last year and I would be surprised if he could improve much on that. Jumping is solid enough.
4. Wonderful Charm 50/1 (Paddy Power)
Age: 8 | Weight: 11st 4lb | Trainer: Paul Nicholls | Jockey: Sam Twiston-Davies
Seems to be a difficult horse to find the right race for and although you can't rule anything out with Paul Nicholls I would be surprised to see this lad stay the distance.
He was pulled up at Sandown last year when tried over a longer trip and although he looks like he is staying on in some races at the death I just don't think he quite has the class to carry this sort of weight to victory.
5. Ballynagour 66/1 (BetBright)
Age: 10 | Weight: 11st 2lb | Trainer: David Pipe | Jockey: Tom Scudamore
A horse who is out of form and carrying a fair bit of weight with no signs that he would be comfortable with the marathon distance. David Pipe is notorious for getting one ready for a big race though, indeed he did it with this horse at Cheltenham in 2014, but it would be a real surprise if this one was ready to rock.
6. O'Faolains Boy 33/1 (BetVictor)
Age: 9 | Weight: 11st 1lb | Trainer: Rebecca Curtis | Jockey: Brian Hughes
Won the RSA at Cheltenham in 2014 but hasn't hit those heights again since to be honest. Rebecca Curtis had issues with some dodgy hay of all things last year but the horses seem to have recovered now. I'm not sure he will stay and I also think they might go a bit too quick for him. Without solid recent form it's hard to recommend him.
7. Gilgamboa 66/1 (BetVictor)
Age: 8 | Weight: 11st 1lb | Trainer: Enda Bolger | Jockey: TBC
The suspicion is that he is better over 2 miles and 2.5 miles and he is probably a year too young from a statistics point of view too.
8. On His Own 66/1 (Betway)
Age: 12 | Weight: 11st 1lb | Trainer: Willie Mullins | Jockey: Patrick Mullins
A grand old horse this one who has been around for years in the top races without quite doing the business although he did manage a very respectable 2nd in the Gold Cup in 2014.
He fell two years in a row in this race so that is a definite no for me, even if he wasn't a bit too old and a bit high in the weights. You're on your own if you fancy backing him.
9. The Druids Nephew 16/1 (Bet365)
Age: 9 | Weight: 11st 0lb | Trainer: Neil Mulholland | Jockey: Denis O'Regan
Was extremely well-handicapped last year coming into the race and he looked like he would be challenging when he fell four fences from home. There is no way of knowing what would have happened though – he was travelling alongside Soll at the time and that one faded from three out and finished 9th (as if I need any reminding).
He isn't as well-handicapped the year and runs off a higher weight with that nagging doubt about the fall last year. I don't see him as great value.
10. Triolo D'Alene 33/1 (BetVictor)
Age: 9 | Weight: 11st 0lb | Trainer: Nicky Henderson | Jockey: Jeremiah McGrath
A lightly raced horse for his age who has popped up a couple of times with a win, most notably in the Hennessy at Newbury in 2013. He hasn't managed much since then although he won in January at Kempton over a shorter distance.
He was pulled up in this race two years ago and although that can be partly forgiven as a result of his age at the time there isn't anything that jumps out at me to recommend him. His trainer Nicky Henderson, although a magician in other areas, has never won this race.
11. Rocky Creek 66/1 (William Hill)
Age: 10 | Weight: 10st 13lb | Trainer: Paul Nicholls | Jockey: Andrew Thornton
I fancied him to get a place last year but he couldn't manage it. He has been out of form this year so despite previous form that gave him a squeak I suspect he has had his chance at the big prize.
12. Sir Des Champs 20/1 (NetBet)
Age: 10 | Weight: 10st 13lb | Trainer: Willie Mullins | Jockey: TBC
It's remarkable to see Sir Des Champs sitting here with a weight under 11st in the Grand National, available to back at 40/1 and still aged only ten.
He was one of the very best a couple of years back and was my tip for the Gold Cup in 2013 when he could only manage 2nd to an impressive Bobs Worth. He has had injury problems since then and despite a winning return after a very long layoff he has subsequently had a couple of poor races.
If I thought he could recapture the form of old we could perhaps take a chance on him staying this sort of trip but I suspect he wouldn't be 40/1 if he was in better form. No doubt he will find the winning habit again but I suspect it won't be this week.
13. Holywell 16/1 (Paddy Power)
Age: 9 | Weight: 10st 12lb | Trainer: Jonjo O'Neill | Jockey: Richie McLernon
It's probably fair to describe Jonjo O'Neill as a 'crafty' trainer. He has a knack of getting horses' handicap ratings down to a reasonable level before suddenly producing the horse to win a big race.
