My Visit to Hong Kong’s Ritz Carlton at Sky 100 – Views from the 104th floor dining room

For work, I recently traveled to China via Hong Kong. Had an opportunity to have a breakfast at Ritz Carlton top of the Sky 100 – the tallest building (1,588 ft) in Hong Kong. This beautifully-architected ICC Tower building stands tall in the Kowloon area.
International_Commerce_Centre_201008

Took some pictures from inside the Ritz Carlton here:

Panoramic View from the big dining room:
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Panoramic Windows View of the Victoria Harbor:
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A good view from the men’s room.
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Lobby level – looks down from the top:
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Formal Dining Room – Panoramic View
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Book Review: “What is a p-value anyway? 34 Stories to Help You Actually Understand Statistics” by Andrew J. Vickers

Review on Video:

When I was studying engineering, Statistics was not a required subject. It wasn’t until I started working that I appreciated the power of statistics. In this imprecise world, many things can be explained only in statistic terms like confidence level, insufficient sample size and etc. I got a full lesson of statistics as part of the my MBA degree curriculum. Ever after taking many more statistics-related class and going through a full Six Sigma Green Belt training, there are a few things about statistics that are still hard to grasp. It’s not just p-value that confused people, there are simply too many pitfalls when novices or even experts apply statistics to real life problems.

The author organizes 34 stories across 34 chapters. Since the author works in the medical field, he mentioned quite a few tidbits about how drugs were clinical tried. It’s a good book for beginners as well as people who used statistics regularly to watch out for its pitfalls. You might get a different perspective about statistics. I did.

My key takeaways – some refresher and something new:
1. Many things in life doesn’t follow normal distribution, especially the ones involve physical ability (pregnancy duration, body BMI). Sometimes log scale fits better.
2. Two sorts of variation: observable natural variations (reproducibility), variation of study results (repeatibility).
3. Statistical ties, e.g. in election poll, means that the confidence interval includes/overlaps with no difference.(Chap. 12)
4. P-values test hypotheses. (Chap. 13)
5. Statistics are mainly used for inference (test hypotheses) or prediction (extrapolation, interpolation).
6. Null hypotheses is a statement suggesting that nothing interesting is going on (status quo) that there is no difference between that observed data and what was expected or no difference between two groups. The P-value is the probability that the data would be at least was extreme as those observed if the null hypotheses were true.
6. T-test vs. Wilcoxon test (new to me). If the data is very skewed, use Wilcoxon test whose data must be converted to ranks first. (Chap. 16)
7. Precision (width of confidence interval) = variation/ sqrt(sample size). To reduce the confidence interval (enhance precision) by half, you’d need 4 times of the sample size – very expensive. To get the sample size for a specific test = (noise or variation / signal or confidence interval )^2.
8. “Adjust the results” can be applied to multi-variable regression to help with confounding (confusing). (Chap. 19).
9. Sensitivity is the probability of a positive diagnostic test given that you have the disease (true positive). Specificity is the probability that a negative diagnostic test given that you don’t have the disease (true negative). The most worrisome situations are when the test comes back positive if they indeed have the disease (positive predictive value) or when the test comes back negative and the patient is truly free of disease is the negative predictive value. (Chap. 20)
10. Don’t accept the null hypothesis. Instead say “we could not show a difference.” Don’t use a p-value of 0, say “P < 0.001" instead. 11. Some test methods, e.g. chi-squired and ANOVA, only provides P-value - no estimates. Correlation provides estimates but no inferences. 12. One common error is to calculate the probability of something that has already happened. Then come into conclusions about what caused it based on whether that probability is how or low. E.g. calculation of the odd OJ killed his wife. Instead, the question to ask is "if a woman has been murdered and has been previous beaten by her husband, what is the chance of he was the murderer." 13. Conditional probability depends on both the probability before the information was obtained (prior probability of a heart disease) and the value of he information (such the accuracy of the heart test). 14. The more statistical tests you conduct, the greater the chance that one will come up statistically significant, even if the null hypothesis is true. 15. A smaller study has a good chance of failing to reject the null hypothesis, even if it's false. Subgroup analysis increases both the rise of falsely rejecting the null hypothesis when it's true and falsely failing to reject the null hypothesis when it's false. 16. P-values measure strength of evidence, not size of an effect.
17. Don’t compare p-values.
18. Many statistical errors occur because of starting the clock at the wrong time.
19. Lead time bias. If you find a way to find the problem earlier, then the time between the problem and the end result will be longer.
20. Statistics is used to help scientists analyze data, but is itself a science.
21. Statistics should be about linking math to science: a. think through the science and develop statistical hypotheses in the light of specific question. b. interpret the results of the analysis in terms of their implications for those questions.
22. Statistics is about people even if you can’t see the tears.

