See how unprecedented mining in the forests of Odisha has violated a raft of laws and regulations, severely despoiled the complex eco-system, and resulted in windfall profits for miners at the cost of the public exchequer. [Photo Thread] 1/n

Full story: https://t.co/oH82uzNHzg

North Odisha’s rich deciduous forests and mountain ranges hold 1/3 of India’s haematite iron ore reserves. Here, a mining company constructs a road through the forests around the mining town of Bonai. Mining is done in over 45,000 hectares (ha) of which 34,000 is forested area
This makes it the site of the state’s largest corruption scam. Truck traffic ferrying iron ore dominates the area’s roads. They only halt on Sundays, after villagers agitated for this weekly break so that they could use the roads to attend church and visit markets. 3/n
Trucks jam the road leading up to the mines in the Kurmitar mountain range. The Supreme Court-appointed Justice MB Shah Commission estimated that at current rates of extraction, quality iron ore reserves in this region could run out in 35 years—the government rejected the claim
The Shah Commission’s report was tabled in Parliament on February 10, 2014. Over the past decade, fuelled by a commodity boom led by exports to China, mining increased manifold. These images show the contrast between the mined landscape, and areas that are yet to be mined. 5/n
An indigenous man walks across a dried mountain stream. He says locals have seen the stream’s fish population disappear in the past 7 years. During the rainy season, waste from the mine flows downhill into the stream, making it impossible to cultivate the kharif crop. 6/n
The remoteness of villages ensures that the limited safeguards which Adivasi communities have, such as participating in environmental public hearings or consenting to chopping off of trees for projects, are effortlessly violated by mining companies and government officials. 7/n
The area’s indigenous Adivasis are deeply dependent on forest produce, including lac, mahua, and sal for food, fuel, and livelihood. The loot is encouraged as much by opaque governing, as by the state’s traditional contempt for these marginalised Adivasi communities. 8/n
Post a 9-hour workday in Orissa Mining Corporation’s iron ore mine, Jaitru Giri and his family return to their shack, at the mine’s outskirts. The Shah Commission criticised mining companies for not paying labourers a fair wage despite making profits from illegal mining. 9/n
Villagers opposed South Korean steel giant POSCO’s plans to start a mine in the area. The residents' primary fear was the mine’s environmental impacts on the area’s network of mountain streams, which currently irrigate their farms, allowing them to grow crops all through the year

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Brief thread to debunk the repeated claims we hear about transmission not happening 'within school walls', infection in school children being 'a reflection of infection from the community', and 'primary school children less likely to get infected and contribute to transmission'.

I've heard a lot of scientists claim these three - including most recently the chief advisor to the CDC, where the claim that most transmission doesn't happen within the walls of schools. There is strong evidence to rebut this claim. Let's look at


Let's look at the trends of infection in different age groups in England first- as reported by the ONS. Being a random survey of infection in the community, this doesn't suffer from the biases of symptom-based testing, particularly important in children who are often asymptomatic

A few things to note:
1. The infection rates among primary & secondary school children closely follow school openings, closures & levels of attendance. E.g. We see a dip in infections following Oct half-term, followed by a rise after school reopening.


We see steep drops in both primary & secondary school groups after end of term (18th December), but these drops plateau out in primary school children, where attendance has been >20% after re-opening in January (by contrast with 2ndary schools where this is ~5%).

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Nano Course On Python For Trading
==========================
Module 1

Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...

... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit
https://t.co/EZt0agsdlV

This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!

In Module 1 of this Nano course, we will learn about :

# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)

# Using Google Colab

Intro link is here on YT: https://t.co/MqMSDBaQri

Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb

You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want

# Importing Libraries

Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.