Holywell is interesting in that he is probably the best horse in training with Jonjo yet he has still managed to manipulate his handicap mark to the extent that he popped up at Cheltenham to finish second in a competitive race and he runs here off a very favourable handicap mark for a horse who finished a respectable 4th in the Gold Cup last year.
He needs good or good to soft ground – anything softer than that and he might even be a non-runner – but if he gets it he could be the best handicapped horse in the field.
The only slight concern I have is that he is a relatively small horse and the National fences can be tiring for a smaller horse who needs to put more into their jumping. His jumping hasn't been perfect over the years but probably isn't too much to worry about.
I can see him running a huge race and perhaps getting Richie McLernon the compensation he deserves after that agonising photo-finish in 2012 when he rode Sunnyhillboy.
14. Shutthefrontdoor 20/1 (Betway)
Age: 9 | Weight: 10st 11lb | Trainer: Jonjo O´Neill | Jockey: Barry Geraghty
Finished 5th last year after it looked like Tony McCoy would sign off his career with a winner in the Grand National but he just faded on the run-in.
There is an interesting argument which suggests that Shutthefrontdoor came to the race last year without a preparation run and that because he has had a warm up this year he should be in better shape. He is also a couple of pounds lower in the weights which is helpful and is a year older – one of my concerns last year was that he was inexperienced.
Another interesting point is that Barry Geraghty was on board for his last two wins (including the Irish National in 2014) so he will know how to get the best out of the horse.
The trends suggests that those who have tried to win the race before don't come back and do it second time round but I could easily see this lad get into the places and at almost three times the price that he was last year that looks like good each way value.
15. Soll 50/1 (Bet365)
Age: 11 | Weight: 10st 11lb | Trainer: David Pipe | Jockey: Conor O'Farrell
He was my top tip last year and was travelling like a dream before just fading late on and ended up only finishing 9th. I tried to argue that Tom Scudamore had him too prominent and went off too quick with him but the truth was that the horse probably just didn't stay the full 4.5 miles. He is higher in the weights this year as a result of another win this season and the task will be even tougher than last year.
Perhaps Conor O'Farrell will ride him differently and will manage to get something extra out of him but it does look unlikely. For the avoidance of doubt, at fancy prices like 50/1 I'll be having a 'wee saver' each way, if only to dream about what might have been last year.
16. Buywise 66/1 (Betfred)
Age: 9 | Weight: 10st 10lb | Trainer: Evan Williams | Jockey: Paul Moloney
A fascinating entry who has never really been tried over longer trips but always finishes his races strongly, suggesting that a longer trip would suit.
His major problem is his jumping – he sometimes clatters one or two fences and that usually puts him just too far behind to catch the leaders at the death. Jumping errors are of course magnified over these fences so that is a major downside. On the plus side though, we have Paul Moloney the jockey.
It seems like has ridden a horse into the top four in the National every year since I used to watch Postman Pat and he is the master of negotiating a horse round the course from the back of the field and then running into a place at the end. Last year it looked like the rest of them saw him coming and accepted the inevitable as Alvarado bounded up into 4th from way off the pace.
It remains to be seen whether Buywise can have a safe round of jumping and give Paul the chance to do the same again. 50/1 is a fair price though.
17. Boston Bob 33/1 (BetBright)
Age: 11 | Weight: 10st 10lb | Trainer: W P Mullins | Jockey: Ruby Walsh
The magical partnership of Mullins and Walsh combine and it's not often you'll find one of their horses available at 25/1. Boston Bob has been in and around the top 3 mile chases in Ireland over the last few years without ever quite becoming a star. One of his better wins actually came in his most recent run when he pipped in On His Own in a 3 mile slog in the mud.
He won't mind a bit of rain and might be the sort who will enjoy the marathon trip but I have slight concerns with his jumping. He sometimes blunders at his fences and if he does that here he won't be able to get back into the race.
18. Aachen 100/1 (Bet365)
Age: 12 | Weight: 10st 10lb | Trainer: Venetia Williams | Jockey: TBC
This old boy had something of a resurgence this season, winning a couple of races including an impressive victory at Cheltenham in December. I'm not sure he has the class to win this off a career-high handicap rating and his front-running style won't be advantageous when it gets to the business end of things.
