Public Articles
Math Econ: Ch. 15 and Class Notes
Redundancia de LAN
20.420.PS1
Mi primer ensayo
Qi Gong & Eurithme
and 1 collaborator
A Visual Insight into the Harmonic Frequency of Prime Numbers
Photocatalytic Ozonation: Study of Reaction Parameters and Mechanism
Plato's Meno: G.M.A. Grube英文译文中文辅助版
and 2 collaborators
【70】MENO: Can you tell me, Socrates, can virtue be taught? Or is it notteachable but the result of practice, or is it neither of these, but men possessit by nature or in some other way?
SOCRATES: Before now, Meno, Thessalians had a high reputation amongthe Greeks and were admired for their horsemanship and their wealth,【b】but now, it seems to me, they are also admired for their wisdom, not leastthe fellow citizens of your friend Aristippus of Larissa. The responsibility
for this reputation of yours lies with Gorgias, for when he came to yourcity he found that the leading Aleuadae, your lover Aristippus amongthem, loved him for his wisdom, and so did the other leading Thessalians.
In particular, he accustomed you to give a bold and grand answer to any【c】question you may be asked, as experts are likely to do. Indeed, he himself
was ready to answer any Greek who wished to question him, and everyquestion was answered. But here in Athens, my dear Meno, the oppositeis the case, as if there were a dearth of wisdom, and wisdom seems to【71】have departed hence to go to you. If then you want to ask one of us that
sort of question, everyone will laugh and say: “Good stranger, you mustthink me happy indeed if you think I know whether virtue can be taughtor how it comes to be; I am so far from knowing whether virtue can betaught or not that I do not even have any knowledge of what virtue itself is.”
【b】I myself, Meno, am as poor as my fellow citizens in this matter, and Iblame myself for my complete ignorance about virtue. If I do not knowwhat something is, how could I know what qualities it possesses? Or doyou think that someone who does not know at all who Meno is couldknow whether he is good-looking or rich or well-born, or the opposite ofthese? Do you think that is possible?
MENO: I do not; but, Socrates, do you really not know what virtue is?【c】Are we to report this to the folk back home about you?
SOCRATES: Not only that, my friend, but also that, as I believe, I havenever yet met anyone else who did know.
MENO: How so? Did you not meet Gorgias when he was here?
SOCRATES: I did.
MENO: Did you then not think that he knew?
SOCRATES: I do not altogether remember, Meno, so that I cannot tell younow what I thought then. Perhaps he does know; you know what he used【d】to say, so you remind me of what he said. You tell me yourself, if you arewilling, for surely you share his views.—I do.
SOCRATES: Let us leave Gorgias out of it, since he is not here. But Meno,by the gods, what do you yourself say that virtue is? Speak and do notbegrudge us, so that I may have spoken a most unfortunate untruth whenI said that I had never met anyone who knew, if you and Gorgias areshown to know.
【e】 MENO: It is not hard to tell you, Socrates. First, if you want the virtueof a man, it is easy to say that a man’s virtue consists of being able tomanage public affairs and in so doing to benefit his friends and harm hisenemies and to be careful that no harm comes to himself; if you want thevirtue of a woman, it is not difficult to describe: she must manage thehome well, preserve its possessions, and be submissive to her husband;
Homework #2
How to use R for statistics and epidemiology and reproducible research
We are going to learn how you can use R for statistical computing in this paper. You will need an instance of Rstudio to work with the modules. Rstudio is a free and open source software that uses R as its back end. In order to work with these series of examples, just load or fire up Rstudio and copy and paste these codes from this page to the script window.
| Markdown syntax | Meaning |
|-----------------|-----------------------------|
| Headers | Use a number of hash marks |
| Table | This is an example of table |
| Figures | ![name](filename.jpg) |
(3 + 5) # type these in the console, not here
wt_kg <- 55 # will not print anything in the console
Note the following with object names
wt_kg <- 100
wt_lb <- wt_kg * 2.0
wt_kg <- 120
(wt_lb) # what do you think the wt_lb will print? 200 or 240? Why?
Functions are
a <- 9 # assign 9 to variable a
b <- sqrt(a) # b calls function sqrt and gives argument a which is 9 to it
wt_g <- c(50, 60, 70, 80)
animals <- c("mouse", "rat", "cat", "dog")
(length(wt_g)) # return 3
(length(animals)) # return 4
(class(wt_g)) # returns numeric as everything is number
(class(animals)) # returns character as it is a character vector
(str(wt_g)) # gives you more information about this vector that is it is number
wt_g <- c(wt_g, 90) # we can add more elements this way to the end
wt_g <- c(30, wt_g) # add an element to its front
# other types of vectors are logical (true/false),
# integers == whole numbers or integer numbers
# complex = complex numbers
# raw = raw data
num_char <- c(1,2,3, "a")
(class(num_char))
num_log <- c(1,2,3, TRUE)
(class(num_log))
char_log <- c("a", "b", "c", TRUE)
(class(char_log))
mix_mix <- c(1, 2, 3, "4")
(class(mix_mix))
ans <- c("mice", "rats", "dogs", "cats")
(ans[2]) # will return "rats"
(ans[c(3,2)]) # will return dogs rats
weight_g <- c(21, 34, 39, 54, 55)
(weight_g[c(TRUE, FALSE, TRUE, TRUE, FALSE)]) # we only want 1st, 3rd and 4th element
(weight_g > 50) # if you want weight > 50
(weight_g[weight_g > 50]) # subset
(weight_g[weight_g > 50 | weight_g < 30]) # use pipe
(weight_g[weight_g > 50 & weight_g < 30] ) # use and boolean
animals <- c("cat", "rat") # define what you want to search
statement <- c("a", "cat", "sat", "on a", "mat", "to catch a", "rat" ) # specify the search string
(animals %in% statement) # are animals in statement?
