pg_similarity is an extension to support similarity queries on PostgreSQL. The implementation is tightly integrated in the RDBMS in the sense that it defines operators so instead of the traditional operators (= and <>) you can use ~~~ and ~!~ (any of these operators represents a similarity function).
pg_similarity has three main components:
pg_similarity is supported on those platforms that PostgreSQL is. The installation steps depend on your operating system.
You can also keep up with the latest fixes and features cloning the Git repository:
$ git clone https://github.com/eulerto/pg_similarity.git
Before you are able to use your extension, you should build it and load it at the desirable database.
$ tar -zxf pg_similarity-0.0.19.tgz
$ cd pg_similarity-0.0.19
$ $EDITOR Makefile # edit PG_CONFIG iif necessary
$ USE_PGXS=1 make
$ USE_PGXS=1 make install
$ psql -f SHAREDIR/contrib/pg_similarity.sql mydb # SHAREDIR is pg_config --sharedir
To use it, simply load it to the server. You can load it into and individual session:
$ psql mydb
psql (9.0.3)
Type "help" for help.
mydb=# load 'pg_similarity';
LOAD
But the typical usage is to preload it into all sessions by including pg_similarity in shared_preload_libraries at postgresql.conf. Keep in mind that there is an overhead added to each new connection.
Sorry, never tried that!
| Algorithm | Function | Operator | Parameters |
|---|---|---|---|
| L1 Distance | block(text, text) returns float4 | text ~++ text | pg_similarity.block_tokenizer (enum) pg_similarity.block_threshold (float4) pg_similarity.block_is_normalized (bool) |
| Cosine Distance | cosine(text, text) returns float4 | text ~## text | pg_similarity.cosine_tokenizer (enum) pg_similarity.cosine_threshold (float4) pg_similarity.cosine_is_normalized (bool) |
| Dice Coefficient | dice(text, text) returns float4 | text ~-~ text | pg_similarity.dice_tokenizer (enum) pg_similarity.dice_threshold (float4) pg_similarity.dice_is_normalized (bool) |
| Euclidean Distance | euclidean(text, text) returns float4 | text ~!! text | pg_similarity.euclidean_tokenizer (enum) pg_similarity.euclidean_threshold (float4) pg_similarity.euclidean_is_normalized (bool) |
| Hamming Distance | hamming(bit varying, bit varying) returns float4 | pg_similarity.hamming_threshold (float4) pg_similarity.hamming_is_normalized (bool) |
|
| Jaccard Coefficient | jaccard(text, text) returns float4 | text ~?? text | pg_similarity.jaccard_tokenizer (enum) pg_similarity.jaccard_threshold (float4) pg_similarity.jaccard_is_normalized (bool) |
| Jaro Distance | jaro(text, text) returns float4 | text ~%% text | pg_similarity.jaro_threshold (float4) pg_similarity.jaro_is_normalized (bool) |
| Jaro-Winkler Distance | jarowinkler(text, text) returns float4 | text ~@@ text | pg_similarity.jarowinkler_threshold (float4) pg_similarity.jarowinkler_is_normalized (bool) |
| Levenshtein Distance | lev(text, text) returns float4 | text ~== text | pg_similarity.levenshtein_threshold (float4) pg_similarity.levenshtein_is_normalized (bool) |
| Matching Coefficient | matchingcoefficient(text, text) returns float4 | text ~^^ text | pg_similarity.matching_tokenizer (enum) pg_similarity.matching_threshold (float4) pg_similarity.matching_is_normalized (bool) |
| Monge-Elkan Coefficient | mongeelkan(text, text) returns float4 | text ~|| text | pg_similarity.mongeelkan_tokenizer (enum) pg_similarity.mongeelkan_threshold (float4) pg_similarity.mongeelkan_is_normalized (bool) |
| Needleman-Wunsch Coefficient | needlemanwunsch(text, text) returns float4 | text ~#~ text | pg_similarity.needlemanwunsch_threshold (float4) pg_similarity.needlemanwunsch_is_normalized (bool) |
| Overlap Coefficient | overlapcoefficient(text, text) returns float4 | text ~** text | pg_similarity.overlap_tokenizer (enum) pg_similarity.overlap_threshold (float4) pg_similarity.overlap_is_normalized (bool) |
| Q-Gram Distance | qgram(text, text) returns float4 | text ~~~ text | pg_similarity.qgram_threshold (float4) pg_similarity.qgram_is_normalized (bool) |
| Smith-Waterman Coefficient | smithwaterman(text, text) returns float4 | text ~=~ text | pg_similarity.smithwaterman_threshold (float4) pg_similarity.smithwaterman_is_normalized (bool) |
| Smith-Waterman-Gotoh Coefficient | smithwatermangotoh(text, text) returns float4 | text ~!~ text | pg_similarity.smithwatermangotoh_threshold (float4) pg_similarity.smithwatermangotoh_is_normalized (bool) |
The several parameters control the behavior of the pg_similarity functions and operators. I don't explain in detail each parameter because they can be classified in three classes: tokenizer, threshold, and normalized.
Set parameters at run time.
mydb=# show pg_similarity.levenshtein_threshold; pg_similarity.levenshtein_threshold ------------------------------------- 0.7 (1 row) mydb=# set pg_similarity.levenshtein_threshold to 0.5; SET mydb=# show pg_similarity.levenshtein_threshold; pg_similarity.levenshtein_threshold ------------------------------------- 0.5 (1 row) mydb=# set pg_similarity.cosine_tokenizer to camelcase; SET mydb=# set pg_similarity.euclidean_is_normalized to false; SET
Simple tables for examples.
mydb=# create table foo (a text); CREATE TABLE mydb=# insert into foo values('Euler'),('Oiler'),('Euler Taveira de Oliveira'),('Maria Taveira dos Santos'),('Carlos Santos Silva'); INSERT 0 5 mydb=# create table bar (b text); CREATE TABLE mydb=# insert into bar values('Euler T. de Oliveira'),('Euller'),('Oliveira, Euler Taveira'),('Sr. Oliveira'); INSERT 0 4
Example 1: Using similarity functions cosine, jaro, and euclidean.
mydb=# select a, b, cosine(a,b), jaro(a, b), euclidean(a, b) from foo, bar; a | b | cosine | jaro | euclidean ---------------------------+-------------------------+----------+----------+----------- Euler | Euler T. de Oliveira | 0.5 | 0.75 | 0.579916 Euler | Euller | 0 | 0.944444 | 0 Euler | Oliveira, Euler Taveira | 0.57735 | 0.605797 | 0.552786 Euler | Sr. Oliveira | 0 | 0.505556 | 0.225403 Oiler | Euler T. de Oliveira | 0 | 0.472222 | 0.457674 Oiler | Euller | 0 | 0.7 | 0 Oiler | Oliveira, Euler Taveira | 0 | 0.672464 | 0.367544 Oiler | Sr. Oliveira | 0 | 0.672222 | 0.225403 Euler Taveira de Oliveira | Euler T. de Oliveira | 0.75 | 0.79807 | 0.75 Euler Taveira de Oliveira | Euller | 0 | 0.677778 | 0.457674 Euler Taveira de Oliveira | Oliveira, Euler Taveira | 0.866025 | 0.773188 | 0.8 Euler Taveira de Oliveira | Sr. Oliveira | 0.353553 | 0.592222 | 0.552786 Maria Taveira dos Santos | Euler T. de Oliveira | 0 | 0.60235 | 0.5 Maria Taveira dos Santos | Euller | 0 | 0.305556 | 0.457674 Maria Taveira dos Santos | Oliveira, Euler Taveira | 0.288675 | 0.535024 | 0.552786 Maria Taveira dos Santos | Sr. Oliveira | 0 | 0.634259 | 0.452277 Carlos Santos Silva | Euler T. de Oliveira | 0 | 0.542105 | 0.47085 Carlos Santos Silva | Euller | 0 | 0.312865 | 0.367544 Carlos Santos Silva | Oliveira, Euler Taveira | 0 | 0.606662 | 0.42265 Carlos Santos Silva | Sr. Oliveira | 0 | 0.507728 | 0.379826 (20 rows)
Example 2: Using operator levenshtein (~==) and changing its threshold at run time.
mydb=# show pg_similarity.levenshtein_threshold; pg_similarity.levenshtein_threshold ------------------------------------- 0.7 (1 row) mydb=# select a, b, lev(a,b) from foo, bar where a ~== b; a | b | lev ---------------------------+----------------------+---------- Euler | Euller | 0.833333 Euler Taveira de Oliveira | Euler T. de Oliveira | 0.76 (2 rows) mydb=# set pg_similarity.levenshtein_threshold to 0.5; SET mydb=# select a, b, lev(a,b) from foo, bar where a ~== b; a | b | lev ---------------------------+----------------------+---------- Euler | Euller | 0.833333 Oiler | Euller | 0.5 Euler Taveira de Oliveira | Euler T. de Oliveira | 0.76 (3 rows)
Example 3: Using operator qgram (~~~) and changing its threshold at run time.
mydb=# set pg_similarity.qgram_threshold to 0.7; SET mydb=# show pg_similarity.qgram_threshold; pg_similarity.qgram_threshold ------------------------------- 0.7 (1 row) mydb=# select a, b,qgram(a, b) from foo, bar where a ~~~ b; a | b | qgram ---------------------------+-------------------------+---------- Euler | Euller | 0.8 Euler Taveira de Oliveira | Euler T. de Oliveira | 0.77551 Euler Taveira de Oliveira | Oliveira, Euler Taveira | 0.807692 (3 rows) mydb=# set pg_similarity.qgram_threshold to 0.35; SET mydb=# select a, b,qgram(a, b) from foo, bar where a ~~~ b; a | b | qgram ---------------------------+-------------------------+---------- Euler | Euler T. de Oliveira | 0.413793 Euler | Euller | 0.8 Oiler | Euller | 0.4 Euler Taveira de Oliveira | Euler T. de Oliveira | 0.77551 Euler Taveira de Oliveira | Oliveira, Euler Taveira | 0.807692 Euler Taveira de Oliveira | Sr. Oliveira | 0.439024 (6 rows)
Example 4: Using a set of operators using the same threshold (0.7) to ilustrate that some similarity functions are appropriated to certain data domains.
mydb=# select * from bar where b ~@@ 'euler'; -- jaro-winkler operator b ---------------------- Euler T. de Oliveira Euller (2 rows) mydb=# select * from bar where b ~~~ 'euler'; -- qgram operator b --- (0 rows) mydb=# select * from bar where b ~== 'euler'; -- levenshtein operator b -------- Euller (1 row) mydb=# select * from bar where b ~## 'euler'; -- cosine operator b --- (0 rows)
Copyright © 2008-2018 Euler Taveira de Oliveira
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.