On the flip side, many will remember when Venetia Williams trained Mon Mome to victory (I certainly do – he was the horse that made me fall in love with the Grand National) who was also a 100/1 shot and if there is a bit of rain then they might not go too fast for Aachen and he could outrun his price.
His jumping is sound and he hasn't been tried over a marathon trip. If you want one who will race near the front at a fancy price and give you something to cheer on for the first couple of laps then this could be the one for you.
19. Morning Assembly 25/1 (BetBright)
Age: 9 | Weight: 10st 9lb | Trainer: P A Fahy | Jockey: Davy Russell
As followers of my Cheltenham previews will remember I tipped this lad for the Ultima Handicap and he duly grabbed a place at nice odds. He ticks a whole load of boxes – he has good form in Grade 1 races so shouldn't be unsettled by the speed of the race, he jumps well, has a good jockey on board and he has potential to improve and he looks like the much longer trip will suit him nicely.
He is also versatile in relation to the ground so a bit of rain won't be a problem, it might actually be beneficial because it will inconvenience so of those above him in the weights. I have backed this one each way and I would advise you all to do the same at nice odds of 25/1. He looks a solid selection and if stays the trip he could be the one to win it with a bit of luck.
20. Double Ross 100/1 (Betway)
Age: 10 | Weight: 10st 9lb | Trainer: Nigel Twiston-Davies | Jockey: TBC
A temperamental sort who pops up from time to time with a big win but he looks out of form at the moment and I am sure his best distance is around about 2.5 miles rather than this test. I think he will be a non-stayer even if he is on song which isn't guaranteed.
One positive in his favour is that his trainer Nigel Twiston-Davies is having an excellent spell at the moment and has won the Grand National twice in the past with Earth Summit (1998) and Bindaree (2002) so he knows better than most how to get one ready.
21. Goonyella 20/1 (Paddy Power)
Age: 9 | Weight: 10st 8lb | Trainer: Jim Dreaper | Jockey: J J Burke
I used to be very much of the opinion that Goonyella was a slow ground plodder who could stay on in a slowly run race, a view that was reinforced by his win at Uttoxeter last year in the Midlands Grand National where he stayed on whilst others dropped away in the tough conditions.
He then went up to Ayr for the Scottish Grand National and if you go back and watch the race again he is very interesting (the SportingLife website lets you watch all the old Racing UK races for free if you have a SkyBet account – a great tool if you are a weirdo like me who likes to watch the old races. Between that and the new AtTheRaces app you can now access pretty much any old race from the UK and Ireland and view it again for free).
That race in the Scottish National was run at a pretty quick pace on good ground and although Goonyella was urged along to keep up with them he did manage to stay with them and then on the run in he looked like he would stay 6 miles as he nearly caught the winner and stormed past other horses.
You can't be anything other than impressed by that run and if this race was run over regulation fences he would be my top selection. My only slight concern is that he ran over the National-style fences in the Becher Chase in December and only managed to finish 9th.
Watching the race back he does seem to jump the fences ok but there is a nagging doubt in my mind that he didn't absolutely love it. The case to be made for him is this: if he can get a good start, keep up with the hectic pace at the start, jump round safely enough (avoiding the inevitable carnage) and stay within say 15-20 lengths of the leaders, I can almost guarantee that there won't be any horse staying on better than him as they come round the elbow an he could be eating up the ground.
Who wouldn't want to be on that horse, roaring him towards the line? I can visualise this plan in my head so let's hope it's as easy as that for the horse and Johnny Burke on board!
22. Ucello Conti 25/1 (Boylesports)
Age: 8 | Weight: 10st 8lb | Trainer: Gordon Elliott | Jockey: Daryl Jacob
A 'talking horse' from Ireland – not literally of course, the humans do the talking for him – but the word is that he is nicely handicapped and could be one to run well at a decent price.
He was given an 'interesting' ride last time out by the French jockey Jacques Ricou who took him so wide on the course the whole way round that he must have run almost an extra mile compared to the other runners in the race and if it was an attempt to protect his handicap mark for this race it wasn't very well disguised.
