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No Excess producten

No Excess producten

No Excess Short - Modern Fit - Grijs

No Excess Broek - Modern Fit - Blauw-25%

No Excess Overhemd - Modern Fit - Blauw

No Excess gemêleerd T-shirt lichtgeel-40%

No Excess Overhemd - Modern Fit - Blauw

No Excess Overhemd - Moderrn Fit - Wit-25%
No Excess Gilet - Modern Fit - Blauw-25%
No Excess regular fit polo met all over print ecru-30%

No Excess Pullover - Modern Fit - Roze

No Excess Pullover - Modern Fit - Grijs

No Excess Jas - Modern Fit - Groen-35%
No Excess regular fit chino short donkergroen-50%
No Excess Overhemd - Modern Fit - Bordo-35%

No Excess Pullover - Slim Fit - Oranje

No Excess Pullover - Slim Fit - Blauw

No Excess Polo Pique Stretch Stone Wash 11370101SN/056

No Excess Pullover - Slim Fit - Wit

No Excess T-shirt - Modern Fit - Geel

No Excess T-shirt - Modern Fit - Geel

No Excess Short Jog Denim Stretch 118190331SN/229

No Excess Overhemd - Modern Fit - Groen

Dstrezzed Chino - Slim Fit - Groen

No Excess T-shirt - Modern Fit - Wit

No Excess Short Jog Denim Stretch 118190331SN/224

No Excess T-shirt - Modern Fit - Wit

No Excess T-shirt - Modern Fit - Zwart

No Excess Overhemd - Modern Fit - Rood

No Excess regular fit polo met bladprint donkerblauw-50%

No Excess Gilet - Modern Fit - Blauw

No Excess Gilet - Modern Fit - Blauw

No Excess Gilet - Modern Fit - Blauw

No Excess regular fit overhemd met all over print donkerblauw-20%
Sale - No Excess Jeans - Modern Fit - Groen-50%

No Excess T-shirt - Modern Fit - Wit

No Excess Short - Modern Fit - Zand

No Excess T-shirt met all over print sky-60%

No Excess Short - Modern Fit - Blauw

No Excess Short - Modern Fit - Blauw

No Excess Overhemd - Modern Fit - Zwart-15%
No Excess T-shirt met all over print indigo blue-60%

No Excess Overhemd - Modern Fit - Zwart

No Excess Overhemd - Modern Fit - Zwart

No Excess Chino - Modern Fit - Zwart

No Excess regular fit overhemd met all over print wit-30%

No Excess Vest - Modern Fit - Zwart

No Excess Overhemd - Modern Fit - Blauw

No Excess Overhemd - Modern Fit - Geel

No Excess regular fit overhemd met all over print wit-30%

No Excess Overhemd - Modern Fit - Blauw

No Excess Pullover - Modern Fit - Brique

No Excess Gilet - Modern Fit - Blauw-15%
No Excess T-shirt met all over print zalm-50%

No Excess Overhemd - Modern Fit - Creme

No Excess Overhemd - Modern Fit - Creme

No Excess Pullover - Modern Fit - Grijs-15%

No Excess Gilet Printed Navy

No Excess Pullover - Modern Fit - Oker-15%

Таблицы размеров

Обратите внимание, что таблицы размеров относятся к одежде собственного бренда ASOS и рассчитаны на соответствующие размеры тела. Некоторые бренды могут отличаться от этих размеров, но вы все равно можете использовать их в качестве ориентира.

More size guides?