| Investment {sandwich} | R Documentation |
US data for fitting an investment equation.
data(Investment)
An annual time series from 1963 to 1982 with 7 variables.
nominal gross national product (in billion USD),
nominal gross private domestic investment (in billion USD),
price index, implicit price deflator for GNP,
interest rate, average yearly discount rate charged by the New York Federal Reserve Bank,
real GNP (= GNP/Price),
real investment (= Investment/Price),
approximation to the real interest rate (= Interest - 100 * diff(Price)/Price).
Table 15.1 in Greene (1993)
Greene W.H. (1993). Econometric Analysis, 2nd edition. Macmillan Publishing Company, New York.
Executive Office of the President (1984). Economic Report of the President. US Government Printing Office, Washington, DC.
## Willam H. Greene, Econometric Analysis, 2nd Ed. ## Chapter 15 ## load data set, p. 411, Table 15.1 data(Investment) ## fit linear model, p. 412, Table 15.2 fm <- lm(RealInv ~ RealGNP + RealInt, data = Investment) summary(fm) ## visualize residuals, p. 412, Figure 15.1 plot(ts(residuals(fm), start = 1964), type = "b", pch = 19, ylim = c(-35, 35), ylab = "Residuals") sigma <- sqrt(sum(residuals(fm)^2)/fm$df.residual) ## maybe used df = 26 instead of 16 ?? abline(h = c(-2, 0, 2) * sigma, lty = 2) if(require(lmtest)) { ## Newey-West covariances, Example 15.3 coeftest(fm, vcov = NeweyWest(fm, lag = 4)) ## Note, that the following is equivalent: coeftest(fm, vcov = kernHAC(fm, kernel = "Bartlett", bw = 5, prewhite = FALSE, adjust = FALSE)) ## Durbin-Watson test, p. 424, Example 15.4 dwtest(fm) ## Breusch-Godfrey test, p. 427, Example 15.6 bgtest(fm, order = 4) } ## visualize fitted series plot(Investment[, "RealInv"], type = "b", pch = 19, ylab = "Real investment") lines(ts(fitted(fm), start = 1964), col = 4) ## 3-d visualization of fitted model if(require(scatterplot3d)) { s3d <- scatterplot3d(Investment[,c(5,7,6)], type = "b", angle = 65, scale.y = 1, pch = 16) s3d$plane3d(fm, lty.box = "solid", col = 4) }


Honourable Mention: Die Hard and yes it is a Christmas Movie.
My interest in joining Code Nation started when I was intoduced to the Develop course.
Those three weeks really opened my eyes to the possibility of a career in Coding and Web Development.
I have always had an interest in tech and the idea of a career in the tech industry and I would really like to chase this goal.
Having the oppourtunity to learn the skills and to be taught by such a great team makes it a no brainer to choose Code Nation.
Started learning JS on week 1 and focused on many aspects such as Variables, Arrays, Functions, and Loops just to name a few.
I initially learned the basics on the Develop course however I am looking forward to really learning more aspects of HTML and CSS.


about us
We are Anna and Naomi Jaffe, partners and managers of Tuition Hot Sauce. As sisters, we have our disputes. Nevertheless, we are always able to persevere through our differences. This is what makes us perfect business partners. Along with our passion for entrepreneurship, we share a love of locally sourced food products. Outside of our hot sauce business, we go to Fairview High School in beautiful Boulder, Colorado.
our ingredients
Our hot sauce contains the freshest hot peppers to acquire the perfect taste and quality. Our most popular peppers are red thai chili, habanero, jalapeño, and anaheim. We grow all of our peppers in our own garden seasonally; no pesticides, all organic, and in great hands. Additional ingredients for our hot sauces include vinegar and other organic vegetables. None of our hot sauces contain any artificial ingredients or additives. They are all gluten free and vegan as well!
earnings for education
As well as our passion for hot sauce, we Jaffe sisters hold education close to our hearts. We believe passion goes hand in hand with knowledge. We therefore promote our own educations through our delectable hot sauces. Our business gives us the opportunity to exercise our previous knowledge and to absorb new knowledge along the way. The stages of our educations are stepping stones. These stepping stones motivate us to improve our entrepreneurship skills. The next stepping stone, or rather, milestone, is college. Our journey towards higher education starts now, at the headquarters of Tuition Hot Sauce.