In any event, there is a suspicion that Gordon Elliott has this race planned for him. It remains to be seen how he will take to the fences and the marathon trip so I have just stopped short of putting him on the shortlist but don't be surprised to see some significant support for him in the market before the start of the race.
23. Unioniste 33/1 (Bet365)
Age: 8 | Weight: 10st 8lb | Trainer: Paul Nicholls | Jockey: Nick Scholfield
Fell in the race last year when he was only a 7 year old and shouldn't have been anywhere near the race but Paul Nicholls remains convinced that he could be a good Grand National horse.
He is certainly better placed in the weights this year but that's because he has been out of form and I'm not sure his win at Kelso is particularly good form at all – the horse he beat that day is a real plodder. I would rarely recommend a bet on a horse who fell the previous year and I won't be doing it now.
24. Le Reve 50/1 (Betfred)
Age: 8 | Weight: 10st 8lb | Trainer: Lucy Wadham | Jockey: Harry Skelton
A good quality horse without being in the top bracket and there isn't much to suggest he will enjoy the long distance here. May be one to keep an eye on for next year.
25. Gallant Oscar 20/1 (NetBet)
Age: 10 | Weight: 10st 8lb | Trainer: Tony Martin | Jockey: TBC
Decent Irish handicap type of horse who won a nice race at Punchestown last year but hasn't done much since and there are others with better recent form that appeal more. Barry Geraghty agrees, having chosen to ride Shutthefrontdoor rather than this or one of the other horses owned by JP McManus (for whom Geraghty is the retained jockey).
26. Onenightinvienna 50/1 (BetVictor)
Age: 7 | Weight: 10st 8lb | Trainer: Philip Hobbs | Jockey: Tom O'Brien
It is almost certainly a year or two too early for this horse who only has four runs over fences to his name (1 win and 2 seconds) and although he is probably well-handicapped he would be better waiting for another year or two. This is not a race for young, inexperienced horses.
27. The Last Samuri 10/1 (Bet365)
Age: 8 | Weight: 10st 8lb | Trainer: Kim Bailey | Jockey: David Bass
Probably the horse with the best handicap mark in the race, having won nicely at Doncaster since the weights were fixed in February. He is now way too short a price for me though at something like 10/1. I'm not convinced that the race he won was particularly strong because most of the better horses were being aimed at Cheltenham at that time and I think he might have been flattered slightly.
He has come right through the ranks winning five races along the way but this is a different test and I doubt he will have the pace to go with them or the stamina to see out the trip. I would perhaps take a chance on him if we were getting 33/1 but not at the current prices.
28. Kruzhlinin 28/1 (Betway)
Age: 9 | Weight: 10st 7lb | Trainer: Philip Hobbs | Jockey: Richard Johnson
A few interesting points to note about this one. Firstly, he has been over the National-style fences twice when he was 10th in the race two years ago (when only 7) and then again when he was 7th in the Becher the following December.
Secondly, Richard Johnson the champion-elect jockey in the UK takes the ride. He was off the track for a year until switching stables from Donald McCain to Richard Hobbs and winning in January. The jury is still out on whether he will get the 4.5 miles, I suspect not, but he is certainly one with the potential to improve and we know he is ok with the fences.
29. Rule The World 50/1 (William Hill)
Age: 9 | Weight: 10st 7lb | Trainer: Mouse Morris " Jockey: TBC
He has a touch of the second-itis disease this horse – finishing 2nd on 10 of his 24 starts over his career. He has some decent form in the book in good quality races in Ireland without ever really showing anything that we could get excited about. It's not clear that he will stay although a 2nd in the Irish National at Fairyhouse gives some hope.
It would be interesting if Bryan Cooper decided to ride this ahead of First Lieutenant or Sir Des Champs (the other horses owned by Michael O'Leary). Mouse Morris managed to get Rogue Angel spot on to win the Irish National this year and who is to say that he can't have this one in perfect shape too? Could be a lively outsider.
30. Just A Par 50/1 (Bet365)
Age: 9 | Weight: 10st 9lb | Trainer: Paul Nicholls | Jockey: Sean Bowen
Won the Bet365 Gold Cup at Sandown last year in good style but I don't think that was the best quality renewal of that race that there has ever been so I would be skeptical about his chances of living with the pace and the quality in this field. Would probably benefit from good ground so any more rain would be a negative. I should also say that Sean Bowen is an excellent young jockey who gets on well with the horse so that is certainly a positive.
31. Katenko 80/1 (Betway)
