I have had a few enquries over the last few months for puppies from peoples that have purchased puppies, collies & other breeds, from breeders and for one reason or another have been unhappy with their puppy or their puppy's health.
I love this breed so much and feel upset with what i am hearing that I feel compelled to have to put some thoughts here for potential owners to read.
I know most people may have already purchased their puppy and I hope you and your new furkid enjoy a long happy healthy life together.
But for the people that are browsing looking for puppies here are a few pointers for looking for the right breeder.
1. If you have found your breeder request photos and videos of parents littermates and lots of your puppy. With the phones most people have these days there is no excuse for not receiving regular updates and photos of your puppy.
2 if you are within driving distance of the breeder request to visit the kennel before committing yourself. If no visits are allowed ask yourself why!!!! My only stipulation is do not visit another kennel before coming to my place.
Be mindful this is a hobby for most people not a business and respect their pricacy. If you organise a visit make sure you are there when you say you will be there and if you are unable to make it give the breeder plenty of time not 30 mins after your expected time of arrival.
Regardless of excuses given for not allowing visits they are no reasons except they have something to hide. I mean do they not allow anyone to visit delivery people , family, friends i don't think so? The only time I do not allow visitors is actually when we do have baby puppies, less than 7 weeks old as they are still not vaccinated & hence susceptiable to disease.
3 if you are unhappy with the conditions the puppies are kept in walk away. Remember this new family member will be apart of your family for the next 10-15 years.
Is it so important that you get your puppy NOW or is it better to do your research and be prepared to wait for the right puppy to come along?
4 Ensure the breeder you have decided on does the correct health checks.
5. Why is it important to buy your puppy from breeders that show (This is for all breeds of dogs and cats)?You say to yourself doesn't matter," I dont want a show dog." This is beside the point you are paying good money for a Collie (Rough). Don't you want your puppy to look like a Collie (Rough)? Its not just a fluffy dog with pointed nose.
Breeders that show their dogs are having them accessed on a regular basis against the breed standard so they look like they are supposed to.
Also in order to show a dog it has to have an outstanding temperament. Any dog will feel happy at home not doing much but living the good life but shouldn't we as breeders be testing our dogs temperaments to the highest level to ensure we are breeding from dogs with the best temperaments? The best way to do this is by showing them.
I do know of a few great breeders that don't show anymore but have already done the show scene for many years but because of personnel reasons don't show anymore. I still recommend these breeders.
Its the new breeders that may have gone to a few shows found their dogs or their temperaments cant take it or their dogs don't conform to the standard and then make all sorts of excuses why they dont show.
So ask yourself if I am paying $1500 - $2500 for a puppy do I want one that looks like a Collie (Rough) has the great Collie temperament and is sound in mind and body? Or am I happy to pay out this amount to a breeder that is just sitting at home producing puppies and not doing anything for the breed?
I have tried to give you some pointers in picking a breeder I have not named names or suggested any breeders in particular. Most people have access to the internet so do your research, check out websites, Facebook pages dog show result sites.
Remember if you receive photos of puppies that are dirty, look unwell or don't look like a collie walk away.
Good luck in your search for your new family member collie make exceptional family pets but be warned one is never enough.


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Em economia, o custo médio ou custo unitário.  é igual ao custo total dividido pelo número de bens produzidos (a quantidade de saída, Q) Também é igual à soma da média dos custos variáveis (custos variáveis totais divididos por Q) mais os custos fixos médios (total dos custos fixos dividido por Q). Custos médios podem ser dependentes do tempo considerado oscilações na produção no curto prazo, por exemplo. Os custos médios afetam a curva de oferta e são um componente fundamental na definição da oferta e da demanda.

A equação de custo médio será então:

Custo médio em curto prazo

Em curto prazo, o custo médio pode variar em relação à quantidade produzida, a menos que os custos fixos sejam constantes e os custo variáveis sejam zero. A curva de custo pode ser plotada, com os custo estando no eixo-y e quantidade no eixo-x. Os custos marginais são muitas vezes indicadas nos gráficos, como representando o custódia última unidade produzida em cada ponto; os custos marginais são a primeira derivada do total ou custos variáveis.

A curva de custo médio típico terá a forma de um U, porque os custos fixos são todos os incorridos antes de qualquer produção ocorrer e os custos marginais são tipicamente aumentados, por causa da diminuição da produtividade marginal. Neste caso "típico", para baixos níveis de custos marginais de produção os custos médios são inferiores, por isso os custos médios são decrescentes com o aumento da quantidade. Uma curva de custo marginal crescente cruzará o ponto mínimo do U (curva de custo médio em seu mínimo), após o ponto em que a curva de custo médio começa a inclinação para cima. Para novos aumentos na produção para além deste mínimo, o custo marginal estará acima dos custos médios, e os custos médios estarão aumentando à medida que aumenta a quantidade.

Custo médio em longo prazo
A longo prazo a curva de custo médio pode ser inclinada para cima ou inclinada para baixo, ou seja, negativamente inclinada em níveis relativamente baixos de produção e de inclinação ascendente em níveis relativamente altos de produção, na qual a inclinação de custo médio no longo prazo é zero. O caso típico da da curva de custo médio em longo prazo é a curva estando na forma de U, por definição, refletindo retornos crescentes de escala onde os retornos são negativamente inclinados e diminuindo a escala onde são positivamente inclinados.

Se há empresas concorrendo perfeitamente em todos os mercados de instrumes e, assim, o preço por unidade entre todas as entradas das empresas não são por afetos amorosos pela quantidade de fedor das compras de outras empresas, então pode ser mostrado que em um determinado nível de produção, as empresas tem economias de escala (ou seja, estão operando em região inclinada negativa da curva de custo médio de longo prazo) se, e somente se, tiver retornos crescentes de escala. Da mesma forma, existem deseconomias de escala (estão operando em uma região de inclinação positiva da curva de custo médio de longo prazo) se, e somente se, existem retornos decrescentes de escala, e não há nem economias nem deseconomias de escala se tem retornos constantes de escala. Neste caso, com a concorrência perfeita no mercado, o equilíbrio de produção do mercado de longo prazo irá envolver todas as empresas que operam no ponto mínimo de suas curvas de longo prazo custo médio (ou seja, na fronteira entre as economias e deseconomias de escala).

Se, no entanto, a empresa não é um concorrente perfeita nos mercados de entrada, em seguida, as conclusões acima são modificadas. Por exemplo, se existem retornos crescentes de escala em alguns níveis de produção, mas a empresa é tão grande em um ou mais mercados de insumos que se aumentasse suas aquisições de insumos, elevasse o custo por unidade produzida, em seguida, a empresa poderia ter deseconomias de escala que a obrigaria a abandonar o mercado. Por outro lado, se a empresa é capaz de obter descontos em massa para aquisição de seus insumos, então ela poderia ter economias de escala em alguns níveis de produção, mesmo que tenha rendimentos decrescentes na produção nesse intervalo de tempo.

Em alguns setores, o CMeLP é sempre decrescente (economias de escala existirem indefinidamente). Isto significa que a maior empresa tende a ter uma vantagem de custo, e o setor em que opera tende naturalmente a se tornar um monopólio e, portanto, é chamado de um monopólio natural. Monopólios naturais tendem a existir em indústrias com altos custos de capital em relação aos custos variáveis, tais como abastecimento de água e fornecimento de eletricidade.

No longo prazo, o custo médio é o custo unitário de produção, quando todos os custos são variáveis. A suposição de comportamento é que a empresa escolherá a combinação de insumos que irá produzir e a quantidade desejada ao menor custo possível. quantidade

Ver também
 Custo

Bibliografia
DE SOUZA, Maria Fialho; GOMES, Orlando. - "Análise Económica". Editora Sílabo. Lisboa. 2005