Womenswear

Clothing — Single Size Conversion
UK468101214161820222426
European323436384042444648505254
US1246810121416182022
Australia468101214161820222426
Clothing — Single Size
Single SizeUK 4UK 6UK 8UK 10UK 12UK 14UK 16UK 18UK 20UK 22UK 24UK 26
CMInCMInCMInCMInCMInCMInCMInCMInCMInCMInCMInCMIn
Bust763078½31813286349136963810140108½4311645½1224812850½13453
Waist5822¾60½23¾6324¾6826¾7328¾7830¾8332¾90½35¾9838½1044111043½11646
Hips83½32¾8633¾88½34¾93½36¾98½38¾103½40¾108½42¾11645¾123½48½129½51135½53½141½56
Clothing — Dual Size conversion
UKXSSMLXL
EuropeanXSSMLXL
USXXSXSSML
AustraliaXSSMLXL
Clothing — Dual Size
Dual SizeUK XS / 6UK S / 8-10UK M / 12-14UK L / 16-18UK XL / 20
CMInchesCMInchesCMInchesCMInchesCMInches
Bust78½3181-8632-3491-7836-38101-108½40-4311645½
Waist60½23¾63-6824¾-26¾73-7828¾-30¾83-90½32¾-35¾9838½
Hips8633¾88½-93½34¾-36¾98½-103½38¾-40¾108½-11642¾-45¾123½48½
Maternity Clothing
Single Size8101214161820
CMInchesCMInchesCMInchesCMInchesCMInchesCMInchesCMInches
Bust863491369638101401064211344¾12047½
Waist9537¾10039½10541½11043½11545½12248¼12951
Hips9336¾9838¾10340¾10842¾11344¾12045¾12748½
Main Range Lengths (Based on average size 10)
TrousersSkirtsDress
ShortInches30MiniInches14MiniInches33½
CM76CM35CM85
RegularInches32MidiInches17½MidiInches35½
CM81CM45CM90
LongInches34KneeInches21½KneeInches37½
CM86CM55CM95
CalfInches29½CalfInches39½
CM75CM100
MaxiInches37½MaxiInches56
CM95CM142
Читайте так же:
Опросник креативности джонсона туник
Petite Lengths (Based on average petite size 10)
TrousersSkirtsDress
RegularInches29MiniInches13MiniInches31½
CM74CM33CM80
MidiInches17MidiInches33½
CM43CM85
KneeInches20½KneeInches35½
CM52CM90
CalfInches25¾CalfInches37½
CM65½CM95
MaxiInches35¾MaxiInches54
CM90½CM137
Dual Sized Swimwear
UK8 B/C10 B/C12 B/C14 B/C16 B/C18 B/C
European36 B/C38 B/C40 B/C42 B/C44 B/C46 B/C
US4 B/C6 B/C8 B/C10 B/C12 B/C14 B/C
Australia8 B/C10 B/C12 B/C14 B/C16 B/C18 B/C
UK8 D/td10 D/DD12 D/DD14 D/DD16 D/DD18 D/DD
European36 D/E38 D/E40 D/E42 D/E44 D/E46 D/E
US4 D/DD-E6 D/DD-E8 D/DD-E10 D/DD-E12 D/DD-E14 D/DD-E
Australia8 D/DD10 D/DD12 D/DD14 D/DD16 D/DD18 D/DD
Bra Sizing
UKEuropeanUSAustralia
32A70A32A32A
32B70B32B32B
32C70C32C32C
32D70D32D32D
32DD70E32DD/E32DD
32E70F32DDD/F32E
32F70G32G32F
34A75A34A34A
34B75B34B34B
34C75C34C34C
34D75D34D34D
34DD75E34DD/E34DD
34E75F34DDD/F34E
34F75G34G34F
36A80A36A36A
36B80B36B36B
36C80C36C36C
36D80D36D36D
36DD80E36DD/E36DD
36E80F36DDD/F36E
36F80G36G36F
38A85A38A38A
38B85B38B38B
38C85C38C38C
38D85D38D38D
38DD85DD38DD/E38DD
38E85E38DDD/F38E
38F85G38G38F
Hosiery (S-L)
Dress Size
HeightUK 8-10 / 35-37″UK 12-14 / 39-41″UK 16-18 / 43-46″
Up to 5ft 3″SmallMediumLarge
Up to 5ft 6″SmallMediumLarge
Up to 5ft 10″MediumLargeLarge

*** If your hips and height are on the borderline of a size, you may find the next size up more comfortable ***

*** One size hosiery is a comfortable fit for UK sizes 8 — 14 and height up to 5ft 4″ ***

Новый лохотрон в Интернете – Коробки с призами и конвертация валюты

Новый лохотрон в Интернете – Коробки с призами и конвертация валюты

Время не стоит на месте, и мошенники тоже развиваются. Недавно появилась обновленная схема обмана доверчивых граждан.

Очередной лохотрон

Во «Вконтакте», «Одноклассниках» или другой социальной сети прилетает сообщение от группы, на которую вы подписаны, или от «друга» с призывом принять участие в розыгрыше с гарантированным денежным призом. Возможно вы сами листая ленту наткнетесь на заманчивую рекламу лохотрона.

После нажатия на ссылку нас перебрасывает на страницу:

Более 100 единиц компьютерной и мобильной техники! 50 денежных призов от 5 до 50 тысяч долларов! Всё, что вам нужно – открыть правильную подарочную коробку!