Age: 10 | Weight: 10st 6lb | Trainer: Venetia Williams | Jockey: TBC
Not the force of old and there is little to suggest that he will be able to bounce back to top form. Even top form wouldn't be good enough I suspect.
32. Vics Canvas 66/1 (BetVictor)
Age: 13 | Weight: 10st 6lb | Trainer: Dermot McLoughlin | Jockey: Robert Dunne
Getting on a bit now and has fallen a couple of times. A bit like your uncle with the drink problem.
33. Black Thunder 66/1 (Bet365)
Age: 9 | Weight: 10st 6lb | Trainer: Paul Nicholls | Jockey: Gavin Sheehan
Nicholls always fancied this as a proper stayer but it doesn't seem to have quite materialised. He has been quite lightly raced over the last couple of years so I suspect there have been injury problems. I can't find much to recommend about him to be honest.
34. Ballycasey 80/1 (Bet365)
Age: 9 | Weight: 10st 6lb | Trainer: Willie Mullins | Jockey: TBC
I've always thought this one was overrated. He was brought down by another horse in the race last year, there is nothing to suggest he will stay and he has also fallen in the past so I'm not convinced.
35. Hadrian's Approach 66/1 (Betfred)
Age: 9 | Weight: 10st 6lb | Trainer: Nicky Henderson | Jockey: Nico D'Boinville
The right age, a good weight and a winner of the Bet365 chase over 3.5 miles in 2014 so he might get the trip. However he is just back from injury and has had more bad days than good days over the the year (including a couple of falls) so I wouldn't have much confidence backing him. Would need Nicky Henderson to perform a minor miracle.
36. Vieux Lion Rouge 80/1 (Betway)
Age: 7 | Weight: 10st 5lb | Trainer: David Pipe | Jockey: James Reveley
A reasonable enough horse who was won his fair share of races recently but he is still quite young. Without any guarantee that he will stay (the 4 miler at Cheltenham recently suggests the opposite) then I think it's daft to run him in this.
37. Pendra 66/1 (BetVictor)
Age: 8 | Weight: 10st 5lb | Trainer: Charlie Longsdon | Jockey: TBC
Won a decent 3 mile race this year but looked like he lacked stamina in the Irish National last year.
38. Saint Are 16/1 (Paddy Power)
Age: 10 | Weight: 10st 5lb | Trainer: Tom George | Jockey: Paddy Brennan
It's absolutely impossible to ignore his claims this year and I would have him almost as favourite for the race if I was pricing it up. He was a (relatively) fast-finishing second last year and races off a similar handicap mark again this year.
There is an argument, made rather eloquently by an old racing friend of mine called Dicko this week actually, that says that once horses have had their chance to win it they don't come back again and do the business.
This is hard to argue against when you look back over the years – horses like Rocky Creek, Sunnyhillboy and Cappa Bleu over recent years I had expected to come back and at least match their performance from the year before but it never seems to happen. Balthazar King is one who did well the second time – improving from 15th to a close 2nd from one year to the next – but it doesn't happen regularly.
I just feel that it is difficult to ignore Saint Are with Tom George the trainer in better form now than he was last year. I suspect good ground would be preferable although he does have some form on soft.
39. Home Farm 100/1 (Boylesports)
Age: 9 | Weight: 10st 4lb | Trainer: Henry De Bromhead | Jockey: TBC
A solid enough horse who appears to have been aimed at very good races when he doesn't quite have the class for it. The same seems to be true today and I wonder whether the owners are looking for a day out with this horse rather than a win. He has been pulled up twice over longer trips when tried in the past.
40. The Romford Pele 50/1 (Paddy Power)
Age: 9 | Weight: 10st 4lb | Trainer: Rebecca Curtis | Jockey: Trevor Whelan
One for the Arsenal fans here with his name being a reference to the Arsenal legend Ray Parlour. Has never looked like a staying chaser and has questionable jumping so much like Arsenal I wouldn't trust him to get the job done when it matters.
Shortlist 1 – if the ground is officially 'Good' or 'Good to Soft':
1. Morning Assembly – 25/1
2. Holywell – 14/1
3. Saint Are – 16/1
4. Goonyella – 16/1
5. Shutthefrontdoor – 16/1
Shortlist 2 – If the ground is officially 'Soft' or 'Heavy'
3. Bishops Road – 25/1
Grand National 2016 – Morning Assembly each way (25/1 Bet365)
Grand National 2016 – Holywell each way (16/1 William Hill)
Grand National 2016 – Goonyella each way (20/1 Bet365)
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text:
Q: How to pass delegate to a UITextField inside an IB_DESIGNABLE? I have created a custom view and added a UITextField to it.My question is: How can I add a delegate so that the you can set it as the default cmd+drag behaviour of XCode?
My current attempt is by doing something like this:
In my .h file:
#import <UIKit/UIKit.h>