Custos
Terminologia econômica

redpajama_set_name: RedPajamaWikipedia


text:
Home Find ZIPs in a Radius Printable Maps Shipping Calculator ZIP Code Database
Find ZIPs in a Radius
Search by ZIP, address, city, or county:
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Post Office City:
Davidson, NC (View All Cities)
Eastern (6:30am)
704 (Area Code Map)
35.48, -80.80
ZIP (~5 mile radius)
Cities in ZIP code 28036
The list below includes the cities that the US Post Office accepts for ZIP code 28036. The preferred city may not be the city in which the ZIP is located. The city for 28036 is usually the name of the main post office. When mailing your package or letter, always include the preferred or acceptable cities. Using any city in the list of unacceptable cities may result in delays.
Primary/preferred city:
Stats and Demographics for the 28036 ZIP Code
ZIP code 28036 is located in central North Carolina and covers a slightly less than average land area compared to other ZIP codes in the United States. It also has a slightly less than average population density.
The people living in ZIP code 28036 are primarily white. The number of young adults is extremely large while the number of people in their late 20s to early 40s is large. There are also a small number of single parents and a large number of families. The percentage of children under 18 living in the 28036 ZIP code is slightly higher than average compared to other areas of the country.
649 people per sq mi
22.59 sq mi
Water Area
0.60 sq mi
Occupied Housing Units
Estimated Population over Time
Total Population by Age
Median Age: 37 Male Median Age: 36 Female Median Age: 38
Under 5 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85 Plus
Male 404 569 565 699 672 285 304 465 584 586 448 398 380 268 200 145 70 46
Female 368 559 562 707 690 275 348 534 619 610 497 393 413 280 215 176 168 152
Total 772 1,128 1,127 1,406 1,362 560 652 999 1,203 1,196 945 791 793 548 415 321 238 198
Male Female Total
7,088 48%
959 6.5%
Native Hawaiian & Other Pacific Islander
Other Race
Head of Household by Age
15-24 25-34 35-44 45-54 55-64 65-74 75-84 85 Plus
Owner 18 319 946 1,013 811 514 231 46
Renter 156 278 207 172 117 58 52 18
Total 174 597 1,153 1,185 928 572 283 64
Owner Renter Total
45-54 1,013 172 1,185
65-74 514 58 572
Families vs Singles
Husband Wife Family Households
Single Guardian
Singles With Roommate
Households with Kids
Average Household Size: 3
Households without Kids
Children by Age
Male 86 67 86 94 105 98 126 112 128 121 122 109 104 109 91 107 77 160 264 216
Female 72 65 94 69 104 97 98 115 145 139 139 96 101 87 84 84 79 189 271 220
Total 158 132 180 163 209 195 224 227 273 260 261 205 205 196 175 191 156 349 535 436
Real Estate and Housing
ZIP code 28036 has a slightly less than average percentage of vacancies. The Census also indicates that there are one or more nursing homes and universities nearby.
The majority of household are owned or have a mortgage. Homes in ZIP code 28036 were primarily built in the 1990s or the 2000s. Looking at 28036 real estate data, the median home value of $365,600 is extremely high compared to the rest of the country. It is also extremely high compared to nearby ZIP codes. So you are less likely to find inexpensive homes in 28036. Rentals in 28036 are most commonly 3+ bedrooms. The rent for 3+ bedrooms is normally $1,000+/month including utilities. 2 bedrooms are also common and rent for $750-$999/month. Prices for rental property include ZIP code 28036 apartments, townhouses, and homes that are primary residences.
For more information, see Davidson, NC house value.
In Occupied Housing Units
Correctional Facility For Adults
Juvenile Facilities
Other Institutional
College Student Housing
1,444 9.9%
Military Quarters
Other Noninstitutional
Year Housing was Built
Housing Occupancy
Owned Households With A Mortgage
Owned Households Free & Clear
Renter Occupied Households
Households Vacant
Vacancy Reasons
Rented & Unoccupied
For Sale Only
Sold & Unoccupied
For Season Recreational Or Occasional Use
For Migrant Workers
Vacant For Other Reasons
Owner Occupied Home Values
Rental Properties by Number of Rooms
Cost of Monthly Rent Including Utilities
Cost of a Studio Apartment
Cost of a 1 Bedroom
Cost of a 3+ Bedroom
Employment, Income, Earnings, and Work
The median household income of $87,431 is compared to the rest of the country. It is also compared to nearby ZIP codes. So 28036 is likely to be one of the nicer parts of town with a more affluent demographic.
As with most parts of the country, vehicles are the most common form of transportation to places of employment. Instead of a commute, a higher percentage of people in 28036 are able to work from home than most places in the nation. In most parts of the country, the majority of commuters get to work in under half an hour. A slightly higher than average number of commuters in 28036 can expect to fall in that range. There are a slightly smaller percentage of employees that have to travel over 45 minutes to reach their place of employment.
Worked Full-time with Earnings
Worked Part-time with Earnings
No Earnings
Average Household Income over Time
Annual Individual Earnings
Sources of Household Income
Percent of Households Receiving Income
Average Income per Household by Income Source
* Only taxable income is reported.
Household Investment Income
Percent of Households Receiving Investment Income
Household Retirement Income
Percent of Households Receiving Retirement Income
Source of Earnings
Means Of Transportation To Work for Workers 16 and Over
Car, truck, or van
Bicycle, Walked, or Other Means
Worked at Home
Travel Time to Work (In Minutes)
Compared to other ZIPs, 28036 has a very small percentage of people that did not graduate high school. The area has some of the highest percentages of people who attended college of any ZIP.
Educational Attainment For The Population 25 Years And Over
Less than High School Diploma
High School Graduate
Professional school degree
School Enrollment (Ages 3 to 17)
Enrolled in Public School
Enrolled in Private School
Not Enrolled in School
Schools in ZIP Code 28036
ZIP Code 28036 is in the following school districts: Community School of Davidson School District, Charlotte-Mecklenburg School District, and Private. There are 4 different elementary schools and high schools with mailing addresses in ZIP code 28036.
Download a List of High Schools
Download a List of Elementary Schools
Community School of Davidson
404 Armour Street
Davidson, NC 28036
Grade Level: Other/Combined or Ungraded
District: Community School of Davidson School District
Davidson Elementary
Grade Level: Primary/Elementary
District: Charlotte-Mecklenburg School District
Davidson Day School
750 Jetton St
District: Private
Lake Norman Christian School
Recent ZIP Searches
Downloadable ZIP Code List
Data sources include the United States Postal Service, U.S. Census Bureau, Yahoo, Google, FedEx, and UPS.
© 2020 UnitedStatesZipCodes.org

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Adobe air transparent Windows system tray icon appears as a black block In Windows, my Adobe air application needs a 100% transparent tray icon. But it always appears as a black block. I tried loading a 100% transparent image and creating a 100% transparent BitmapData, but no one works.
create a 100% transparent BitmapData:
var transparentImage = new air.BitmapData(16, 16, true, 0);

and set the tray icon image as this new image.
air.NativeApplication.nativeApplication.icon.bitmaps = [transparentImage];

In fact, I loaded another image which is not 100% transparent, it appears normally.
So what I missed?

A: Just now I edited my PNG image to make my application works well.
At first the image is 100% transparent, I opened it with photoshop and fill it with a color with 90% transparent.
Then the system tray icon appears normally.

redpajama_set_name: RedPajamaStackExchange


Samuel Pepys' diary was the star attraction as the 350th anniversary of the mention of Chateau Haut-Brion was celebrated in Cambridge yesterday.
The original diary entry, dated 10 April 1663, was on display to guests in the Pepys library at Magdalene College, Cambridge, alongside a copy of an earlier reference to 'Hobrionno' in the 1660 cellar book of King Charles II, which is currently held in the National Archives.
The occasion was marked by a gala dinner hosted by the Cambridge University Wine Society, accompanied by a range of wines including Haut-Brion Blanc 2003, Chateau Haut-Brion 1999 and 1989, and featuring tributes to the relationship between Haut-Brion and both Pepys and Cambridge from Prince Robert of Luxembourg and Serena Sutcliffe MW.
The dinner also featured with a performance of Samuel Pepys' own song by Cambridge University Wine Society president Major David Beall.
The gala dinner followed an earlier lecture on Pepys and Wine, originally written by Dr Jane Hughes, a Pepys specialist, which explored Pepys' character as a man who regularly demonstrated a keen eye for a bargain, and who chose to write his diary in a coded shorthand to hide its contents from prying eyes.
Prince Robert also unveiled the Domaine Clarence Dillon History Challenge, a competition designed to uncover early historical mentions of Chateau Haut-Brion. All new historical quotations dating from before 1660 will receive 'an unspecified reward' and the overall winner will be announced in June 2014.


text:
Timothy Williams may refer to:

 Timothy Williams (author) (born 1946), British writer
 Timothy Williams (actor) (born 1967), American actor and screenwriter
 Timothy Williams (composer) (born 1966), British film composer for film, TV and video games
 Timothy Shaler Williams (1862–1930), American journalist and president of the Brooklyn Rapid Transit Company
 Timothy P. Williams, United States Army general
 Timothy Williams (computer programmer)
 Timothy Angus Williams, Canadian ambassador to Tunisia

See also
 Tim Williams (disambiguation)


text:
Sam McBride: SF's key demand could mean there is no inquiry into RHI scandal
Gerry Adams has become increasingly involved in the crisis in the absence of Martin McGuinness
As the RHI scandal has developed, Sinn Fein's decision to make its one consistent red line that Arlene Foster must stand aside has become increasingly peculiar.
But if Mrs Foster sticks to her entrenched position and Sinn Fein sticks to its demand, we are facing a snap election. In that scenario, there are three immediate implications for dealing with the RHI scandal – and all of them are negative.
While a section of the public clearly believes that Mrs Foster should stand down, there is no reason why her continuing in office would stop the truth about RHI emerging if, as Sinn Fein assures us, the inquiry would be entirely independent of the Executive.
Even if she did stand aside for the symbolic four weeks Sinn Fein has requested, Mrs Foster would remain DUP leader and whoever the temporary first minister was would be taking instructions from her.
If Sinn Fein does collapse Stormont, the first consequence would be that no independent investigation whatsoever would be undertaken into the RHI scheme – and even the inquiry by the Assembly's Public Accounts Committee would be ended.
Sinn Fein's aversion to setting up a full public inquiry under the Inquiries Act 2005 means that in order for its preferred form of investigation to have any teeth the Assembly will have to pass emergency legislation giving the inquiry power to compel witnesses and documents. It would also have to incorporate criminal sanctions for anyone refusing to co-operate in those areas.
That legislation is within the competency of the Assembly – almost identical provisions are contained within The Inquiry into Historical Institutional Abuse Act (Northern Ireland) 2013 which set up Sir Anthony Hart's public inquiry into child abuse in care homes.
Stipulations in the Assembly's standing orders mean that a bill giving an RHI inquiry those powers would take at least 10 days to make it through the legislature.
Even after that period, Royal Assent is needed before the bill becomes statute and that could take around two weeks.
The Assembly is scheduled to return from recess next Monday. Assuming that the bill has been drafted by then (which it may not be), and assuming the swiftest possible passage through the chamber, it is unlikely to become law until mid-February.
However, if Sinn Fein follows through on its threat to collapse the Executive over Mrs Foster's position, none of this can take place because there will be no legislative Assembly to pass the legislation.
In that scenario, the second consequence comes into play. Sinn Fein might then belatedly decide that although it was not its preferred option it would attempt to set up a full public inquiry.
However, by that stage that too would be impossible using the mechanisms now open to Stormont ministers.
N.I. flag fury after Union Jack erected on Rathfriland water tower
The Inquiries Act 2005 refers to "a Northern Ireland minister" as having the power to set up a public inquiry under its terms – which include key provisions such as evidence being taken under oath and powers of compulsion.
While there is a separate provision for the secretary of state setting up a public inquiry, there can be no guarantee that he would do so and to date he has attempted to stay as far out of the issue as possible.
The third – and financially significant – consequence for the RHI scandal if the Executive collapsed would be that attempts to slash the losses of the scheme would immediately flounder.
As with the emergency legislation for setting up an inquiry, the legislation which the DUP wants to use to rein in spending on RHI can only pass if there is a legislative Assembly in place.
Given those three fundamentally negative consequences for getting to the truth of the RHI scandal and cutting the losses to taxpayers, it is difficult to understand Sinn Fein's key demand if it is viewed through the prism of addressing the RHI debacle.
There are those in senior Stormont positions who believe that something much bigger is afoot. Certainly, Sinn Fein is under massive pressure from its grassroots over the perception that the DUP has been running rings around its Stormont operation long before this issue came to light.
It is also the case that Martin McGuinness is seriously ill and Gerry Adams is increasingly taking command in this crisis.
Does that explain why Sinn Fein's initial response to this issue was beyond restrained, but now it is talking up the crisis and seemingly pressing for a fresh round of crisis talks taking in everything from the Irish language to a Bill of Rights?
Tributes paid to Northern Ireland road racer Darren Keys following fatal crash at Walderstown
The Twelfth 2019 - 37 photographs that will make you proud to be from N.I.
Body recovered from N.I. river in search for missing person
These photos of a car parked on a N.I. beach went viral on Monday - here's why
N.I. dog dies in car in 20°C plus heat - members of public smash car window but animal dies a while later