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Gradients and color stops
The Canvas API uses a rather convoluted way to add color data to a CanvasGradient interface object:
- the object is created first on the <canvas> context engine where it is to be applied, with start and end coordinates,
- then color stops are individually added to it afterwards.
- This needs to be done for every gradient applied to a context engine before any fill or stroke operation using that gradient.
- And only one gradient may be applied to the context engine at any time.
The specificity of the above requirements - in particular relating to position coordinates - and the inability to update the CanvasGradient beyond adding color stops to it, means that storing these objects for future use is not a useful proposition … especially in a dynamic environment where we want the gradient to move in-step with an entity, or animate its colors in some way.
Scrawl-canvas overcomes this problem through the use of Palette objects which separate a gradient-type style’s color-stop data from its positioning data. We treat Canvas API CanvasGradient
objects as use-once-and-dispose objects, generating them in a just-in-time fashion for each entity’s stamp
operation in the Display cycle.
Palette objects store their color data in a colors
attribute object:
{
name: "mygradient_palette",
colors: {
"0 ": [0, 0, 0, 1],
"350 ": [255, 0, 0, 1],
"650 ": [0, 0, 255, 1],
"999 ": [255, 255, 255, 1],
"index-label-between-0-and-999 ": [redValue, greenValue, blueValue, alphaValue]
},
}
To set
the Palette object’s colors
object, either when creating the gradient-type style or at some point afterwards, we can use CSS color Strings instead of an array of values for each color:
myGradient.set({
colors: [
[0, 'black'],
[0, 'red'],
[0, 'blue'],
[0, 'white'],
]
});


-[ background ]-
For a class I took during the Spring of 2004, I had to write a program that plays tic tac toe. Technically, I should say “we,” since I had a partner, but I wrote the program and he did the manual enumeration of a tic tac toe decision tree. I think I picked the easier half of the assignment. If you want to see the tree, you’ll have to do that part of the assignment yourself.
The class was “Cognitive Engineering;” it was all about Turing machines and Turing tests and such. This assignment related to the cognitive aspects of problem solving and whether or not a machine could duplicate them. It’s a very (extremely amazingly ultra) simple AI.
-[ actual assignment ]-
PART A
Draw the state transition tree for playing tic-tac-toe. This diagram should outline all of the possible states of the game; starting with a blank diagram and moving to all possible ends. Note that who goes first (X or O) doesn’t really matter because when we get done, we can just replace all X’s with O’s and O’s with X’s to get the other tree. Because your program (see below) will have X go first, do this tree. Also, when making your tree realize that many different configurations are the same. For instance, there are only 3 possible initial moves (middle, side or corner) even though there are 9 spaces. If you can rotate the game to get an identical configuration, these are the same in terms of strategy (i.e. if you are playing tic-tac-toe, nobody cares if you rotate the paper before making your move).
PART B
Design and construct a computer program to play tic-tac-toe. Assume that ‘X’ always goes first, and that your opponent can be either ‘X’ or ‘O’. You may use any computer language you wish, within reason.
Material turned in for this assignment should include: an enumeration of the problem space; a written verbal description of your computer algorithm, and a disk containing your program.
-[ algorithm ]-
From the document we turned in:
This tic tac toe program was written in Turbo Pascal for Windows version 1.5. The algorithm that the program uses to play tic tac toe is rather simple. First, the program allows the user to choose if s/he wants to be play as X or O. If the user chooses O (and thus chooses to allow the computer to play first), the computer’s first move is to randomly select one of the corners. If the user chooses X (and thus, chooses to go first him/herself), the program chooses a space based on the user’s choice. If the user has selected a corner, the program chooses the middle. Otherwise, the program chooses a corner at random. After the first turn, the program is designed to play strategically. The strategy employed by the program is used until a winner is determined. The program is designed to first try to win. It examines all of the squares, and if it is capable of winning (having three X’s or O’s in a row, depending on whether it is X or O), it does so. If it cannot win, it again examines all nine squares and attempts to block the user from winning. If the user has two in a row and can win on the next turn, the program will block the user. If the program cannot win or block, it will attempt to play offensively. If either pair of side squares is free (e.g. above and below the middle or to the left and right of the middle), it will randomly pick one of these free sides. If no pair of side squares is free, it will try to take the middle square. If the middle square has already been taken, it will randomly choose a free corner. If no corner is free, it will randomly choose a free side. The program allows the user to play multiple times without exiting. Additionally, it keeps track of how many times it has won, how many times the user has won, and how many tie (or cat) games there have been. For more detail about how the program works, please see the source code.
-[ notes ]-
This was written in Turbo Pascal, for that ultra-retro feel (actually, because that was the only compiler I had at the time), but the executable file should work on most (all?) Win32 installations. All ASCII art included is believed to be in the public domain. If you use the code for anything, please let me know by leaving a comment. Thanks.
This needs work, but it can beat me. Of course, I always sucked at tic tac toe. According to the professor, there’s a flaw in the logic that allows the user to always win. I’ve been unable to find it, though. Please let me know if you find it.
We got a 100 on this project, despite the “flaw,” in case you’re wondering. I still got a B in the class, though.
-[ license ]-
-[ download ]-
[ releases ] | [ all files ] | [ source ] | [ binary ] | [ source + binary (.zip) ]


Upload an executable package — a ZIP archive containing a single executable in .exe format and optionally any number of additional libraries in .dll format.


E
Step-1
Basic
Education
Education
- Completed high school from R.S.M senior secondary school,Gorakhpur in 2014
- Completed Intermediate from Oxford Public school,Gorakhpur in 2016
- Cleared JEE Mains 2016.
- Cleared VITEEE with All India rank-495 in 2016
S
Step-2
SKILLS
- Basic knowledge of C/C++
- HTML
- CSS,SCSS
- Ruby on Rails
- Javascript,JQuery
- Bootstrap
- AJAX/JSON-Handling
- GIT/GITHUB
P
Step-3
Projects
- Rating and Review website completely in Ruby on rails
- Voting System WebApp
- A blog-engine for personal chat and posts
- An Online Library Portal
- Chatbot on NodeJs using socket.io
C
CASE 4
CYBER
SECURITY
SECURITY
I am just a noob in cyber-security but having a great interests on it.
Have some basic knowledge on Computer Networks.
Have some basic knowledge on Computer Networks.