IB_DESIGNABLE

@interface CustomTextField : UIView

   @property (assign, nonatomic) IBInspectable id textFieldDelegate;

@end

and my .m file:
#import "CustomTextField.h"

@interface CustomTextField()

@property (strong, nonatomic) IBOutlet UITextField *textField;

@end

@implementation CustomTextField

- (instancetype)initWithFrame:(CGRect)frame{
    if (self = [super initWithFrame:frame]) {
        [self loadNib];
    }
    return self;
}

- (void)loadNib{

    UIView *view = [[[NSBundle bundleForClass:[self class]] loadNibNamed:@"CustomTextField" owner:self options:nil] firstObject];
    [self addSubview:view];
    view.frame = self.bounds;

}

- (void)setTextFieldDelegate:(id)textFieldDelegate{
    self.textField.delegate = textFieldDelegate;
}

@end

But it doest show  up in the left panel of XCode, Connections Inspector Tab.
Also here is my current code.
Update 1:
also if using the code below I get a an error:
-  (id)initWithFrame:(CGRect)aRect
{
    self = [super initWithFrame:aRect];

    if (self)
    {
        [self loadNib];
    }

    return self;
}

- (id)initWithCoder:(NSCoder*)aDecoder
{
    self = [super initWithCoder:aDecoder];
    if (self)
    {
        [self loadNib];
    }

    return self;
}

- (void)loadNib{
//
//    UIView *view = [[[NSBundle bundleForClass:[self class]] loadNibNamed:@"CustomTextField" owner:self options:nil] firstObject];
//    [self addSubview:view];
//    view.frame = self.bounds;

    UIView *view = [[[NSBundle bundleForClass:[self class]] loadNibNamed:@"CustomTextField" owner:self options:nil] firstObject];
    [view setTranslatesAutoresizingMaskIntoConstraints:NO];
    [self addSubview:view];
    [self addConstraints:[NSLayoutConstraint constraintsWithVisualFormat:@"H:|[view]|" options:0 metrics:nil views:NSDictionaryOfVariableBindings(view)]];
    [self addConstraints:[NSLayoutConstraint constraintsWithVisualFormat:@"V:|[view]|" options:0 metrics:nil views:NSDictionaryOfVariableBindings(view)]];

    self.textLabel.text = @"1";

}

Any suggestions on what should I do?
Update 2
 I ended up by replacing the initWithFrame, initWithCoder and loadNib functions with:
- (id)awakeAfterUsingCoder:(NSCoder *)aDecoder
{
    if (![self.subviews count])
    {
        NSBundle *mainBundle = [NSBundle mainBundle];
        NSArray *loadedViews = [mainBundle loadNibNamed:@"CustomTextField" owner:nil options:nil];
        CustomTextField *loadedView = [loadedViews firstObject];

        loadedView.frame = self.frame;
        loadedView.autoresizingMask = self.autoresizingMask;
        loadedView.translatesAutoresizingMaskIntoConstraints =
        self.translatesAutoresizingMaskIntoConstraints;

        for (NSLayoutConstraint *constraint in self.constraints)
        {
            id firstItem = constraint.firstItem;
            if (firstItem == self)
            {
                firstItem = loadedView;
            }
            id secondItem = constraint.secondItem;
            if (secondItem == self)
            {
                secondItem = loadedView;
            }
            [loadedView addConstraint:
             [NSLayoutConstraint constraintWithItem:firstItem
                                          attribute:constraint.firstAttribute
                                          relatedBy:constraint.relation
                                             toItem:secondItem
                                          attribute:constraint.secondAttribute
                                         multiplier:constraint.multiplier
                                           constant:constraint.constant]];
        }

        return loadedView;
    }
    return self;
}

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