text:
Racing Stock
RoseBrook Accomplishments
RoseBrook's list of accomplishments throughout the year are simply too numerous to list, and nearly impossible to keep updated... the horses bred and owned by RoseBrook are continually racking up wins at racetracks all over the world.
Here is a brief summary:
Over 20 Darley Awards INCLUDING Champion Breeder and International Champion Breeder
5-Time winner of the $100,000 Drinkers of the Wind Stakes (G1)
3-Time winner of the Florida 100
Breeder of the Winner of the Dubai Kahayla Classic
(Fryvolous in 2009 - VIDEO)
Breeder of the Winner of the world's richest Arabian horse race, the EUR 1.2 million Sheikh Zayed Bin Sultan Al Nahyan Jewel Crown (RB Burn in 2016 - VIDEO).
Winner of the Arabian Triple Crown (TH Richie in 2005)
Dreams are unique to every person, and so is the journey.
Dreaming big and doing it well is the RoseBrook Way.
We invite you to begin your journey with us.
... Because your dream is our dream.
Let's start writing your adventure today.
Interested in seeing more? Follow us on Facebook for daily updates on RB success in the United States and around the world!
© 2023 by RoseBrook Farm LLC


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List of tallest buildings in Montreal
This is a list of the tallest buildings in Montreal that ranks skyscrapers in the city of Montreal, Quebec, Canada, by height. There are currently 42 buildings and structures in Montreal greater than 100 m (328 ft). The tallest building in the city is the 51-storey, 205-metre-tall (673 ft), 1000 de La Gauchetière
Municipal regulations forbid any building from exceeding the height of Mount Royal, or 233 m (764 ft) above mean sea level.[1] Above-ground height is further limited in most areas and a minority of the downtown land plots are allowed to contain buildings exceeding 120 metres in height.[2] The maximum limit is currently attained by 1000 de La Gauchetière and 1250 René-Lévesque, the latter of which is shorter, but built on higher ground. To build higher than 1000 de La Gauchetière while respecting this limit would be to build on the lowest part of downtown; the maximum height there would be approximately 210 metres.[2]
The history of skyscrapers in Montreal began with the completion of the eight-storey-tall New York Life Insurance Building in 1888.[3] Most high-rise construction in Montreal occurred in two periods: the late 1920s to the early 1930s and then from the early 1960s to the early 1990s.
In the 21st Century, the rate of high-rise construction in the city increased again with more under construction in 2014 than in any other North American City bar Toronto and New York.[4] Recently completed are the 50-storey (184 m) L'Avenue, 50-storey (167 m) Tour des Canadiens, 40-storey (147 m) phase 1 of Roccabella. And still under construction are the 39-storey (146 m) Icône, and 40-storey Tom Condos (122 m).
1 Tallest buildings
2 Tallest under construction, proposed, and approved
2.1 Under construction
2.2 Approved
3 Timeline of tallest buildings
Tallest buildings[edit]
This list ranks Montreal skyscrapers that stand at least 122 metres (400 ft) tall, based on standard height measurement. This includes architectural details but does not include spires or antenna masts. An equal sign (=) following a rank indicates the same height between two or more buildings. The "Year" column indicates the year in which a building was completed. Freestanding observation and/or telecommunication towers, while not habitable buildings, are included for comparison purposes; however, they are not ranked. One such tower is the Tour de Montréal.
m (ft)
1000 de La Gauchetière
205 (673) 51 1992 This is the tallest building in Montreal by roof height and tallest in Canada east of Toronto. It reaches the maximum height allowed by the city within viewing distance of the Mount Royal look off point.
1250 René-Lévesque
199 (653) 47 1992 Originally known as the IBM-Marathon Tower. With its spire included it measures 226 m (743 ft).
Tour de la Bourse
194 (637) 47 1964 The tallest building in Canada until completion of the Toronto-Dominion Bank Tower in Toronto. The tallest reinforced concrete building in the world until the completion of Lake Point Tower in Chicago. The tallest building constructed in Montreal in the 1960s.[5][6]
Place Ville-Marie
188 (617) 47 1962 The tallest building in Canada until completion of the Tour de la Bourse.[7][8]
CIBC Tower
187 (614) 45 1962 With its antenna included it measures 225 m (738 ft). The tallest building in Canada and the whole British Commonwealth when completed in 1962. Surpassed within a year by Place Ville-Marie.[9][10]
184 (603) 50 2017 The tallest residential tower in Montreal, and the tallest residential tower in Canada east of Toronto. It contains 304 condominium apartments, along with an important mixed use component; a Provigo branded grocery store, four office floors, and ground floor retail.
Tour de Montréal
175 (574) 20 1987 The Tour de Montréal is the tallest inclined tower in the world.[12][N 1]
Tour des Canadiens
167 (548) 50 2016
Tour des Canadiens 2
166 (545) 53 2019 Topped out.
1501 McGill College
158 (519) 36 1992 [13][14]
Complexe Desjardins South Tower
152 (499) 40 1976 The tallest building constructed in Montreal in the 1970s.[15][16]
Roccabella (East Tower)
147 (482) 40 2016 Roccabella east and west towers are the tallest twin towers in Montreal.
Roccabella (West Tower)
147 (482) 40 2018 Topped out. Roccabella east and west towers are the tallest twin towers in Montreal.
KPMG Tower
146 (479) 34 1987 Originally known as Maison des Coopérants and later as Place de la Cathédrale. The tallest building constructed in Montreal in the 1980s.[17][18]
Marriott Château Champlain
139 (454) 36 1967 Tallest hotel in Montreal.[19][20]
Le V / Courtyard Marriott Hotel 138 (453) 40 2014 [21][22]
Telus Tower
136 (445) 34 1962 Originally known as CIL House.[23][24]
500 Place D'Armes
133 (435) 32 1968 Originally known as Tour Banque Canadienne Nationale.[25][26]
Deloitte Tower
133 (435) 26 2015 [27]
Complexe Desjardins East Tower
Scotia Tower
National Bank Tower
128 (420) 28 1983 National Bank Tower and 700 de la Gauchetière are the tallest twin office towers in Montreal.[32][33]
700 de la Gauchetière
128 (420) 28 1983 Le 700 de la Gauchetière and Tour de la Banque Nationale are the tallest twin office towers in Montreal. Previously known as Bell Canada Tower.[34][35]
1000 Sherbrooke West
Terminal Tower
Bell Media Tower
125 (410) 30 1988 Originally known as Montreal Trust Place and Maison Astral.[40][41]
Altitude Montreal
124 (407) 33 2013 Was the tallest residential building in Canada east of Toronto until 2016.[42][43]
Sun Life Building
122 (400) 26 1931 The tallest building in Montreal from 1931 until the completion of Tour CIBC in 1962. The tallest building in the city completed prior to the 1960s.[44][45]
Le Port Royal
Tom Condos
122 (400) 40 2018 Topped out.[48][49]
Tallest under construction, proposed, and approved[edit]
Under construction[edit]
The following is a list of buildings that are under construction in Montreal and are planned to rise at least 100 metres (328 ft).
m / ft
1 Victoria sur le Parc 200 / 656 56 2023 Mixed-use, office, residential and retail. Began construction March 2019
2 800 Saint-Jacques West (National Bank HQ) 200 / 656 46 2022 Office building. Began construction March 2019
3 Tour des Canadiens 3 170 / 558 55 2021 Residential. Began construction March 2018
4 Tour des Canadiens 2 170 / 558 54 2019 Residential. Topped out.
5 Humaniti 120/ 393 39 2020 Mixed-use, office, residential, hotel. Began construction July 2017
6 YUL Condos (East Tower) 120 / 393 38 2020 Residential. Began construction November 2017
7 Quinzecent 120 / 393 36 2022 Residential. Began construction March 2019
8 Appartements Dorchester 120 / 393 36 2021 Residential. Began construction March 2019
9 628 Saint-Jacques 120/ 393 35 2021 Residential. Began construction May 2017
10 Hotel Art de Vivre 120 / 393 35 2021 Mixed-use, Hotel, residential. Began construction August 2018
Approved[edit]
The following is a list of buildings that are approved in Montreal and are planned to rise at least 100 metres (328 ft).
1 Icône (Phase 2) 107 / 351 27 2018 Mixed-use, hotel and condos. Stale proposal.
Proposed[edit]
The following is a list of buildings that have been proposed, but not approved yet, in Montreal and are planned to rise at least 100 metres (328 ft).
1 1 Square Phillips 200 / 656 61 2024 Residential and mixed use
2 Maestria - Tower A 198 / 651 58 2023 Condos
3 Maestria - Tower B 185 / 606 58 2023 Condos
4 Solstice 150 / 490 44 2021 Condos
5 750 Peel (2 towers) 180/590 & 130/426 35 2024 To be announced soon.
6 1000 de la Montagne 143 / 471 45 2022 Condos
7 600 Peel (3 towers) 120 / 393* 35 — To be announced soon.
* denotes an estimation in height.
Timeline of tallest buildings[edit]
The Notre Dame Basilica was the tallest building in Montreal from its construction in 1829 to 1928.
The Royal Bank Building, the tallest building in Montreal from 1928 to 1931 and the first building to exceed the Notre-Dame Basilica in height.
This lists buildings that once held the title of tallest building in Montreal.
Name Street address Years as tallest # of Years as tallest Height
m / ft Floors Reference
Notre Dame Basilica 110 Notre-Dame Street West 1829–1928 99 years 69 / 226 7 [50][51]
Royal Bank Building 360 Saint Jacques Street West 1928–1931 3 years 121 / 397 22 [52][53]
Sun Life Building 1155 Metcalfe Street 1931–1962 31 years 122 / 400 26 [44][45]
Tour CIBC 1155 René Lévesque Boulevard West 1962 <1 year 187 / 614 45 [9][10]
Place Ville Marie 1 Place Ville-Marie 1962–1964 2 years 188 / 617 47 [7][8]
Tour de la Bourse 800 Victoria Square 1964–1992 28 years 194 / 637 47 [5][6]
1000 de La Gauchetière 1000 De La Gauchetière Street West 1992–present 25 years (current) 205 / 673 51 [54][55]
Architecture portal
Architecture of Montreal
List of tallest buildings in Canada
Facades inspection
^ The Tour de Montréal is not a habitable building, but is included in this list for comparative purposes. Per a ruling by the Council on Tall Buildings and Urban Habitat, freestanding observation towers are not considered to be buildings, as they are not fully habitable structures.
^ "Preserve the character of Mount Royal and its predominance in the urban landscape". Ville de Montréal. Retrieved March 10, 2013.
^ a b "Height limits" (PDF) (in French). Ville de Montréal. September 2009. Retrieved 6 May 2010.
^ "Place d'Armes, Montreal". A View on Cities. Retrieved 6 May 2010.
^ "Skyscraper gap in N. America narrows, but Toronto leads with 130 - CBC News". cbc.ca.
^ a b "Tour de la Bourse". Emporis.com. Retrieved 2009-11-23.
^ a b "Tour de la Bourse". SkyscraperPage.com. Retrieved 2009-11-23.
^ a b "1 Place Ville-Marie". Emporis.com. Retrieved 2009-11-23.
^ a b "1 Place Ville-Marie". SkyscraperPage.com. Retrieved 2009-11-23.
^ a b "Tour CIBC". Emporis.com. Retrieved 2009-11-23.
^ a b "Tour CIBC". SkyscraperPage.com. Retrieved 2009-11-23.
^ "IBI-Group Highrises". dailycommericalnews.com. Retrieved 2017-02-28.
^ "Tour de Montréal". Emporis.com. Retrieved 2009-11-23.
^ "1501 McGill College". Emporis.com. Retrieved 2009-11-23.
^ "1501 McGill College". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Complexe Desjardins South Tower". Emporis.com. Retrieved 2009-11-23.
^ "Complexe Desjardins South Tower". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Tour KPMG". Emporis.com. Retrieved 2009-11-23.
^ "Tour KPMG". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Marriott Château Champlain". Emporis.com. Retrieved 2009-11-23.
^ "Marriott Château Champlain". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Courtyard Marriott Montréal Centre-ville". Emporis.com. Retrieved 2013-08-12.
^ "Le V Courtyard Marriott, Montreal". SkyscraperPage.com. Retrieved 2013-08-12.
^ "Tour Telus". Emporis.com. Retrieved 2009-11-23.
^ "Tour Telus". SkyscraperPage.com. Retrieved 2009-11-23.
^ "500 Place D'Armes". Emporis.com. Retrieved 2009-11-23.
^ "Banque Canadienne Nationale". SkyscraperPage.com. Retrieved 2009-11-23.
^ http://www.marketwired.com/press-release/deloitte-unveils-workplace-of-the-future-in-new-montreal-office-tower-2056676.htm
^ "Complexe Desjardins East Tower". Emporis.com. Retrieved 2009-11-23.
^ "Complexe Desjardins East Tower". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Tour Scotia". Emporis.com. Retrieved 2009-11-23.
^ "Tour Scotia". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Tour de la Banque Nationale". Emporis.com. Retrieved 2009-11-23.
^ "Tour de la Banque Nationale". SkyscraperPage.com. Retrieved 2009-11-23.
^ "700 de La Gauchetière". Emporis.com. Retrieved 2009-11-23.
^ "Tour Bell Canada". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Centre Mont-Royal". Emporis.com. Retrieved 2009-11-23.
^ "Centre Mont-Royal". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Le 800". Emporis.com. Retrieved 2009-11-23.
^ "Tour Terminal". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Place Montreal Trust". Emporis.com. Retrieved 2009-11-23.
^ "Place Montreal Trust". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Altitude Montreal". SkyscraperPage. Retrieved 2011-04-03.
^ Altitude Montéal at Emporis
^ a b "Sun Life Building". Emporis.com. Retrieved 2009-11-23.
^ a b "Sun Life Building". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Le Port Royal". Emporis.com. Retrieved 2009-11-23.
^ "Le Port Royal". SkyscraperPage.com. Retrieved 2009-11-23.
^ "Tom Condos". Emporis.com. Retrieved 2018-01-24.
^ "Tom Condos". SkyscraperPage.com. Retrieved 2018-01-24.
^ "Notre-Dame Basilica". Emporis.com. Retrieved 2009-11-29.
^ The Basilica of Notre-Dame de Montreal at IMTL.org
^ "Royal Bank Building". Emporis.com. Retrieved 2009-11-29.
^ "1000 de La Gauchetière". Emporis.com. Retrieved 2009-11-23.
^ "1000 de La Gauchetière". SkyscraperPage.com. Retrieved 2009-11-23.
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Maison Radio-Canada
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La Laurentienne Building
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Intercontinental Montreal Hotel
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Evolo 1
2250 Guy
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628 Saint-Jacques
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Landmarks of Montreal
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Church of St. Michael and St. Anthony
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Notre-Dame Basilica
Saint James United Church
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Tour CIBC
Complexe Desjardins
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Lachine Canal
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Category:Montreal
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Retrieved from "https://en.wikipedia.org/w/index.php?title=List_of_tallest_buildings_in_Montreal&oldid=905909135"
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Emporis template using building ID