( animals[animals %in% statement]) # which animals?
height <- c(2,4,4,NA, 6)
( mean(height)) # will return NA
( mean(height, na.rm = T)) #T is short hand for TRUE
( height[!is.na(height)] ) # will return 4 values
( na.omit(height)) # remove missing data
( height[complete.cases(height)]) # similar to !is.na()
lengths <- c(10, 24, NA, 18, NA, 20) # vector
lengths_without_NA <- lengths[!is.na(lengths)]
( median(lengths_without_NA)) # can you think of one other way of doing this?
We will analyse a data set that has the following variables
Column | Description |
---|---|
record_id | Unique ID |
month | month of observation |
day | day of observation |
year | year of observation |
plot_id | ID of particular plot |
species_id | ID of a particular species |
sex | sex male or female |
hindfoot_length | length of the hindfoot |
weight | weight in grams |
genus | genus of the animal |
species | species of the animal |
taxa | the taxonomy |
plot_type | type of plot |
download.file("https://ndownloader.figshare.com/files/2292169", "data/portal_data_joined.csv") #download data
surveys <- read.csv('data/portal_data_joined.csv')
( head(surveys)) # first six rows
( tail(surveys)) # last six rows
( str(surveys)) # get the data structure
(nrow(surveys)) # number of rows
(ncol(surveys)) # number of columns
( names(surveys)) # lists variables
(colnames(surveys)) # lists variables another style
( summary(surveys)) # get a summary of the data set
(surveys[1,1]) # first row first column
( surveys[1,6]) # element in row 1 and column 6
( surveys[, 1]) # contents of the first column
( surveys[c(1:3), 7]) # first three rows, column 7
( surveys[, -1]) # data set minus the first column
( surveys[c(1:6), ]) # keep only the first 6 rows
( surveys["species_id"]) # return a column by name
( surveys[, "species_id"]) # returns a vector values
sex <- factor(c("male", "female", "female", "male"))
( levels(sex)) # R assigns 1 to female and 2 to male
( nlevels(sex)) # returns number of levels
plot(surveys$sex) # plot the number of observations
( levels(surveys$sex)) # returns "", "F", "M"
levels(surveys$sex)[1] <- "not known" # change "" to "not known"
library(lubridate) # load the lubridate package
surveys$date <- ymd(paste(surveys$year, surveys$month, surveys$day, sep = "-")) # ymd converts dates
( str(surveys$date)) # returns the structure of date object
Macro Notes Class 9/11
Agricultural Nanotechnologies
and 3 collaborators
Micro Notes Class 9/12
Lecture notes on biostatistics for environmental health
HABACUC 3
HABACUC 2
Lab #2: Forking and Waiting
Kodes/Zitate:Internetseite MTI-engAge + Broschüre (Linda)
and 2 collaborators
PhD writing
In the last decade, a large amount of attention has been drawn to earthquakes triggered by human activity. Most of this attention is focused on hydraulic fracturing for oil and gas, specifically where reinjection of wastewater is concerned. However, a number of other industrial activities produce earthquakes through deep injection of large amounts of fluid; namely geothermal power generation and CO2 sequestration. Although human-induced seismicity, as a phenomenon, is not new, our understanding of the mechanisms which produce it is incomplete. It is difficult to predict, for instance, whether a particular injection scheme will produce significant seismicity, partly due to uncertainties about the local state of stress and the location and orientation of deep faults which might be activated. HOW IS THIS STUDY GOING TO IMPROVE THAT UNDERSTANDING??
The presence of induced seismicity at geothermal power generation sites has been recognized for decades \citep{Ward_1972, Allis_1982}. Commonly these events are caused by temperature and pressure change within a reservoir as a result of fluid injection, although seismicity has also been attributed to reservoir volume changes and changes in fluid chemistry \citep{Allis_1982, Sherburn_2015}. Most of this seismicity is of magnitude <3.0, termed microseismicity, and presents limited hazard to local population and infrastructure, although the degree to which humans are affected varies considerably from location to location \citep{cladouhos2010injection}.
Managing Employee Reputation & Engagement in Blockchain Era