threes@bot.com – The Book of Threes

Posted on June 10, 2020June 10, 2020 by threes@bot.com

COVID -19: Fabric masks should have three layers of different material

10 JUN, 2020 – 04:06

Roselyne Sachiti

Fearures, Health and Society Editor

The World Health Organisation (WHO) has published an updated guidance on the use of face masks for control of COVID-19.

WHO wearing masks properly — WHO do’s and don’ts

In his opening remarks at a media briefing on Covid- 19 last Friday, WHO Director-General Dr Tedros Adhanom Ghebreyesus said the guidance is based on evolving evidence, and provides updated advice on who should wear a mask, when it should be worn and what it should be made of.

He said WHO has developed this guidance through a careful review of all available evidence, and extensive consultation with international experts and civil society groups.

“I wish to be very clear that the guidance we are publishing today is an update of what we have been saying for months: that masks should only ever be used as part of a comprehensive strategy in the fight against COVID.

“Masks on their own will not protect you from COVID-19,” he emphasised.

He said the updated guidance contains new information on the composition of fabric masks, based on academic research requested by WHO.

“Based on this new research, WHO advises that fabric masks should consist of at least three layers of different material. Details of which materials we recommend for each layer are in the guidelines.”

According to the latest guidelines, cloth masks should consist of at least three layers of different materials: an inner layer being an absorbent material like cotton, a middle layer of non-woven materials such as polypropylene (for the filter) and an outer layer, which is a non- absorbent material such as a polyester or a polyester blend.

“Fabric cloths (e.g., nylon blends and 100 percent polyester) when folded into two layers, provides 2-5 times increased filtration efficiency compared to a single layer of the same cloth, and filtration efficiency increases 2-7 times if it is folded into 4 layers. Masks made of cotton handkerchiefs alone should consist of at least 4 layers, but have achieved only 13 percent filtration efficiency. Very porous materials, such as gauze, even with multiple layers will not provide sufficient

Posted on February 12, 2020 by threes@bot.com

Dennis Miller

“Bad television is three things: a bullet train to a morally bankrupt youth, a slow spiral into an intellectual void, and of course, a complete blast to watch.”

Posted on September 9, 2019September 9, 2019 by threes@bot.com

Big Oh(O) Big Theta(Θ) Big Omega(Ω)

Analysis of Algorithms

Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. It is a member of a family of notations invented by Paul Bachmann, Edmund Landau, and others, collectively called Bachmann–Landau notation or asymptotic notation.

Two simple concepts separate properties of an algorithm itself from properties of a particular computer, operating system, programming language, and compiler used for its implementation. The concepts, briefly outlined earlier, are as follows:

• The input data size, or the number n of individual data items in a single data instance to be processed when solving a given problem. Obviously, how to measure the data size depends on the problem: n means the number of items to sort (in sorting applications), number of nodes (vertices) or arcs (edges) in graph algorithms, number of picture elements (pixels) in image processing, length of a character string in text processing, and so on.

• The number of elementary operations taken by a particular algorithm, or its running time. We assume it is a function f(n) of the input data size n. The function depends on the elementary operations chosen to build the algorithm.

Algorithms are analyzed under the following assumption: if the running time of an algorithm as a function of n differs only by a constant factor from the running time for another algorithm, then the two algorithms have essentially the same time complexity. Functions that measure running time, T(n), have nonnegative values
because time is nonnegative, T(n) ≥ 0. The integer argument n (data size) is also nonnegative.

Definition 1 (Big Oh)

Let f(n) and g(n) be nonnegative-valued functions defined on nonnegative integers n. Then g(n)is O(f(n)) (read “g(n)is Big Oh of f(n)”) iff there exists a positive real constant c and a positive integer n0 such that g(n) ≤ c f(n) for all n > n0.

Note. We use the notation “iff ” as an abbreviation of “if and only if”.