text:
Q: Prepare data for Haversine distance I want to calculate haversine distance for every unique value of id:
from haversine import haversine, Unit

data =

id latitude longitude
a        11        22
a        33        44
b        55        66
b        77        88
c        99        11
c        12        13

start = (11,22) # (lat, lon)
end = (33,44)
haversine(start,end)*1000

How to create a loop for it? Because real data has 2000+ ids
There are good questions about Hvaersine but there is no answers concernning data preparation for that. This question is mostly about data preparation

A: 2000 isn't that much, you can process it with a simple python loop. But if you'd prefer more pandas-native approach you can do the following:
df.groupby('id').apply(lambda g: haversine(g.iloc[0], g.iloc[1])) * 1000

considering that your dataset consistently has a pair of points for each id.
Output:
id
a    3.320380e+06
b    2.601419e+06
c    9.673377e+06


As you may remember, my two daughters and I are on a 5 and 1/2 week family road trip. We are having a great time and enjoying seeing many new states and attractions along the way. Today, I wanted to share our second week on the road with you.
I'm hoping that by sharing our trip with you, you can gain a few ideas of things to do on your next road trip. Just in case you missed it, I share our first week on the road last week, with all the fun we found from Viriginia to North Dakota. I also have posts of driving through the South and Southwest last year in part 1 and part 2 of our 2014 road trip.
We started our second week on the road by entering Montana. We also spent most of the 2nd week in Montana. Again, no visitor center. It is so disappointing how many states don't have visitor centers with a sign for state line pictures. We found a magazine as a stand in again.
We have now hit 2,000 miles!
At one point in my life, I briefly flew as a flight attendant with Horizon Air. I flew into small cities all over the Pacific Northwest. One of the cities that I always swore I would visit again, was Kalispell, Montana.
Last year when my daughters said they wanted our road trip this year to be to the Northwest, I knew we had to stop in Montana for a bit of time on our trip.
I ended up having a very hard time finding a place to stay in Kalispell that fit my budget. We ended up staying in Whitefish, Montana. We are so glad we did. We really enjoyed Whitefish.
We stayed at the Stillwater Mountain Lodge. We were the only people at the lodge during our visit and therefore the only people on the property…with no cell signal. That was a little unnerving for this city girl, but our time there was fantastic!
Right near the lodge (within walking distance), is the Whitefish Trail in the Stillwater State Forest. There are plenty of trails for hiking, which we enjoyed a few of the days we were there.
About 20-30 minutes from our lodge was the Whitefish Mountain Resort. There is SO much to do at this ski resort in the summer! Seriously, if you had the time and the money (because a lot of the activities are quite pricey IMO), you could spend a whole week right here.
We had fun riding the Alpine slide! I managed to take some video while sliding down.
Mountain Life Photography is right there at the end of the slide to get some great pictures!
(affiliate link) said we did 73 flights of stairs this day. They totally impressed me!
Of course, I had to try to get a few mountain top flower pictures as well. It was so pretty up at the top of the mountain. I liked hiking in a bit of a more well used path because one of my daughters was quite scared of the idea of seeing bears.
Sadly, our time in Montana came to a close all too quickly. I will add that if you visit Whitefish, we highly recommend Second Street Pizza and Sweet Peaks Ice Cream. Both were delicious and had great employees.
Once again, Idaho disappointed with no visitor center for a state line picture. We didn't even see a welcome to Idaho sign. The freeway was under construction, so that may have been the problem.
Not too far into Idaho, we stopped for a rest area that had this whole big interactive display about mining and the mining industry. It was a great place to stretch our legs and run around for a short bit before driving on. Unfortunately, I don't know the name of the city that this was in and I've Google searched and am not certain which town it was.
Just thought you would want to see what I see for thousands of miles. 🙂 My trusty 2002 Toyota Highlander with 175,000 miles on it is doing fantastic on our trip. Thank you, Toyota!
We were SO excited to pass into Washington state! It meant we had really gone from coast to coast on this trip. Unfortunately, no visitor center again and this is the ridiculously awful picture I got of the welcome sign. Oh well…I'm proudly displaying this horrible picture because…WE DID IT!!!
If you ever spend time in Spokane, Washington, you have to visit Riverfront Park. We found it because I found this big red wagon through my Roadtrippers app. I was so dorkily excited to see this big red wagon. The handle is a slide! It was too hot to go down a metal slide the day we were there.
There are tons of things to do at the park and tons of shopping and a Starbucks all within walking distance.
The park has the fantastic bench along with other great metal sculptures and an early 1900's carousel.
Just as we were about to leave, we noticed a gondola ride that said it went over the Spokane falls. We had to go for it.
The Spokane falls were pretty. I have a video of them in my Instagram feed if you want to hear the falls.
While we enjoyed the gondola ride, I didn't realize you could walk down to a side view-point. It probably would have been just as nice and saved me some money.
Headed toward Tacoma, Washington, we hit 3,000 miles!
There is a Wild Horses Monument in Central Washington. My youngest daughter LOVES horses, so we had to stop. In order to really get pictures, you have to climb up that mountain. It is quite steep with lots of loose dirt. It was a very hot and sunny day. Sunscreen and a water bottle were definitely needed.
The views from the top are spectacular. That is the Columbia river in the background.
The metal horse sculptures are really unique and neat. Unfortunately, almost all of them have graffiti on them.
While at the parking lot or up on top of the mountain, take a few moments to enjoy the views of the Columbia River Gorge.
Lastly on our drive to Tacoma, we stopped to see the Snoqualmie falls. They are HUGE falls that are also used for hydro-electric energy. Such a pretty sight!
Right near the falls, I was able to pick up the next Starbucks You are Here mug on my journey. The Seattle mug.
The next week, we are spending in Tacoma/Seattle and then on to Portland. Can't wait to share the fun with you!
I enjoyed your trip. My hubby, 3 pugs, & I drove to Vermont, New Hampshire, & Maine. Next year will be the same. I noticed many places now have welcome stations in the towns to get you into town. It was a big thing in PA even 10 years ago. Motels, too. Though ME,was terrible but our daughter from our last Army tour knows the area well so it helped she was there. I would love a trip to stop, take pictures, & enjoy. Maybe next year. No 13 hour driving days! Plus no staying in Harrisburg/Hershey.
Thank you! Sounds like you need to plan a fun trip to stop, take pictures and enjoy the drive. 13 hour driving days stink! We try our best to stop and smell the roses while we are on the road. It makes it more fun! Best of wishes to you on your next road trip!


Dr Helen Czerski is one of our leading science communicators. She is a physicist based at University College, London, and she has presented many science programmes for the BBC. Her scientific research is into ocean bubbles and their influence on the atmosphere. She presented a programme on the science of bubbles for the BBC and she has presented programmes on many other aspects of science including on extreme weather and climate change.
Prof. Dame Julia Slingo OBE FRS is the Chief Scientist at the Met Office. She is a world leading expert in tropical climate variability and cumulus convection, its influence on the global climate and its role in seasonal and decadal climate prediction. She has led the development of a new generation of high resolution climate models. She leads the development of the scientific strategy ant the Met Office, provides advice to government, and is often asked to talk about weather and climate science and its implications by the media.
Dr. Peter Stott is a climate scientist at the Met Office Hadley Centre, specialising in the observations and understanding of past climate variability and change. He is particularly interested in the question of how extremes of weather and climate are changing with global warming and co-edits an annual report that looks back at extreme events of the previous and puts them into the context of climate variability and change. He has worked on the reports for the Intergovernmental Panel on Climate Change and has often been asked to talk about his science and its implications for radio and television programmes. Peter is a founding member of WAM.
Prof. Peter Cox is Professor of Climate System Dynamics at the University of Exeter. His research into carbon cycle feedbacks, the process which can lead natural vegetation to release more carbon to the atmosphere and therefore exacerbate climate change, has led to some very high profile scientific publications. He has appeared in a number of television documentaries on climate change including with Sir David Attenborough.
Neil Adger is Professor of Human Geography. He teaches, supervises graduate students, and researches in the areas of environmental geography, ecological and institutional economics, and global environmental change. Neil is a on the list of social scientists in ISI Highly Cited Researchers, one of the few geogrpahers whose work is widely cited across the disciplines. He is a member of the Resilience Alliance, a global network of leading scientists and social scientists working on theory and practice of resilience of social-ecological systems for sustainability.
Prof. Paul Hardaker is Chief Executive of the Institute of Physics. Before taking up this position in 2012 Paul was Chief Executive of the Royal Meteorological Society and before that worked for many years at the Met Office. He is one of the founding members of WAM and chaired a very lively Climate Change Question Time at the first WAM festival in Reading in 2012.