THE UNDERGROUND COMEDY MOVIE
Reviewed by Harvey Karten, Ph.D. Phaedra Cinema Director: Vince Offer Writer: Vince Offer Cast: Geena Lee Nolin, Slash, Michael Clark Duncan, Joey Buttafuoco, Lightfield Lewis, Karen Black, Rebecca Chaney, Jeery Mongo Brownlee, Michael Parisi, Gloria Sperling, Bobby Lee
Humankind's foibles are so numerous that we don't wonder that satirical theater, printed words and later movies became a popular genre ever since Aristophanes spoofed the pretensions of Socrates 2500 years ago. Satire is my genre: but it's gotta be good. There's a difference, for example, between John Cleese's stellar role as Fawlty Towers on TV some two decades ago and John Cleese's embarrassing performance as a high-kicking transvestite in the abysmal recent comedy, "The Out-of-Towners." There's also a difference between the limp parody of high-school kids in "Jawbreakers" and the incisive send-up of American morality and ethics in the wonderful new teen-based film, "Election."
There's also the kind of movie that's so bad that it's good. Such is not "The Underground Comedy Movie," which is so bad that it's bad. Really atrocious. Its writer-director, young Vince Offer, supposedly took ten years to make this alleged comedy, with which he hopes to draw chuckles and guffaws by pure outrageousness. Remember how some people walked out of last year's "There's Something About Mary" because of a supposedly vulgar riff on the title character's hair mousse? "The Underground Comedy Movie" not only makes that movie look like "The Wizard of Oz," but director Offer actually sued the team that gave Cameron Diaz a new look for allegedly stealing fourteen--count 'em--fourteen ideas from his new film.
Though Offer insists that his film is "guaranteed to offend," presumably to affront those who remain in their seats throughout the 87 minutes, "The Underground Comedy Movie" is so unfinished, so lacking in varnish, that it can scarcely wound anyone--unless Offer can find someone around who is horrified to find gallons of milk, substituting for semen, poured over one actor after another. Actually a series of skits with performers popping up now and then, the movie showcases such events as "The Miss American Bag Lady Contest," using actual bag women in the L.A. area (or is it Tijuana?), who parade around in swim suits to be judged by four drunks lying in the gutter casting their votes. In a running skit on the theme "things you will never see," the film opens on two supermodels "taking a dump," and concludes with a hopelessly inept "jury making the right decision"--in which two white guys cast the jury's only "not guilty votes" in the case of a black defendant, claiming that the society is "only trying to keep the brother down."
 An actor named Bobby Lee plays "Sushi Papa." Doesn't Mr. Offer know that he's playing the role of a Chinese and not a Japanese? Michael Parisi performs in the role of the Godmother in yet another skit mocking one of the great cinema works of all time, but making an offer that the audience can easily resist.
"The Underground Comedy Movie" has humor, so to speak, directed at the 14-17-year-old demos, but since it would be rated NC-17 when it opens in theaters on May 7, its prime audience would presumably not even get to see the movie. For once, the juvenile set can thank the ratings system for keeping them out.
Not Rated. Running Time: 87 minutes. (C) 1999 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Switch the watering system on or off.
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Have you considered what equality and diversity issues are present and how they can help inform your proposal?
Have you used the effective proposal tool?
Be aware of the sentencing guidelines and relevant legislation. Remember to reference the sentencing intentions of the court, if they were made clear.
Consider all possible sentencing options, and whether or not the offender is past the custody threshold or suitable for a community order.
Consider and describe both aggravating or mitigating factors.
Consider whether specific needs such as gender, maturity, diversity make particular sentences more appropriate.
In cases of women, ensure that you think about women only disposals and that your proposal gives a robust community option.
Make clear how your proposals will manage risk.
What will the impact of the sentence be on the offender?
How will the proposed sentence affect the offender’s family and anyone for whom they have caring responsibilities?
Remember that evidence shows that imprisonment of mothers has significant impacts on those that they care for.
If relevant, what is not suitable in this case?
Are there any relevant alternative requirements that the offender may have been eligible for, but circumstances have made them unsuitable for participation?
Remember that your proposal should be commensurate with risk and need. Proposals should not be made for suspended sentence orders.


7. Examples.¶
7.1. Interference examples.¶
When two or more waves overlap each other in space a more or less complicated pattern of fringes can be observed on a screen placed in the overlap area. These fringes are the result of superposition of the fields, which cause local enhancement or decrement of the intensity depending on the phase and amplitude distribution of the waves in the observation regio. In this chapter we will demonstrate a number of interference examples.
7.2. Diffraction examples.¶
7.3. Non-diffractive Bessel beam.¶
A Bessel beam has the interesting property that it does not diffract and that it keeps its shape over large distances. Several meters, depending on parameters, can be realized. Applications of Bessel beams take advantage of the very large size of the focus, which cannot be obtained using lenses or mirrors. For example generation of a long narrow plasma channel can be realized using a high-power laser beam converted into a Bessel beam by an axicon lens.
Besides an axicon, a combination of an annular slit and a positive lens or concave mirror can be used in staed. In the following example a Poisson spot is generated by illuminating a circular disk by a plane mono-chromatic beam. The disk is positioned in the primary focus of a positive lens so that the waves originating from the edge of teh disk will be collimated. By blocking the rest of the incoming beam only the edge waves remain which results in a non-diffracting Bessel beam.
reference: J.Durnin, “Exact solutions for nondiffracting beams. I. The scalar theory.” JOSA A, Vol. 4, Issue 4, pp. 651-654 (1987)
7.4. Laser examples.¶
- 7.4.1. Hermite Gauss modes.
- 7.4.2. Laguerre Gauss modes.
- 7.4.3. Laguerre doughnut modes.
- 7.4.4. Laser simulation, stable laser resonator.
- 7.4.5. Unstable laser resonator.
- 7.4.6. Transformation of a fundamental Gauss mode into a doughnut mode with a spiral phase plate.
- 7.4.7. Transformation of high order Gauss modes.
- 7.4.8. Gauss TEM00 mode described with geometric optics.
7.5. Phase recovery.¶
7.6. Zernike aberration.¶
Any aberration in a circle can be decomposed over a sum of Zernike polynomials. The Zernike command accepts four arguments: 1. The radial order n 2. The azimuthal order m. 3. The radius, R 4. The amplitude of the aberration.


