Reverse Loop Model Trains

To reverse the direction of a model train you can use two main options.

This video link shows is the simplest option and least expensive option. Watch the video and it saves me drawing.

You need two points or turnouts, here we use two RH ones.

Both are connected to the main loop. The top points accepts the train coming in clockwise direction. Our aim is to get the train to come out traversing counterclockwise. Thus the points need to be set, firstly to take the train off and then to allow the train to move back onto the main track.

Both points are connected with a separate track, its length and shape depends on dimensions of your layout.

This section of track needs to be isolated with its own power. I have placed the isolation sections at the end of each point to the connecting track.

The polarity of this isolated track will match that of the incoming train.

As the train enters the isolated section you need to switch the polarity of the outer track in this video to counterclockwise wise. The train will then travers the point and move back onto the outer track in the opposite direction.

US Elections

Thought I would do a series of wordclouds and sentiment graphs each day from Australia using data from twitter.

Basic searchkey’s are “trump election” and then “biden election”, very simple and some overlap. 5000 tweets are sampled.

The wordcloud is formed from taking counts of the most groups of 5 words.

The sentiment graph is taken using a method that attempts to account for the use of negation eg not like is considered negative but like is positive.

22/10/2020 midday EST Australia

“trump election”

Sentiment

The overall sentiment is negative, less than zero on LHS.

5 word phases wordcloud

“biden election”

Sentiment

More balanced postive and negative around 0

Wordcloud

Comparison of Biden and Trump tweets

The report on this research can be found at:

https://drive.google.com/file/d/13Cgne35F4OckDIP8iOfsBaLYuecFxPsY/view?usp=sharing

The two most interesting visualization are the wordclouds of three words phrases. Biden’s is first and then Trump’s.

Sentiment analysis showed only minor differences. However, the wordcloud’s show very different focus for both. Biden’s is characterised by more positive statements about issues and the need to defeat Trump. Trump’s on the other hand shows a strong focus on his slogans e.g. Corrupt Joe Biden.

Covid-19 US Race Investigation

The covid-19 death rate in the US is shown to have a statistically significant difference between between Afro-American (AA) and White (W) populations. The graph below shows the AA proportion difference compared to the W proportion below the x=0 line. There is a clear difference across nearly all states.

The full outline, R program and data file are available at ameyenn/covid19

The PCA biplot confirms the difference. The vectors show a negative relationship between AA and W, AA is linked with the poverty vector as well. NY is the main outlier.

A basic cluster plot across all the data is shown below.

A t-test was performed with the following results, indicating a statistically significant difference.

Welch Two Sample t-test

data: dt[, 1] and dt[, 2]
t = 3.7143, df = 80.722, p-value = 0.0003739
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.3026568 1.0010984
sample estimates:
mean of x mean of y
2.688740 2.036862

PISA – Australia and China Maths

Australian sample is randomly chosen from 720 school forming a sample of 14,000 students.
China sample is not random. It is skewed to the brightest students across 4 provinces nominated by the Chinese Government. This is the exception all other countries are sampled randomly.
 







The above shows the trend using the full Y-axis rather than exaggerating the decline by using a restricted access.

Climate Debate Issues

Climategate – as I understand it this is about the statistical techniques (trick) used to compare data sources and to ‘hide the decline’ of tree ring data from trees in Siberia after 1960 which were not consistent with other worldwide data. There have been plenty of enquires which all clear scientists of wrongdoing.

This website gave the best discussion I could find: https://www.ucsusa.org/global-warming/solutions/fight-misinformation/debunking-misinformation-stolen-emails-climategate.html I can’t vouch for the organization but it appears reputable.

Even if one accepted the skullduggery claims, the temperature data was not, it seems, involved.

With respect to the destruction of data, I found this blog article (best I could find), which seems at variance to the claims made by skeptics. http://blogs.nature.com/climatefeedback/2009/08/mcintyre_versus_jones_climate_1.html

My conclusion is that there is not much that I can find to support the view expressed in a range of sites that the scientists were involved in a cover up.

Removal from Journal Editorial Boards or paper rejection

There are discussions of paper refusal – here is one example https://www.telegraph.co.uk/news/earth/environment/climatechange/10835291/Scientists-accused-of-suppressing-research-because-of-climate-sceptic-argument.html

I am concerned about the claim that climate science can move into activist territory and be biased – there needs to be evidence, is there hard evidence that this is preventing contrary views being expressed?

How Climate Skeptic papers can be published. This is an interesting perspective: https://www.theguardian.com/environment/planet-oz/2018/jan/24/murky-world-of-science-journals-a-new-frontier-for-climate-deniers

A literature review of climate skeptic paper published: https://www.sciencedirect.com/science/article/pii/S0959652617317821

On this brief review, I can’t find any evidence of a hoax, I find disputes, attempts to set record straight and to explain, possible confusion, accusations etc. all of which are classic in a complex change process.