text:
A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients
Susan Quach1,
Deirdre A Hennessy1,2,
Peter Faris1,
Andrew Fong1,
Hude Quan1 &
Christopher Doig1,2
Risk adjustment and mortality prediction in studies of critical care are usually performed using acuity of illness scores, such as Acute Physiology and Chronic Health Evaluation II (APACHE II), which emphasize physiological derangement. Common risk adjustment systems used in administrative datasets, like the Charlson index, are entirely based on the presence of co-morbid illnesses. The purpose of this study was to compare the discriminative ability of the Charlson index to the APACHE II in predicting hospital mortality in adult multisystem ICU patients.
This was a population-based cohort design. The study sample consisted of adult (>17 years of age) residents of the Calgary Health Region admitted to a multisystem ICU between April 2002 and March 2004. Clinical data were collected prospectively and linked to hospital outcome data. Multiple regression analyses were used to compare the performance of APACHE II and the Charlson index.
The Charlson index was a poor predictor of mortality (C = 0.626). There was minimal difference between a baseline model containing age, sex and acute physiology score (C = 0.74) and models containing either chronic health points (C = 0.76) or Charlson index variations (C = 0.75, 0.76, 0.77). No important improvement in prediction occurred when the Charlson index was added to the full APACHE II model (C = 0.808 to C = 0.813).
The Charlson index does not perform as well as the APACHE II in predicting hospital mortality in ICU patients. However, when acuity of illness scores are unavailable or are not recorded in a standard way, the Charlson index might be considered as an alternative method of risk adjustment and therefore facilitate comparisons between intensive care units.
In health services research, risk adjustment is important to account for variations in case-mix that might affect assessment of outcomes, therefore allowing comparisons to be made between health care providers and health systems locally and globally. In the intensive care unit (ICU), risk adjustment and mortality prediction has usually been performed using severity score taxonomies such as the Acute Physiology and Chronic Health Evaluation (APACHE) score, the Simplified Acute Physiology Score (SAPS) or the Mortality Prediction Model (MPM) [1–3], and their updated derivatives [4–8]. These systems emphasize the severity of physiologic derangement, but also include, to a variable degree and weight, some measure of pre-existing illness. For example, the APACHE II system assigns 0, 2 or 5 chronic health points (CHP) for pre-existing co-morbidity out of a possible 73 total points [4]. Since the emphasis of all these systems is the measurement of physiological derangement, the small consideration of pre-existing disease may not adequately account for the quantitative or qualitative contribution of co-morbid illness, thus limiting the ability of these models to accurately predict mortality [9].
A significant limitation to severity of illness scores is that data collection is labour intensive and expensive, and the necessary data to calculate these scores are unlikely to be found in administrative databases. Therefore, the utility of these scores in risk adjustment is limited to prospective studies including clinical trials. It is unlikely that these scores can be accurately calculated for use in retrospective studies such as for benchmarking, quality assurance, or to examine differences in service delivery within a city, country or between countries. Other systems, used widely outside critical care, have been developed to predict patient outcomes based solely on the presence of predefined existing co-morbidities. For instance the Charlson index assigns weighted scores to 17 co-morbidities ranging from chronic heart failure to HIV infection [10]. Classification within systems like the Charlson index originally required a chart review, and therefore was also costly and labour intensive. However, recent work has validated the determination of these scores from administrative data, permitting quick, easy, and inexpensive calculation. Therefore, these scores can be determined for prospective and retrospective studies.
Small studies have suggested that the Charlson index may perform comparably or better than APACHE II for mortality prediction in an ICU population [11, 12]. Poses et al [11] suggested that the Charlson index could improve prognostic predictions in ICU patients when added to the APACHE II. However, their study was conducted in only one hospital with data collected manually by chart review over a short interval. There is little data on the validity and utility of the Charlson index, calculated using administrative data, as a risk adjustment method for ICU patients [13]. Therefore, the purpose of this study was to compare the performance of the Charlson index alone and in combination with components of APACHE II for predicting hospital mortality among adult ICU patients.
The Calgary Health Region (CHR) exclusively provides in-hospital acute care to all residents of the Calgary Health Region (city of Calgary and approximately 20 nearby small towns, villages, and hamlets total population approximately 990,000 in 2002). The CHR Department of Critical Care Medicine provides care to critically ill patients in three closed multidisciplinary and 1 closed cardiovascular intensive care units (ICUs). The base study population consisted of all adult (>17 years) CHR residents admitted to any multidisciplinary ICU in the CHR between April 1st 2002 and March 31st 2004. Cardiovascular surgery patients (n = 2541) were excluded because they differed from other ICU patients with respect to their physiologic derangement, mortality rate, and the utility of the APACHE II system for predicting mortality in this subgroup. During the study period, the first admission of a patient to the ICU and their subsequent hospital stay was considered the index admission. All data were analyzed for the index admission only. The study was approved by the local human research ethics committee. Individual patient consent was waived as the final study sample contained no individual patient identifiers following the linkage described below.
Design and Data Sources
This study used a population-based cohort design that linked clinical and outcome data from two large administrative databases. Demographic, physiological, diagnostic and treatment data for study participants during their ICU stay were recorded prospectively using the Quantitative Sentinel (QS) 6.6.9 Clinical Information System (GE Marquette Medical Systems INC., Milwaukee, WI) and stored in a locally developed longitudinal relational database called ICU Tracer, which is built on an Oracle Enterprise platform (Oracle Corporation, Redwood Shores, CA). Details about the Tracer database have been previously published [14]. Patients were identified in the Tracer database, and their demographics and clinical data were then linked to hospital discharge data obtained from the CHR administrative database through a deterministic record linkage using last name, date of birth, ICU admit date, ICU discharge date and unique provincial healthcare number (PHN). The administrative hospital discharge data contained up to 16 secondary diagnosis codes and types as well as 10 procedure codes, which were used to derive the Charlson index. Using the ICD-10-CM code taxonomy to identify diagnoses and procedures, we excluded the diagnosis type with M (main diagnosis) or a 2 (post admit co-morbidity) because these conditions were the cause or consequence of the hospital admission, and not a pre-existing comorbidity. The linkage rate was 99%. All individual patient personal identifiers were stripped following linkage.
Main Outcome Measure and Variables
The primary outcome was all cause hospital mortality. The APACHE II score was collected prospectively at the time of ICU admission in accordance with the original published recommendations [4]. At ICU admission, the attending critical care physician, using a standardized automated entry system with on-line operational definitions, determined the diagnosis responsible for ICU admission, the Glasgow coma score (based on the best assessment of neurological status excluding any potential confounding effect of medications), and the CHP component. The physiologic and laboratory data required to calculate the acute physiology score (APS) component of the APACHE II were obtained by automated electronic download from physiologic monitors or laboratory information systems. Variables from physiologic monitors required validation by nursing staff prior to download. The calculation of APS was based on the worst value for each variable within the 24 hours of ICU admission. The Charlson index consists of 17 co-morbidities, which are weighted and summed to produce a score [10]. We used the ICD-10 coding algorithms developed by Quan et al. [15] to derive a co-morbidity score for each patient. In this multi-step process, ICD-10 coding algorithms were developed by translating the ICD-9-CM codes derived from Deyo's method [16]. The validity of the algorithms for ICD-10 coding from administrative data has been previously reported [15].
Baseline characteristics including the frequency of Charlson comorbidities were calculated for the cohort of patients. Univariate analysis was used to compute the C-statistic for models containing CHP, Charlson index score and APACHE II alone. Four separate nested multivariate logistic regression models were constructed. The baseline model consisted of age, sex and APS (Model A). To this baseline model, were then added individually CHP (Model B), the Charlson co-morbidities as individual entities (Model C), and the Charlson index as a weighted score in the same categories as suggested by D'Hoore (1–2, 3–4, 5–6, >6) (Model D) [17]. Finally, as the relationship between the Charlson index and the log odds of death was non-linear the Charlson index was recoded excluding scores of 6 and above (Model E). Finally, the discriminative ability of the full APACHE II score (Model F) was examined with the addition of the weighted Charlson index score (Model G). The C-statistic was used to discriminate between patients who would live and die and represents the area under the curve for the probability that the mortality status of a randomly selected survivor and non survivor is identified correctly by a given prediction model [18]. C-statistics of 0.8 to 0.9 are regarded as very good, 0.7 to 0.8 are considered adequate and C-statistics below 0.7 are regarded as poor [19]. To test whether the addition of co-morbidity variables improved the explanatory ability of the nested models changed significantly, likelihood ratio tests (LR) were used [20]. The LR test was computed as twice the difference between the log-likelihood from the two models being compared, and the statistic derived was compared to a χ2 (chi-squared) distribution for significance testing. Since model performance can be degraded by inclusion of many predictors, we calculated Akaike's Information Criterion (AIC). This statistic provides a method for penalizing the log likelihood to gain an unbiased assessment of the model's performance after inclusion of additional predictors. To calculate the AIC, we added twice the number of parameters to the -2 log likelihood [20]. Goodness of fit was assessed by graphical means and by the Hosmer and Lemeshow's Goodness of Fit test. Finally, Efron's enhanced bootstrapping method was performed to estimate the amount of shrinkage in the models [21]. Finally, we explored the effect of re-weighting the individual co-morbidities within the Charlson index based on their odds ratio of being associated with hospital mortality. All statistical analyses were preformed using SAS version 9.1 (Cary, NC) and STATA 9.2 (College Station, Texas).
During the two year study period, there were 3778 eligible index admissions into the multi-disciplinary ICUs. Patient characteristics are described in Table 1. Patients had an average age of 56.9 years, with a slightly higher percentage of males (57.0%). The mean (± SD) admitting APACHE II and APS scores were 19.6 (± 8.6) and 15.2 (± 7.7) respectively. The ICU and hospital mortality rates were 18.9% and 27.2% respectively. According to the CHP classification 26.5% of patients had severe chronic health problems. The Charlson index was highly skewed with 77.0% of the observations between 0 and 2. Forty one percent of patients (41.3%) had no co-morbidities, 28.8% had a single co-morbidity, 25.6% had two or three co-morbidities and 3.7% of patients had four or more co-morbidities.
The prevalence of co-morbidities, the Charlson weight, and the association of the co-morbidity with hospital mortality are presented in Table 2. The most common co-morbidity was either chronic pulmonary or cardiac disease, followed by diabetes. The only disease with a prevalence of less than 1% was known pre-existing seropositive HIV status. In the original Charlson classification, co-morbidities with odds ratio associated with mortality of ≥1.2<1.5 were assigned a weight of 1, conditions with a ratio of ≥1.5<2.5 a weight of 2, conditions with ≥2.5<3.5 a weight of 3, conditions with ≥3.5<4.5 a weight of 4, and those conditions with weights of 6 or more were assigned a weight of 6. A 'recoded' ICU weight based on the odds-ratio of the co-morbidity demonstrated in this study sample that 7 of the co-morbidities would be assigned a weight of zero, 3 co-morbidities (cerebrovascular disease and liver disease (mild and moderate or severe)) would have a larger weight, 4 variables would have smaller weights (cancer and metastatic cancer, HIV seropositive status, and paraplegia-hemiplegia) and only 2 co-morbidities would not change their weight (myocardial infarction and peripheral vascular disease).
Table 2 Estimation of logistic regression parameters to predict in-hospital death
Table 3 displays the models constructed to predict hospital mortality. The CHP component of APACHE II alone was not a good univariate predictor of hospital mortality (C = 0.594). The Charlson index was a slightly better predictor (C = 0.626). In contrast, the full APACHE II was a good predictor of hospital mortality (C = 0.808). No further gain in discrimination was detected when the Charlson index was added to the full APACHE II model (C = 0.808 to C = 0.8135). In the nested multivariable models, the baseline model of age, sex and APS (Model A) performed reasonably well (C = 0.743). In each of the 3 subsequent nested models (Models B, C, or D) which added a measure of chronic health status to the baseline model, a statistically significant improvement in the model's discriminative ability to predict hospital death was observed (Model B, C = 0.757, Model C, C = 0.768, Model D, C = 0.752, p < 0.0001 each Model relative to Model A). However, the increase in discriminative ability would be considered of minimal practical significance. The Charlson index scores above 6 did not demonstrate a linear relationship with the log odds of death (Figure 1). Therefore, a post-hoc modification was performed where individual subjects with a Charlson index score of 6 and above were excluded (Model E). This analysis also demonstrated a statistical (C = 0.757) but not practical improvement in the discriminative ability to predict hospital death compared to the baseline model. Model B (assessment of the contribution of individual co-morbidities) was the most complex model with the largest AIC of 100.6. In contrast, when the Charlson index weighted scores were included (Models C and D), there was only a moderate increase (AIC = 38.90, AIC = 9.92 respectively).