vignettes/CheckTypeDescriptions.rmd
CheckTypeDescriptions.rmd
The DataQualityDashboard functions by applying 20 parameterized check types to a CDM instance, resulting in over 3,351 resolved, executed, and evaluated individual data quality checks. For example, one check type might be written as
The number and percent of records with a value in the cdmFieldName field of the cdmTableName table less than plausibleValueLow.
This would be considered an atemporal plausibility verification check because we are looking for implausibly low values in some field based on internal knowledge. We can use this check type to substitute in values for cdmFieldName, cdmTableName, and plausibleValueLow to create a unique data quality check. If we apply it to PERSON.YEAR_OF_BIRTH here is how that might look:
The number and percent of records with a value in the year_of_birth field of the PERSON table less than 1850.
And, since it is parameterized, we can similarly apply it to DRUG_EXPOSURE.days_supply:
The number and percent of records with a value in the days_supply field of the DRUG_EXPOSURE table less than 0.
Version 1 of the tool includes 20 different check types organized into Kahn contexts and categories (link to paper). Additionally, each data quality check type is considered either a table check, field check, or concept-level check. Table-level checks are those evaluating the table at a high-level without reference to individual fields, or those that span multiple event tables. These include checks making sure required tables are present or that at least some of the people in the PERSON table have records in the event tables. Field-level checks are those related to specific fields in a table. The majority of the check types in version 1 are field-level checks. These include checks evaluating primary key relationship and those investigating if the concepts in a field conform to the specified domain. Concept-level checks are related to individual concepts. These include checks looking for gender-specific concepts in persons of the wrong gender and plausible values for measurement-unit pairs.
This article will detail each check type, its name, check level, description, definition, and to which Kahn context, category, and subcategory it belongs.
Name: measurePersonCompleteness
Level: Table check
Context:
Validation
Category: Completeness
Description: The number and percent of persons in the CDM that do not have at least one record in the cdmTableName table.
Definition: For each table indicated this check will count the number of persons from the PERSON table that do not have at least one record in the specified clinical event table. It may be that there are 100 persons listed in the PERSON table but only 30 of them have at least one record in the MEASUREMENT table. If the measurePersonCompleteness check is indicated for the MEASUREMENT table, the result will be 70%, meaning that 70% of the persons in the CDM instance do not have at least one record in MEASUREMENT.
Name: cdmField
Level: Field
check
Context: Verification
Category: Conformance
Subcategory: Relational
Description: A value indicating if all fields are present in the cdmTableName table.
Definition: For each table indicated this check will go through and determine if all fields are present as specified based on the CDM version. If the field is present, the resulting value will be 0; if the field is absent the resulting value will be 100.
Name: isRequired
Level: Field
check
Context: Validation
Category: Conformance
Subcategory: Relational
Description: The number and percent of records with a NULL value in the cdmFieldName of the cdmTableName that is considered not nullable
Definition: This check is meant to ensure that all NOT NULL constraints specified in the CDM version are followed. It will count up all records with a NULL value in the specified field of the specified table and return the percent of records in the table that violate the constraint.
Name: cdmDatatype
Level: Field
check
Context: Verification
Category: Conformance
Subcategory: Value
Description: A yes or no value indicating if the cdmFieldName in the cdmTableName is the expected data type based on the specification.
Definition: At present this will check only that fields that are supposed to be integers are the expected datatype. For a given field, it will count the number of records with a non-null, non-integer value.
Name: isPrimaryKey
Level: Field
check
Context: Verification
Category: Conformance
Subcategory: Relational
Description: The number and percent of records that have a duplicate value in the cdmFieldName field of the cdmTableName.
Definition: This check will make sure that all primary keys as specified in the CDM version are truly unique values in the database. While this should be caught by primary key constraints, some database management systems such as redshift do not enforce these.
Name: isForeignKey
Level: Field
check
Context: Verification
Category: Conformance
Subcategory: Relational
Description: The number and percent of records that have a value in the cdmFieldName field in the cdmTableName table that does not exist in the fkTableName table.
Definition: This check will make sure that all foreign keys as specified in the CDM version have a value in the related primary key field. While this should be caught by foreign key constraints, some database management systems such as redshift do not enforce these.
Name: fkDomain
Level: Field
check
Context: Verification
Category: Conformance
Subcategory: Value
Description: The number and percent of records that have a value in the cdmFieldName field in the cdmTableName table that do not conform to the fkDomain domain.
Definition: It is often the case that standard concept fields in the OMOP CDM should belong to a certain domain. All possible domains are listed in the vocabulary table DOMAIN and the expected domain for CDM fields are listed as part of the CDM documentation. For example, in the field PERSON.gender_concept_id all concepts in that field should conform to the gender domain. This check will search all concepts in a field and count the number of records that have concepts in the field that do not belong to the correct domain.
Name: fkClass
Level: Field
check
Context: Verification
Category: Conformance
Subcategory: Computational
Description: The number and percent of records that have a value in the cdmFieldName field in the cdmTableName table that do not conform to the fkClass class.
Definition: There is the occasional field in the OMOP CDM that expects not only concepts of a certain domain, but of a certain concept class as well. The best example is the drug_concept_id field in the DRUG_ERA table. Drug eras represent the span of time a person was exposed to a particular drug ingredient so all concepts in DRUG_ERA.drug_concept_id are of the drug domain and ingredient class. This check will search all concepts in a field and count the number of records that have a concept in the field that do not belong to the correct concept class.
Name: isStandardValidConcept
Level: Field check
Context:
Verification
Category: Conformance
Description: The number and percent of records that do not have a standard, valid concept in the cdmFieldName field in the cdmTableName table.
Definition: In order to standardize not only the structure but the vocabulary of the OMOP CDM, certain fields in the model require standard, valid concepts while other fields do not. For example, in the PERSON table, the field gender_concept_id MUST be a standard, valid concept: either 8532 or 8507. In contrast the field gender_source_concept_id can be any concept, standard or no. This check will count the number of records that have a concept in a given field that are not standard and valid.
Name: measureValueCompleteness
Level: Field check
Context:
Verification
Category:Completeness
Description: The number and percent of records with a NULL value in the cdmFieldName of the cdmTableName.
Definition: This check will count the number of records with a NULL value in a specified field. This is different from the isRequired check because it will run this calculation for all tables and fields whereas the isRequired check will only run for those fields deemed required by the CDM specification. Often the thresholds for failure are set at different levels between these checks as well.
Name: standardConceptRecordCompleteness
Level: Field check
Context:
Verification
Category: Completeness
Description: The number and percent of records with a value of 0 in the standard concept field cdmFieldName in the cdmTableName table.
Definition: It is important to understand how well source values were mapped to standard concepts. This check will count the number of records in a standard concept field (condition_concept_id, drug_concept_id, etc.) with a value of 0 rather a standard concept. NOTE for the field unit_concept_id in the MEASUREMENT and OBSERVATION tables both the numerator and denominator are limited to records where value_as_number is not null. This prevents over-inflation of the numbers and focuses the check to records that are eligible for a unit value.
Name: sourceConceptRecordCompleteness
Level: Field check
Context:
Verification
Category: Completeness
Description: The number and percent of records with a value of 0 in the source concept field cdmFieldName in the cdmTableName table.
Definition: This check will count the number of records in a source concept field (condition_source_concept_id, drug_source_concept_id) with a value of 0. This is useful since source values that are represented by concepts in the vocabulary have automatic mappings to standard concepts. Using this check along with the standardConceptRecordCompletness check can help identify any vocabulary mapping issues during ETL.
Name: sourceValueCompleteness
Level: Field check
Context:
Verification
Category: Completeness
Description: The number and percent of distinct source values in the cdmFieldName field of the cdmTableName table mapped to 0.
Definition: This check will look at all distinct source values in the specified field and calculate how many are mapped to 0. This should be used in conjunction with the standardConceptRecordCompletness check to identify any mapping issues in the ETL.
Name: plausibleValueLow
Level:
Field check
Context: Verification
Category: Plausibility
Subcategory: Atemporal
Description: The number and percent of records with a value in the cdmFieldName field of the cdmTableName table less than plausibleValueLow.
Definition: This check will count the number of records that have a value in the specified field that is lower than some value. This is the field-level version of this check so it is not concept specific. For example, it will count the number of records that have an implausibly low value in the year_of_birth field of the PERSON table.
Name: plausibleValueHigh
Level:
Field check
Context: Verification
Category: Plausibility
Subcategory: Atemporal
Description: The number and percent of records with a value in the cdmFieldName field of the cdmTableName table greater than plausibleValueHigh.
Definition: This check will count the number of records that have a value in the specified field that is higher than some value. This is the field-level version of this check so it is not concept specific. For example, it will count the number of records that have an implausibly high value in the year_of_birth field of the PERSON table.
Name: plausibleTemporalAfter
Level: Field check
Context:
Verification
Category: Plausibility
Subcategory: Temporal
Description: The number and percent of records with a value in the cdmFieldName field of the cdmTableName that occurs prior to the date in the plausibleTemporalAfterFieldName field of the plausibleTemporalAfterTableName table.
Definition: This check is attempting to apply temporal rules to a CDM instance. For example, it will check to make sure that all visit records for a person in the VISIT_OCCURRENCE table occur after the person’s birth.
Name: plausibleDuringLife
Level: Field check
Context:
Verification
Category: Plausibility
Subcategory: Temporal
Description: If yes, the number and percent of records with a date value in the cdmFieldName field of the cdmTableName table that occurs after death.
Definition: This check will calculate the number of records that occur after a person’s death. This is called plausibleDuringLife because turning it on indicates that the specified dates should occur during a person’s lifetime, like drug exposures, etc.
Name: plausibleValueLow
Level:
Concept check
Context: Verification
Category: Plausibility
Subcategory: Atemporal
Description: For the combination of CONCEPT_ID conceptId (conceptName) and UNIT_CONCEPT_ID unitConceptId (unitConceptName), the number and percent of records that have a value less than plausibleValueLow.
Definition: This check will count the number of records that have a value in the specified field with the specified concept_id and unit_concept_id that is lower than some value. This is the concept-level version of this check so it is concept specific and therefore the denominator will only be the records with the specified concept and unit. For example, it will count the number of records that have an implausibly low value in the value_as_number field of the MEASUREMENT table where MEASUREMENT_CONCEPT_ID = 2212241 (Calcium; total) and UNIT_CONCEPT_ID = 8840 (milligram per deciliter). These implausible values were determined by a team of physicians and are meant to be biologically implausible, not just lower than the normal value.
Name: plausibleValueHigh
Level:
Concept check
Context: Verification
Category: Plausibility
Subcategory: Atemporal
Description: For the combination of CONCEPT_ID conceptId (conceptName) and UNIT_CONCEPT_ID unitConceptId (unitConceptName), the number and percent of records that have a value higher than plausibleValueHigh.
Definition: This check will count the number of records that have a value in the specified field with the specified concept_id and unit_concept_id that is higher than some value. This is the concept-level version of this check so it is concept specific and therefore the denominator will only be the records with the specified concept and unit. For example, it will count the number of records that have an implausibly high value in the value_as_number field of the MEASUREMENT table where MEASUREMENT_CONCEPT_ID = 2212241 (Calcium; total) and UNIT_CONCEPT_ID = 8840 (milligram per deciliter). These implausible values were determined by a team of physicians and are meant to be biologically implausible, not just higher than the normal value.
Name: plausibleGender
Level:
Concept check
Context: Validation
Category: Plausibility
Subcategory: Atemporal
Description: For a CONCEPT_ID conceptId (conceptName), the number and percent of records associated with patients with an implausible gender (correct gender = plausibleGender).
Definition: This check will count the number of records that have an incorrect gender associated with a gender-specific concept_id. This check is concept specific and therefore the denominator will only be the records with the specified concept. For example it will count the number of records of prostate cancer that are associated with female persons.
Name: plausibleUnitConceptIds
Level: Concept check
Context:
Verification
Category: Plausibility
Subcategory: Atemporal
Description: The number and percent of records for a given CONCEPT_ID conceptId (conceptName) with implausible units (i.e. UNIT_CONCEPT_ID NOT IN (plausibleUnitConceptIds))
Definition: This check will count the number of records for a given concept that do not use one of the allowable units, which is represented in the csv file as a quoted comma-separated list of unit_concept_ids. If no units are specified for plausibleUnitConceptIds, this check will count the number of records that have non-NULL units. This check is concept specific and therefore the denominator will only be the records with the specified concept. For example it will count the number of records or Glomerular Filtration Rate (CONCEPT_ID = 3030354) that do not use unit mL/min/1.73.m2 (UNIT_CONCEPT_ID = 9117).