В ходе эксперимента мы «выиграли» 3060 долларов.

Поздравляем, Вы угадали! Следуйте инструкциям на следующей странице, чтобы получить свой приз.

Далее ожидание соединения с оператором, перед вами в очереди 2 человек, оператор подготовит ваш выигрыш.

Соединение с оператором происходит, некий оператор Анастасия Теплова набирает мне сообщение, поздравляет с выигрышем.

Далее пишет: Для того чтобы продолжить вывод вам необходимо дать свое согласие, сейчас я вызову у вас специальную кнопку.

Окей, согласие получено, возвращаемся, и Анастасия готовит необходимую информацию. В это время человек уже ни о чем не думает, кроме своих денег.

Ниже на странице сайта идет чат, люди пишут, что пришло 20000 рублей на карту, кто-то выиграл 1900 рублей, радуются, пишут, что это американская контора, специально пишут, что кому-то пришло 0 рублей, (ну не могут же все выиграть). Кто-то даже пишет матюки для правдоподности. Думаю, вы понимаете, что это не отзывы реальных победителей.

Затем от нас требуется банковская карта и номер счета либо карты.

Мы просто «от балды» ввели набор случайных цифр и заказали вывод средств в размере 3060 долларов, и опять нас заставляют долго долго ждать, чтобы мы потеряли остатки разума и думали только о своём выигрыше.

Далее выскакивает уведомление:

Банк получателя отклонил операцию. Код 409. Идет переадресация на страницу решения проблемы. Для решения проблемы необходима конвертация валюты из долларов в рубли.

Стоимость конвертации 3060 долларов в 232 560 рублей составит всего лишь 348 рублей.

Здесь начинается самое веселье, теперь нам нужно оплатить конвертацию валюты и вывести деньги. Нас перебрасывает уже на настоящую форму оплаты, где мы можем пожертвовать своими рублями в количестве 348 через банковскую карту или через Яндекс деньги.

Дальше не будет ничего хорошего. С вас будут еще и еще вымогать деньги под разными предлогами. Возможно потребуется оплатить еще некую комиссию, а возможно вам предложат сыграть еще разок.

Covid-19 data
Tracking covid-19 excess deaths across countries

In many parts of the world, official death tolls undercount the total number of fatalities

A S COVID-19 has spread around the world, people have become grimly familiar with the death tolls that their governments publish each day. Unfortunately, the total number of fatalities caused by the pandemic may be even higher, for several reasons. First, the official statistics in many countries exclude victims who did not test positive for coronavirus before dying—which can be a substantial majority in places with little capacity for testing. Second, hospitals and civil registries may not process death certificates for several days, or even weeks, which creates lags in the data. And third, the pandemic has made it harder for doctors to treat other conditions and discouraged people from going to hospital, which may have indirectly caused an increase in fatalities from diseases other than covid-19.

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One way to account for these methodological problems is to use a simpler measure, known as “excess deaths”: take the number of people who die from any cause in a given region and period, and then compare it with a historical baseline from recent years. We have used statistical models to create our baselines, by predicting the number of deaths each region would normally have recorded in 2020 and 2021.

Many Western countries, and some nations and regions elsewhere, regularly publish data on mortality from all causes. The table below shows that, in most places, the number of excess deaths (compared with our baseline) is greater than the number of covid-19 fatalities officially recorded by the government. The full data for each country, as well as our underlying code, can be downloaded from our GitHub repository. Our sources also include the Human Mortality Database, a collaboration between UC Berkeley and the Max Planck Institute in Germany, and the World Mortality Dataset, created by Ariel Karlinsky and Dmitry Kobak.

The chart below uses data from EuroMOMO, a network of epidemiologists who collect weekly reports on deaths from all causes in 23 European countries. These figures show that, compared with a historical baseline of the previous five years, Europe has suffered some deadly flu seasons since 2016—but that the death toll from covid-19 has been far greater. Though most of those victims have been older than 65, the number of deaths among Europeans aged 45-64 was 40% higher than usual in early April 2020.

Below are a set of charts that compare the number of excess deaths and official covid-19 deaths over time in each country. The lines on each chart represent excess deaths, and the shaded area represents the number of fatalities officially attributed to coronavirus by the government.