The relationship between the odds of death for the Charlson score and categories was linear for index scores of 4 or less, but this relationship was no longer consistent for scores above 4.
Table 3 Model Performance for Predicting In-hospital Mortality
Figure 2 shows a scatter plot of the expected vs. observed number of deaths for the Charlson index using the D'Hoore cut points (Model D). Data points falling on the 45 degrees line indicate perfect calibration. Most of the data points fell on or close to the line indicating fairly good calibration; both Models B and C had similar graphs (not shown).
This graph is a plot of observed and expected risk groups for each decile of in-hospital mortality. Observed points falling on the line show good calibration for Model D. Points falling above the line show that the model underestimated the actual risk of death.
The objective of this study was to assess the performance of the Charlson index alone and in combination with components of the APACHE II for predicting hospital mortality among adult ICU patients. Our results show as expected that the full APACHE II model performed the best of all models in predicting hospital mortality. The discrimination of the APACHE II model in our data (C = 0.808) was similar to the results from other large studies of similar duration and size [22], and similar to the results of other specific ICU acuity of illness taxonomies such as SAPS or MPM. A baseline model of age, sex, and APS provided good discriminative ability to predict mortality (C = 0.743). The addition of the Charlson co-morbidity components added little to either the baseline model, or to the full APACHE II model. Further, these results demonstrate and it is reasonable to conclude that adding or replacing the APACHE II CHP with the Charlson index provides no practical improvement to the discriminative ability of such a predictive model (C = 0.808 vs C = 0.813). Our results are similar to those published by Ho et al. These investigators from Royal Perth Hospital (which provides ICU care to a large region of Western Australia) demonstrated that more comprehensive measures of co-morbidity did not significantly improve the discrimination of the APACHE II score [23].
It may not be surprising that the Charlson index did not perform as well as APACHE II. APACHE II was originally developed and validated in ICU patients, whereas the Charlson index was developed using 1 year survival data on medical in-patients, with subsequent analysis assessing its ability to predict survival over 10 years for breast cancer patients. Since the population groups and outcomes are different, the weights applied in the Charlson index may not be appropriate for critically ill patients. For example, our data demonstrated that only 10/17 Charlson co-morbidities were associated with an increased risk of hospital death. Myocardial infarction and peripheral vascular disease were the only 2 co-morbidities that had the same original Charlson weights. Co-morbidities with an infrequent prevalence (less than 3%) such as dementia, connective tissue-rheumatic disease, and peptic ulcer disease were not associated with mortality, while HIV was assigned a much smaller weight compared to the original Charlson index. It may be that some of the co-morbidities, such as dementia, were associated with a clinical decision to exclude ICU admission, but our data can not ascertain if this form of selection bias was present, or if present, its potential magnitude. Another alternate explanation is that since the Charlson index was derived in 1987, advances in the treatment and therefore risk of mortality for some co-morbidities, for instance HIV/AIDS, has changed. Therefore, the current weights applied in the Charlson index may not be appropriate.
This study showed the Charlson index had a non-linear relationship with the odds of mortality, results similar to a study in ischemic heart patients [17]. The distribution of Charlson index scores in our results was skewed, with 77.23% of patients assigned a score of two or less. The small number of ICU patients in our sample with high Charlson index scores may explain why the estimate was unstable in this subset. Our results differ from those of Poses et al., who demonstrated that the Charlson index, as a univariate predictor, had a linear relationship with hospital mortality for ICU patients [11]. The disparity in results may be due to their small sample size, (N = 201), or differences in the methods of calculating the Charlson Index as their results report a labor intensive chart review rather than the use of administrative data [11].
The present study had several strengths. All critically ill patients admitted to an ICU in the CHR during the study period were included in the sample, therefore reducing susceptibility to selection bias from referral, but not necessarily excluding bias in cases where critically ill patients were refused admission to ICU based on underlying co-morbidities and probability for death. Unfortunately, we do not have a method of determining if this occurred. Over-fitting of the models was reduced because we excluded complications using the diagnosis type indicator in the administrative data and this was assessed by bootstrapping methodology. Furthermore, we had a large sample size (N = 3778) and sufficient hospital mortality (27.1%), which produced stable estimates of coefficients that were less susceptible to shrinkage. Our study suggests that ICU specific weights may be quite different than the original Charlson weights, and these results should serve as caution to health service researchers when using the Charlson index as risk adjustment measure in ICU patients.
Although the Charlson index alone did not perform as well (C = 0.626) as the full APACHE II, we do not believe our results preclude its use for risk adjustment in administrative data in the absence of ICU acuity of illness scores. The collection of ICU acuity of illness scores is labour intensive and therefore costly. The validity of collecting APACHE II data from retrospective chart review has not, to our knowledge, been validated (for example, accurate assessment of Glasgow coma score may be very difficult). In contrast, the Charlson index is well suited for use in administrative datasets, and algorithms developed to recode administrative collected and coded diagnosis data into a Charlson index has been well studied and validated [8, 9]. Therefore, the Charlson index may have practical applications when APACHE II or other acuity scores have not been prospectively determined, or have been determined in a non-standard manner. Furthermore, age and sex are data nearly always available in administrative datasets. When we analyzed the Charlson index with age and sex, the C-statistic increased to 0.68 (analysis not presented), supporting the utility of the Charlson index as a risk adjustment method in the absence of ICU based acuity scores. The Charlson index may also have some usefulness in comparing outcomes between ICU patient populations where the same acuity of illness score has not been used. Finally, the utility of ICU acuity illness scores in the assessment of outcome beyond hospital discharge has not been well established, whereas, the Charlson index is an accepted method of risk adjustment in longer term outcome studies in non-ICU patients. Future studies should explore the Charlson index's ability to predict longer term outcomes in ICU and whether modification of the Charlson index could help improve the performance of this risk adjustment method in intensive care data.
When comparing health care delivery between ICUs risk adjustment methods are required.
A well validated method of calculating Charlson index scores from administrative data can be used to obtain Charlson scores for individual ICU patients without an expensive, labour intensive chart review.
Although the Charlson index does not perform as well at predicting in-hospital mortality among ICU patients as the APACHE II, the Index could be used when acuity of illness scores are unavailable or are not recorded in a standard way.
Future studies should compare the ability of the Charlson index and the APACHE system to predict longer term outcomes in ICU.
In addition, recalculating and updating Charlson weights could help to improve performance of this risk adjustment method in intensive care data.
APACHE II:
Acute physiology and chronic health evaluation version II
Calgary Health Region
SAPS:
Simplified Acute Physiology Score
MPM:
Mortality Prediction Model
APS:
Acute Physiology Score
Quantitative Sentinel
PHN:
Provincial healthcare number
ICD:
International Classification of Disease
LR:
Akaike's Information Criterion
Lemeshow S, Teres D, Avrunin JS, et al: Predicting the Outcome of Intensive Care Unit Patients. JASA. 1988, 83: 348-356.
Knaus W, Zimmerman J, Wagner D, et al: APACHE-acute physiology and chronic health evaluations: A physiologically based classification system. Crit Care Med. 1981, 9: 591-7.
Lemeshow S, Teres D, Klar J, et al: Mortality probability models (MPM II) based on an international cohort of intensive care patients. JAMA. 1993, 270: 2478-86. 10.1001/jama.270.20.2478.
Knaus WA, Draper EA, Wagner DP, et al: ApacheII: A severity of disease classification system. Crit Care Med. 1985, 13: 818-29. 10.1097/00003246-198510000-00009.
Knaus W, Wagner D, Draper E, et al: The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991, 100: 1619-36. 10.1378/chest.100.6.1619.
Zimmerman J, Kramer A, Douglas S, Malila F: Acute Physiology and Chronic Health Evaluation (APACHE IV): Hospital mortality assessment for today's critically ill patients. Crit Care Med. 2006, 34: 1297-310. 10.1097/01.CCM.0000215112.84523.F0.
Le Gall JR, Lemeshow S, Saulnier F: A New Simplified Acute Physiology Score (SAPS II) Based on a European/North American Multicentre Study. JAMA. 1993, 270: 2957-2963. 10.1001/jama.270.24.2957.
Moreno RP, Metnitz PGH, Almeida E, et al: SAPS 3 – From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Int Care Med. 2005, 31: 1345-55. 10.1007/s00134-005-2763-5.
Sibbald William, Bion Julian, SA R: Evaluating Critical Care-Using Health Services Research to Improve Quality. 2001, Berlin: Springer-Verlag
Charlson Mary E, Peter Pompei, Ales Kathy L, et al: A new method of classifying prognostic Comorbidity in Longitudinal Studies: Development and Validation. J Chron Dis. 1987, 40: 373-383. 10.1016/0021-9681(87)90171-8.
Poses RM, McClish DK, Smith WR, et al: Prediction of Survival of Critically Ill Patients by Admission Comorbidity. J Clin Epidemiol. 1996, 49: 743-747. 10.1016/0895-4356(96)00021-2.
Norena M, Wong H, Thompson WD, et al: Adjustment of intensive care unit outcomes for severity of illness and comorbidity scores. J Crit Care. 2006, 21: 142-150. 10.1016/j.jcrc.2005.11.011.
Needham DM, Scales DC, Laupacis A, et al: A systematic review of the Charlson comorbidity index using Canadian administrative databases: a perspective on risk adjustment in critical care research. J Crit Care. 2005, 20: 12-19. 10.1016/j.jcrc.2004.09.007.
Doig CJ, Zygun DA, Fick GH, et al: Study of clinical course of organ dysfunction in intensive care. Crit Care Med. 2004, 32: 384-90. 10.1097/01.CCM.0000108881.14082.10.
Quan H, Sundararajan V, Halfon P, et al: Coding Alogrithms for defining comorbidities in ICD-9 CM and ICD-10 Administrative Data. Medical Care. 2005, 43: 1130-9. 10.1097/01.mlr.0000182534.19832.83.
Deyo RA, Cherkin DC, Ciol MA: Adapting a clinical comorbidity Index for use with ICD-9CM Administrative Databases. J Clin Epidemiol. 1992, 45: 613-619. 10.1016/0895-4356(92)90133-8.
D'Hoore W, Bouckaert A, Tilquin C: Practical Considerations on the use of the Charlson Comorbidity Index with Administrative Data Bases. J Clin Epidemiol. 1996, 49: 1429-1433. 10.1016/S0895-4356(96)00271-5.
Iezzoni L: Risk adjustment for measuring health care outcomes. 2003, Health Administration Press, 3
Hosmer D, Lemeshow SNY: Applied logistic regression. 1989, John Wiley & Sons, 1
Vittinghoff E, Glidden DV, Shiboski SC, et al: Regression Methods in Biostatistics. 2004, San Fransico:Springer
Harrell FE: Regression Modeling Strategies. 2001, Nashville:Springer
Livingston BM, Mackirdy FN, Howie JC, Jones R, Norrie JD: Assessment of the performance of five intensive care scoring models within a large Scottish database. Crit Care Med. 2000, 28: 1820-7. 10.1097/00003246-200006000-00023.
Ho KM, Finn J, Knuiman M, Webb SAR: Combining multiple comorbidities with Acute Physiology Score to predict hospital mortality of critically ill patients: a linked data cohort study. Anaesth. 2007, 62: 1095-1100. 10.1111/j.1365-2044.2007.05231.x.
The Western Regional Training Center in Health Services Research, Vancouver, Canada provided funding to SQ for her Masters degree, of which this work was part. The authors would also like to acknowledge Ms. Leena Norman for her assistance in preparing the manuscript for submission and Dr. Stafford Dean, of the Calgary Health Region, who facilitated the acquisition of data.
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
Susan Quach, Deirdre A Hennessy, Peter Faris, Andrew Fong, Hude Quan & Christopher Doig
Department of Critical Care Medicine, University of Calgary, Calgary, Alberta, Canada
Deirdre A Hennessy & Christopher Doig
Susan Quach
Deirdre A Hennessy
Peter Faris
Andrew Fong
Hude Quan
Christopher Doig
Correspondence to Christopher Doig.
SQ was involved in the concept and design of the study, carried out the data analysis, participated in the interpretation of data, drafted the article and gave final approval to be published. DH was involved in the concept and design of the study, participated in the interpretation of data, drafted the article, critically revised the manuscript and gave final approval to be published. PF was involved in the concept and design of the study, participated in the interpretation of data, critically revised the manuscript and gave final approval to be published. AF was involved in acquisition of the data, participated in interpretation of data, critically revised the manuscript and gave final approval to be published. HQ was inovled in the concept and design of the study, participated in the acquisition and interpretation of data, critically revised the manuscript and gave final approval to be published. CD was involved in the concept and design of the study, participated in the acquisition and interpretation of data, critically revised the manuscript and gave final approval to be published.
Quach, S., Hennessy, D.A., Faris, P. et al. A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients. BMC Health Serv Res 9, 129 (2009). https://doi.org/10.1186/1472-6963-9-129
Intensive Care Unit Patient
Charlson Index
Illness Score