usp.ime.gclib.net.communication
Interfaces
IReceiveListener
Classes
CommunicationSocket
IP
Receiver
TCPReceiver
TCPSender
UDPReceiver
UDPSender


Welcome to the developer resources website for Terasology
An open source voxel world - imagine the possibilities!
So, you have decided to develop with Terasology? 😉 Then this is the best place for you! From this site, you can get to all sorts of tutorials you might ever need, as well to the detailed documentation of all stuff of ours. On this site, you will also find showcase of our officially recommended API modules providing the whole range of functionality your shiny new module might need, so you can maintain the highest possible compatibility with other modules.
{% for post in paginator.posts %} {% assign loopindex = forloop.index | modulo: 2 %} {% if loopindex == 1 or forloop.first %} {% endif %} {% endfor %}
{% if paginator.previous_page %}
keyboard_arrow_left
{% else %}
keyboard_arrow_left
{% endif %}
{{ paginator.page }} of {{ paginator.total_pages }}
{% if paginator.next_page %}
keyboard_arrow_right
{% else %}
keyboard_arrow_right
{% endif %}


Power allows you to track your time, while you work. meaning you never overcharge, or get undercharged.


KEEP YOUR DISTANCE
We create a new system to adapt the requirements for our system. Creating a convenient life and productivity is our aim for this system. Technology has vastly increased productivity since the ability of computers to solve complex mathematical equations has allowed them to speed up practically every task. Computer modelling permits engineers to simulate structures, vehicles, and materials to provide information on performance before prototyping
The team has created the whole system to demonstrate object detection and measure distances between individuals over a period. Our user can use the camera to conduct and provide all the necessary information to the client through the system. Furthermore, the user can recheck the data under the authorization of the system. It will be a step to support future technology.


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Continue reading... | Comments (124)Project One
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Measures/day
3Systolic mean
12.30Diastolic mean
8.20Last measure
2 hours agoBlood pressure normality
Percentage of days with good, regular or bad Blood Pressure
Historic chart
Evolution of systolic and diastolic measures
Measures/day
3Systolic mean
12.30Diastolic mean
8.20Last measure
2 hours agoPercentage of days with good, regular or bad Blood Pressure
Evolution of systolic and diastolic measures