In March 2020 America’s east coast was hit hard by the pandemic. States elsewhere locked down quickly enough to prevent major outbreaks at that point, but a second wave in November and December surged through most of the country. Excess mortality was low from March 2021 onwards, as a rapid vaccination campaign allowed the country to open up again.

While covid-19 was devastating New York in March 2020, cities in western Europe were also suffering severe outbreaks. Britain, Spain, Italy, Belgium and Portugal have some of the highest national excess-mortality rates in the world, after adjusting for the size of their populations. These countries also suffered a second wave of deaths in the autumn and winter of 2020. Some western European countries were slow to vaccinate their citizens in early 2021, as shown by our covid-19 data tracker. But by June mortality rates had returned to normal across the region.

Countries in northern Europe have generally experienced much lower mortality rates throughout the pandemic. Some Nordic nations have experienced almost no excess deaths at all. The exception is Sweden, which imposed some of the continent’s least restrictive social-distancing measures during the first wave.

In central Europe only the Netherlands and Switzerland suffered large numbers of excess deaths in early 2020. After international travel resumed, the entire region was ravaged in the autumn. Poland, Hungary and the Czech Republic all endured additional spikes of mortality in March and April 2021.

South-eastern Europe has followed a similar pattern. November and December 2020 were particularly lethal, with Bulgaria recording the highest weekly excess-mortality rates of any country in our tracker. Several countries have since experienced further deadly outbreaks.

Among former republics of the Soviet Union, only Belarus suffered substantial excess mortality in early 2020, after introducing almost no constraints on daily life. A second wave in late 2020 affected the entire region. Russia now has one of the world’s largest excess-mortality gaps. It recorded about 580,000 more deaths than expected between April 2020 and June 2021, compared with an official covid-19 toll of only 130,000.

Much of Latin America experienced a devastating first wave from April to July 2020, with Bolivia and Ecuador hit particularly hard. A second wave surged through the region in late 2020, as Mexico, Peru and Brazil all recorded higher peaks of excess mortality than at any previous point during the pandemic. The virus has continued to circulate throughout the continent since then, with Colombia and Paraguay suffering their worst death tolls in April and May 2021.

Outside Europe and the Americas, few places release data about excess deaths. No such information exists for large swathes of Africa and Asia, where some countries only issue death certificates for a small fraction of people. For these places without national mortality data, The Economist has produced estimates of excess deaths using statistical models trained on the data in this tracker (as explained in our methodology post). In India, for example, our estimates suggest that perhaps 2.3m people had died from covid-19 by the start of May 2021, compared with about 200,000 official deaths.

Among developing countries that do produce regular mortality statistics, South Africa shows the grimmest picture, after recording three large spikes of fatalities. In contrast, Malaysia and the Philippines had “negative” excess mortality—fewer deaths than they would normally have recorded, perhaps because of social distancing.

A handful of rich countries elsewhere publish regular mortality data. They tend to have negative excess mortality. Australia and New Zealand managed to eradicate local transmission after severe lockdowns. Taiwan and South Korea achieved the same outcome through highly effective contact-tracing systems. Israel has experienced some excess deaths, but has also outpaced the rest of the world in vaccinating its population, with promising results.

Update (October 14th 2020): A previous version of this page used a five-year average of deaths in a given region to calculate a baseline for excess deaths. The page now uses a statistical model for each region, which predicts the number of deaths we might normally have expected in 2020. The model fits a linear trend to years, to adjust from long-term increases or decreases in deaths, and a fixed effect for each week or month.

Correction: The data for deaths officially attributed to covid-19 in Chile were corrected on September 9th 2020. Apologies for this error.

Sources: The Economist; Our World In Data; Johns Hopkins University; Human Mortality Database; World Mortality Dataset; Registro Civil (Bolivia); Vital Strategies; Office for National Statistics; Northern Ireland Statistics and Research Agency; National Records of Scotland; Registro Civil (Chile); Registro Civil (Ecuador); Institut National de la Statistique et des Études Économiques; Santé Publique France; Provinsi DKI Jakarta; Istituto Nazionale di Statistica; Dipartimento della Protezione Civile; Secretaría de Salud (Mexico); Ministerio de Salud (Peru); Data Science Research Peru; Departamento Administrativo Nacional de Estadística (Colombia); South African Medical Research Council; Instituto de Salud Carlos III; Ministerio de Sanidad (Spain); Datadista; Istanbul Buyuksehir Belediyesi; Centres for Disease Control and Prevention; USA Facts; New York City Health. Get the data on GitHub

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