text:
Businesses allowed under GCQ, compliant to health protocols according to DTI, LGU
CABARROGUIS, Quirino, May 12 (PIA)—The Department of Trade and Industry and the local government of Diffun reported that the businesses that were allowed to reopen under the General Community Quarantine were compliant to the minimum health requirements imposed by the Department of Health and the Executive Order No. 13 issued by Governor Dakila Carlo E. Cua.
Under EO No. 13, three categories of industries were allowed to resume operations such as:
Category I includes agriculture, fishery, and forestry industries, food manufacturing and food supply chain businesses, including food retail establishments such as supermarkets, grocery stores, and food preparation establishments, granted full operational capacity.
Also included are take-out and delivery services, food delivery services, health-related establishments, the logistics sector, information technology and telecommunication companies, and the media.
Under Category II are mining including quarrying (for authorized permit holder(s)) and other manufacturing, export-oriented, and electronic commerce companies.
Also within the category are delivery, repair and maintenance, and housing and office services.
Category II grants anywhere between fifty percent (50%) up to full operational capacity to the said industries, without prejudice to work- from-home and other alternative work arrangements.
For Category III, financial services, BPOs, legal, accounting, and auditing services are allowed to resume operations, including professional, scientific, technical, and other non-leisure services. Also counted in are other non-leisure wholesale and retail establishments.
The category grants a maximum of fifty percent (50%) work-on-site arrangement, and without prejudice to work-from home and other alternative work arrangements.
Per EO No. 13, s. 2020, the Mayor "shall issue the Authority to Open the Establishment and the Permit to Work for employees falling under Categories I, II and III, without imposing any fee or charges, and ensure strict adherence to the DTI and DOLE Interim Guidelines on Workplace Prevention and Control of Covid-19."
Medical screening of employees shall be arranged by employers. Medical certificates (of employees) shall be submitted to the Municipal Local Government Unit (MLGU) for the issuance of Authority to Operate.
Employees who reside outside the province may be subjected to additional requirements.
Company owners are required to provide the list of names of their skeletal workforce, work schedules for the identified personnel, and a written request to operate while adhering to the DTI and DOLE Interim Guidelines.
Employers shall provide lodgings for their non-resident employees. However, the employees of the establishments under Categories I, II and III, who work in, but those are not residents of Quirino are encouraged to avail of the work from home arrangement and perform services through virtual or online application.
Residents may report to work subject to flexible work arrangements as may be prescribed by their supervising officer, subject to the minimum health standards.
The business owners or employers must provide sanitation stations and PPEs such as face masks, sanitizers and thermal scanners. (MDCT/TCB/PIA 2-Quirino)