Warning
Running Jenkins on WebSphere 7.x requires WebSphere <IP_ADDRESS> or above. Jenkins will NOT work on WebSphere versions <IP_ADDRESS> - <IP_ADDRESS>. See issue 3274 in the #Known Issues section for further information!
- The following installation was tested with WAS 6.1 ND, WAS 7.0 ND, WAS 8.0 Developer Edition and WAS 8.5 Developer Edition
- Some links in Jenkins are broken in WebSphere, but there is usually either another way to access them or it is intuitive to correct the URL in the address bar. It is not critical problem, thus no time to investigate it further...
Table of contents
Requirements
- The following Hudson versions run on WAS:
- Hudson 1.238 to 1.126 (see hudson: issue 1680).
- Hudson/Jenkins 1.291 or greater (see hudson: issue 3069).
- Other versions won't run on WAS.
- WAS versions:
- Previously mentioned Jenkins versions should all run on WAS 6.1.
- With WAS 7.0 (<IP_ADDRESS>, <IP_ADDRESS> and <IP_ADDRESS>), a WebSphere class loader issue will prevent Hudson 1.291 or greater to start and use all its internal components (see hudson: issue 3274). The bug has been identified and fixed by IBM, but the fix is not yet publicly available. This page will be updated when it's done.
See also the #Known Issues section.
Installation procedure
There are two ways to install Jenkins on WAS:
Admin console installation
The following works fine on WAS 6.1, WAS 7.0, WAS 8.0 and WAS 8.5.
The installation instructions are simple as for each WAR Module.
- Start the installation wizard under Applications - Install new application.
- Select jenkins.war and enter the context root for the application - for example jenkins
After finishing the wizard click the installed application (jenkins_war for example) and perform the following steps to change the classloader mode:
- Click Manage Modules
- Click Hudson / Jenkins
- From the "Class loader order" drop-down select "Classes loaded with application class loader first" ("Classes loaded with local class loader first" V8.5)
- Save the changes
- Restart the server - this is necessary to apply the changed class loader mode. You need to restart only the server onto which jenkins is installed. (V8.5: no need to restart the server, just start the Jenkins app).
If you try to install Jenkins 1.450 on WAS 8, you may face some exceptions mentioned in JENKINS-12334. This may occur, if an SLF4J implementation is missing.
Since log4j is already on the Classpath, you might want to download the slf4j-log4j bridge and copy that JAR into the WEB-INF/lib folder of Jenkins.
Installing through wsadmin
Likely needs to be edited to work with Jenkins
If you plan to install Hudson on WAS 7, use the Admin console as the script below won't work (due to some changes in IBM codebase which requires some fixing).
A jacl script called hudson.jacl and configuration file config.properties are provided to install and configure hudson on WAS 6.1 ND.
Before running hudson.jacl make the necessary changes in the config.properties file - adjust the server envoronment, the context root, the virtual host mapping and optional the hudson home directory. After that run following command from the deployment manager bin directory:
wsadmin(.sh) -f <path_to_hudson.jacl>/hudson.jacl <path_to_config.properties>/config.properties <command> <path_to_hudson.war>/hudson.war
where the <command> is:
- deploy - use it for first time installation. it performs installation only
- redeploy - use it to upgrade hudson. It uninstalls previously installed hudson instance and installs the new version from the hudson.war
- undeploy - use to uninstall hudson. In this case the last parameter is not necessary.
The paths must be provided in UNIX style even on Windows - d:/test/hudson/hudson.war instead of d:\test\hudson\hudson.war
After successfully installation the server must be restarted manually - the script doesn't provide this functionality at the moment, but the stopServer and startServer commands can be used for this purpose.
If security is enabled on the server either provide -user and -password arguments to the command or adjust the soap.client.props file with the login information in order to be able to connect to the server's admin interface.
The script is not tested with the base server edition, but should work on it - the only change is to disable the node synchronization in the saveConfigAndSync procedure - the line to comment can be found in hudson.jacl - search for BASE or for saveConfigAndSync.
Installation using Jython Scripts
These scripts make heavy use of 'wsadminlib.py', a large monolithic jython library for WebSphere Administration.
The original scripts can be found here Sample Scripts for WebSphere Application Server.
The 'wsadminlib.py' file that is included here has been modified to include some extra functions that were not present in the original.
There are four script files that have been provided. They have been tested under WAS 6.1 and AIX. Feedback is sought for WAS 7.0 and other platforms.
- createHudsonServer.py
- deleteHudsonServer.py
- installHudson.py
- wsadminlib.py
They are executed using the wsadmin.sh tool:
/was61/WebSphere/AppServer/bin/wsadmin.sh -lang jython -f <<<script file>>>
Attachments
- createHudsonServer.py - wsadmin script to create the Hudson Server.
- deleteHudsonServer.py - wsadmin script to delete the Hudson Server.
- installHudson.py - wsadmin script to install the Hudson Application into the Hudson Server.
- wsadminlib.py - The wsadmin Über library.
Additional Setup
Please remember that a 64-bit JVM takes about 60% more heap space than a 32-bit JVM does. Size your VM, if using 64-bit, accordingly. I'm using 1Gb (-Xmx1024M) heap, as 512Mb was causing regular heap dumps due to out of memory errors.
Notes
This is simply how I do it. It does require additional machine resources, as it takes a complete JVM, but I prefer to create a server per application which provides for better application isolation and tuning capabilities.
CVS SSH authentication
For CVS ssh authentification following environment entry is required in the application server: CVS_RSH=ssh. This can be set in the admin console in:
Application Servers / <server> / Java and Process Management / Process Definition / Environment Entries
Upgrading
Changing the WAR file to reference the newest dom4j library (see hudson: issue 1680):
- Unpack hudson.war and navigate to WEB-INF/lib directory.
- Download dom4j-1.6.1.jar and copy it to the WEB-INF/lib directory.
- Delete dom4j-1.5.jar
- Zip the war file again.
Following steps should be done upgrading running hudson instance:Do not remove dom4j-1.6.1-hudson-1.jar!
- Backup the current configuration
tar -cvf hudsonXX.tar <hudson_home_dir>
* Redeploy hudson with the provided scripts as follows:
Edit config.properties to the environment and run (see Installing through wsadmin above for further information):
wsadmin.sh -f hudson.jacl config.properties redeploy hudson.war
The above command assumes that wsadmin.sh is in PATH and hudson.jacl, config.properties and hudson.war are in the current dir.
- Restart the application server.
Setting up WAS for the update center to work
As of version 1.23x? hudson provides update center. In order to be able to use it with the WebSphere's JDK a trusted certificate must be imported in the server's keystore. To acomplish this following steps are necessary:
- Download the RapidSSL root certificate from here .
- Install the certificate using #keytool (command-line mode) or IBM Key Management (GUI mode).
- After importing the certificate the application server must be restarted. After that it should be possible to upgrade your plugins from the Update Center in hudson (don't forget to set the proxy if you have one).
Importing the certificate using the keytool command (command-line mode)
keytool -import -trustcacerts -file rapidssl_01.cer -keystore <server_trust.p12> -storepass WebAS -storetype PKCS12 -alias rapidssl
where as:
- <server_trust.p12> points to the trust.p12 file of the server on which hudson is running. It is found in
<WAS_Install_Dir>/profiles/<profile>/config/cells/<cellname>/node/<nodename>
. - The command uses the default WebSphere password for the keystore file (WebAS) - if it has been changed change the command accordingly.
Issuing the command brings a message that the certificate is found in the system wide keystore - confirm with yes that it should be updated.
Eg:
$ cd /tmp $ wget http://www.rapidssl.com/cps/rapidssl_01.cer <snip> $ /was61/WebSphere/AppServer/java/jre/bin/keytool -import -trustcacerts -file rapidssl_01.cer -keystore /was61/WebSphere/AppServer/profiles/AppSrv01/config/cells/localhostCell01/nodes/localhostNode01/trust.p12 -storepass WebAS -storetype PKCS12 -alias rapidssl Certificate already exists in system-wide CA keystore under alias <equifaxsecureca> Do you still want to add it to your own keystore? [no]: yes Certificate was added to keystore $
Importing the certificate using IBM Key Management (GUI mode)
- Start
ikeyman.bat
/ikeyman.sh
located in<WAS_Install_Dir>/bin
. - Go to Key Database File > Open.
- In the Open window, set the Key database type to PKCS12 _, then use the _Browse button to locate the
trust.p12
file (it's in<WAS_Install_Dir>/profiles/<profile>/config/cells/<cellname>/node/<nodename>
). - Enter the password for this keystore (the default value is WebAS).
- In the Key database content, select the Signer Certificates item.
- Click on the Add... button, locate the certificate you've previously downloaded, and click on OK.
- When asked to Enter a Label, set the value to rapidssl and click on OK.
- Go to Key Database File > Save As... and overwrite the existing
trust.p12
file (it's a good idea to make a copy of it before...). - When asked for a password, set it to the previous value (again, WebAS is the default one).
- Exit from the tool.
Known Issues
Following table tries to summarize the known issues. If there is a fix from IBM its PK Id will be referenced.
Issue # |
Hudson - Affects |
Hudson - Fixed In |
WAS - Affects |
WAS - Fixed In |
PK # |
|---|---|---|---|---|---|
1.213 |
1.237 |
All |
|
|
|
1.271 |
1.291 |
All |
|
|
|
> 1.285 |
|
7.0.0.x |
<IP_ADDRESS> |
PK86109 |
JIRA Plugin
I'm experiencing VerifyError on WAS 7.0 trying to use the provided JIRA plugin due to dependencies on the Stax API included in the stax-api-1.0.1.jar. I've simply removed the jar file from hudson's lib directory and it works ok for me. For further details see this thread.
Attachments:
hudson.jacl (application/octet-stream)
wsadminlib.py (application/octet-stream)
createHudsonServer.py (application/octet-stream)
deleteHudsonServer.py (application/octet-stream)
installHudson.py (application/octet-stream)