Book Review: “Teaming with Microbes – A Gardener’s Guide to the Soil Food Web” by Jeff Lowenfels & Wayne Lewis

My first time with the book review on video: It’s a lot of work but worth a try.

Learned a lot of bacteria and fungi and how they affect the plants – the symbiotic relationship among them. Very interesting. This is a must read for any one aspiring to be have a green thumb – gardener.

The book starts out with the basic science of the food web – how the roots secretes exudates to feed the microbes which in turn feed the the root. The nutrients come from the microbes in the organic world instead of the N-P-K petroleum-based fertilizers.

The food web from USDA.
USDA Food Web

Chapter 2 goes into the soil science – informative but not very interesting.

Chapter 3 covers the bacteria. Now that’s the half of the magic. The two groups: aerobic and anaerobic bacteria. The good soil smell comes from the volatile chemicals given out by the actinomycetes – particularly adept at decaying cellulose (long chain of carbon-based glucose that gives plants structure) and chitin. The Nitrogen cycle is introduced here.

Chapter 4 covers the fungi. The job of fungi is still mysterious to many scientists and it’s a huge topic by itself. I think this chapter added more confusion than clarification. I’ll find other sources to dig deeper.

Chapter 5, 6, 7, 8, 9, 10, 11 cover the Algae and Slim Molds. (Not much there). Protozoa (single-cell organism that eats bacteria), Nematodes (nonsegmented, blind roundworms), antropods (flies, beetles, and spiders), earthworms, and gastropods (snails), reptiles, mammals, and birds.

Part 2: is about applying soil food web science to yard and garden care.

I got the most out of the compost tea making. But for the most parts the following 19 rules are the key points:

1) Some plants prefer soils dominated by fungi; others prefer soils dominated by bacteria.
2) Most vegetables, annuals, and grasses prefer their nitrogen in nitrate form and do best in bacterially dominated soils.
3) Most trees, shrubs, and perennials prefer their nitrogen in ammonium form and do best in fungal dominated soils.
4) Compost can be used to inoculate beneficial microbes and life into soils around your yard and introduce, maintain, or alter the soil food web in a particular area.
5) Adding compost/ compost teas and its soil food web to the surface of soil will inoculate the soil with the same soil food web.
6) Aged, brown organic materials support fungi; fresh, green organic materials support bacteria.
7) Mulch laid on the surface tends to support fungi; mulch worked into the soil tends to support bacteria.
8) If you wet and grind mulch thoroughly, it speeds up bacterial colonization.
9) Coarse, dryer mulches support fungal activity.
10) Sugars help bacteria multiply and grow; kelp, humic and fulvic acids, and phosphate rock dusts help fungi grow.
11) By choosing the compost you begin with and what nutrients you add to it, you make teas that are heavily fungal, bacterially dominated, or balanced.
12) Compost teas are very sensitive to chlorine and preservatives in the brewing water and ingredients.
13) Applications of synthetic fertilizers kill off most or all of the soil food web microbes.
14) Stay away from additives that have high NPK numbers.
15) Follow any chemical spraying or soil drenching with an application of compost tea.
16) Most conifers and hardwood trees (birch, oak, beech, and hickory) form mycorrhizae with ectomycorrhizal fungi.
17) Most vegetables, annuals, grasses, shrubs, softwood trees, and perennials form mycorrhizae with endomycorrhizal fungi.
18) Rototilling and excessive soil disturbance destroy or severely damage the soil food web.
19) Always mix endomycorrhizal fungi with the seeds of annuals and vegetables at planting time or apply them to roots at transplanting time.