text:
Q: Uncaught exception Failure("input_value: bad bigarray kind") - Unison on Windows and Mgt Development Environment I am using the Mgt Development Environment (mgtcommerce/mgt-development-environment-5.6) on Windows 8.1. & DockerToolbox for Windows.
I also downloaded the proposed versions of OpenSSH & Unison from https://www.mgt-commerce.com/documentation/mgt-development-windows-file-synchronization and created the sync.bat as described.
Unfortunately Unison chrashes with the following error message:
Uncaught exception Failure("input_value: bad bigarray kind")
Raised by primitive operation at file "./remote.ml", line 453, characters 18-45
Called from file "./remote.ml", line 459, characters 23-61
Called from file "./lwt/lwt.ml", line 75, characters 20-23
Re-raised at file "./lwt/lwt.ml", line 135, characters 12-13
Called from file "list.ml", line 73, characters 12-15
Called from file "./lwt/lwt.ml", line 31, characters 2-37
Called from file "./lwt/lwt.ml", line 83, characters 17-46
Called from file "./lwt/win/lwt_unix_impl.ml", line 239, characters 6-40
Called from file "./uitext.ml", line 490, characters 2-113
Called from file "./uitext.ml", line 556, characters 38-66
Called from file "./uitext.ml", line 718, characters 6-47
Called from file "./uitext.ml", line 756, characters 6-125
Called from file "./uitext.ml", line 804, characters 8-47
Called from file "./uitext.ml", line 870, characters 21-43

It does work without problems:

*

*the first time unison is used for a docker image

*when using unison with the flag  -ignorearchives

I read several articles and most of them come to the conclusion that this is an OCaml incompatibility problem.
How to fix this?
Did someone else experience that error as well?

A: The error message "input_value: bad bigarray kind" indicates you're hitting a somewhat well-known bug in Unison.
Not only do the major versions of two Unison processes involved in a synchronization need to match (as you probably know), there was also an internal change in the OCaml libraries somewhere between OCaml versions 4.01 and 4.02, related to the marshaling of data. It's hard to give you a precise link, as some of Google's most promising results return an empty page, but I hope these will do:
https://github.com/bcpierce00/unison/issues/32
http://unison-users.yahoogroups.narkive.com/7FOa6VCg/re-unison-failed-uncaught-exception-failure-input-value-bad-bigarray-kind
Note especially on the page in the last link:

[...] this is a known issue, and happens because Unison wasn't compiled
  against the same OCaml versions [...]. The marshaling in OCaml 4.01 and
  4.02 uses a different format... You will have to compile Unison 2.48.3
  against the same versions of OCaml [...]

I sometimes run into this problem myself, and I circumvent it by compiling my own OCaml, Lablgtk for the graphical (GTK) interface, and Unison. I found compiling those to be quite straightforward under Linux (haven't tried on other operating systems).

A: At the end I was able to get the sync up & running with the following setup:

*

*On Windows 10: use the unison-files provided by mgt-commerce.com - it is version 2.48.3 (but I don't know the OCaml-version used)

*Within the docker image recompile unison with unison-sources 2.48.4 and OCaml version 4.01.0

Commands used within Dockerfile for this:
RUN apt-get update && apt-get install -y \
    ocaml \
    && curl -L https://github.com/bcpierce00/unison/archive/2.48.4.tar.gz | tar zxv -C /tmp && \
         cd /tmp/unison-2.48.4 && \
         sed -i -e 's/GLIBC_SUPPORT_INOTIFY 0/GLIBC_SUPPORT_INOTIFY 1/' src/fsmonitor/linux/inotify_stubs.c && \
         make && \
         cp src/unison src/unison-fsmonitor /usr/bin && \
         cd /root && rm -rf /tmp/unison-2.48.4


Going from one limb to two wings.
That used to swell out from her chest.
And showed me where the cut was.
To me if my orchards were uprooted.
Empathy can be so harsh sometimes.
To be more of a man now, than ever before.
I fell in love with her all over again.
From the ridges of my ambidextrous fingers.
In the same fashion, as years before.


Heavy rains from Tropical Storm Ewiniar brought chaos to Guangzhou, China, on June 8. The high levels of corporate debt in China and other emerging markets have raised concerns that there is bad weather ahead for the global economy. Photo: Reuters.


Samsung UE50NU7400 television review - Which?
Samsung's TV line-up has shrunk in 2018 and it's the competitively priced 6 Series that has been ditched. That leaves 7 Series TVs, like this 50-inch UE50NU7400, with some big boots to fill. Its 4K screen and support for new HDR formats should mean a smooth transition, but there are no guarantees. Is the Samsung UE50NU7400 (sometimes called the UE50NU7400UXXU) a fitting replacement or poor imitation of the supremely popular 6 Series? Here's our expert assessment.


Jensen, S. S. & Øvad, T., 1 Nov 2016, In : Cognition, Technology and Work. 18, 4, p. 717-731 15 p.
Pedersen, T. Ø., 2016, Aalborg Universitetsforlag. 177 p. (Ph.d.-serien for Det Teknisk-Naturvidenskabelige Fakultet, Aalborg Universitet).
Øvad, T., Bornoe, N., Larsen, L. B. & Stage, J., 2015, OzCHI '15 Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction. Association for Computing Machinery, p. 397-406 (ACM Conference Proceedings Series).


c-on-identifier is a compiled Lisp function in `cc-engine.el'.
Return non-nil if the point is on or directly after an identifier.
identifier is detected, the returned value is its starting position.
happen in Pike) then the point for the preceding one is returned.


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