| Path: | vendor/plugins/juggernaut/README |
| Last Update: | Sun Feb 03 01:29:21 +0800 2008 |
Juggernaut
Author: Alex MacCaw E-Mail Address<EMAIL_ADDRESS>License: MIT Website: http://juggernaut.rubyforge.org Blog: http://www.eribium.org
The Juggernaut plugin for Ruby on Rails aims to revolutionize your Rails app by letting the server initiate a connection and push data to the client. In other words your app can have a real time connection to the server with the advantage of instant updates. Although the obvious use of this is for chat, the most exciting prospect for me is collaborative cms and wikis.
What Happens:
1. Client A opens socket connection to the socket server 2. Client B makes Ajax call to Rails 3. Rails sends message to the socket server 4. Socket server broadcasts message to clients
Juggernaut Features:
* Allows a real time connection with a client - Rails can literally push javascript in real time to the client which is then evaluated. * Push server - written in Ruby. * Integrated, as a plugin, into Rails. * Subscribers can subscribe to multiple channels, and broadcasters can broadcast to multiple channels. * Subscribers can provide a 'unique_id' and broadcasters can send data to specific clients. * Add and remove channels at runtime * Uses Flash 8 - installed on more than 98% of computers. * Supports all the major browsers (uses ExternalInterface): Firefox 1+, IE 6+ and Safari 2+.
Requirements:
* Rails 2.0.2 or edge * json gem (gem install json) * EventMachine gem (gem install eventmachine) * juggernaut gem (gem install juggernaut)
INSTALLATION
1. From your Rails Dir: script/plugin install http://juggernaut.rubyforge.org/svn/trunk/juggernaut 2. Make sure to include the appropriate JavaScripts in your views/layouts in the header of your views <%= javascript_include_tag 'prototype', :juggernaut %> 3. Add this to your view/layout head: <%= juggernaut %> 4. Make sure the juggernaut gem is installed (gem install juggernaut) and run: juggernaut -g juggernaut.yml juggernaut -c juggernaut.yml 5. Run script/server and visit the Jugged up page. 6. Then, to send data to juggernaut, execute this in the console: Juggernaut.send_to_all("alert('hi from juggernaut')")
Usage
To demonstrate Juggernaut I‘ll walk you through building a simple chat.
Start the push server going by running: juggernaut -g juggernaut.yml juggernaut -c juggernaut.yml
The chat controller:
class ChatController < ApplicationController
def index end def send_data render :juggernaut do |page| page.insert_html :top, 'chat_data', "
#{h params[:chat_input]}
" end render :nothing => true end
end
The index.rhtml
  <%= javascript_include_tag :defaults, :juggernaut %> <%= juggernaut %>   <%= form_remote_tag( :url => { :action => :send_data }, :complete => "$('chat_input').value = ''" ) %> <%= text_field_tag( 'chat_input', '', { :size => 20, :id => 'chat_input'} ) %> <%= submit_tag "Add" %> </form>

Start the webserver going with: ruby script/server
Try it and see what you think. If it doesn‘t work please visit the faq.
Other ways of rendering to juggernaut:
render :juggernaut do |page|
page.alert('hi')
end
render_juggernaut(:action => ‘whatever’)
More usage information, examples and support
<%= juggernaut(:channels => [‘one’, ‘two’, ‘three’]) %> render :juggernaut => {:type => :send_to_channels, :channels => [‘one’]} do |page|
page.alert('hi')
end
Client id usage: <%= juggernaut(:client_id => session[:user_id]) %> render :juggernaut => {:type => :send_to_clients, :client_ids => [1, 2, 3]} do |page|
page.alert('hi')
end
Other juggernaut render options: OPTION_TYPE PARAMS :send_to_all :send_to_channels :channels :send_to_channel :channel :send_to_client :client_id :send_to_clients :client_ids :send_to_client_on_channel :client_id, :channel :send_to_clients_on_channel :client_ids, :channel :send_to_client_on_channels :client_id, :channels :send_to_clients_on_channels :client_ids, :channels
You can also call these methods directly on the Juggernaut class: Juggernaut.send_to_clients(‘data’, [1,2,3])
For authentication options and callbacks see the juggernaut.yml configuration file.
Usage and examples: ncavig.com/blog/ Support and forums: groups.google.com/group/Juggernaut-for-Rails?hl=en
Firstly you will need to configure juggernaut_hosts.yml in your Rails app to point to the proper IP of the push server (rather than <IP_ADDRESS>). For example:
Ok, remote clients that visit pages on this server (once you restart it) will connect to the proper push server IP. BUT, if you‘re using IP based authentication (recommended) you‘ll find that the broadcast authentication fails. You‘ll need to add the Rails IP to juggernaut.yml, like so:
- <IP_ADDRESS> # IP of the Rails app
Troubleshooting
Check out the support forums on google groups: groups.google.com/group/Juggernaut-for-Rails


Wild police chase caught on camera
Suspect makes break when officers try to cuff him
ALBUQUERQUE, N.M. - - A suspect took police on a wild chase that is typically only seen in the movies.
ALBUQUERQUE, N.M. - - A suspect took police on a wild chase that is typically only seen in the movies.


buildMost recent change: , View Change Log editFind an error or missing stuff? Edit on Github！ peoplePage Contributer(s): copyrightCopyright: {{ config.extra.copyright }}. Please use with care.


All the few attributes needed to customize WiseLoop JavaScript Premium Weather Widget are optional as the default values provides a very good balanced set of options to meet most of the usage scenarios.
Using the widget attributes one can tune the graphical appearance (theme and background), language, initial location to retrieve the weather and initial measure units:
- theme: the widget theme.
The themes are HTML files consisting of a layout template and some CSS styling.
All the themes are located under the javascript-premium-weather-widget/bins/themes directory and the .html extension must be skipped.
Default value: "complex1"
- background: the weather widget background.
It can be any CSS valid color such as 'red', '#f00', '#ef23a2', 'transparent' or use 'dynamic' to have a dynamic background according to the current weather conditions.
Default value: "dynamic"
- latitude: default initial location - the latitude.
Default value: "0"
- longitude: default initial location - the longitude.
Default value: "0"
- location: default initial location address.
It can be anything: country, city, zone, area, zipcode, company name, address, street etc.
If latitude or longitude are also set, then this parameter will be just shown like a label as the weather will be retrieved from the (latitude, longitude) pair.
Default value: ""
- units: default initial metric system.
Choose between si (°C, km/h, mBar) or us (°F, mph, InHg).
Default value: "si"
- lang: the widget language.
Forecast.io supports the following languages: bs (bosnian), de (german), en (english), es (spanish), fr (french), it (italian), nl (dutch), pl (polish), pt (portuguese), ru (russian).
Although, forecast.io supports only a limited language set, the widget texts (i.e. weekdays, high/low etc.) can still be localized using external language files located under the javascript-premium-weather-widget/bins/lang directory.
If the specified language is not supported by forecast.io, the weather messages will be returned in english as it is the default language of forecast.io.
A few more languages were already included in the package: hi (hindi), hr (croatian), ro (romanian), tr (turkish) and using them will localize the widget texts but the forecasts will be in english as forecast.io do not support these languages.
More languages can be added add or the existing ones can be modified.
For more information on localization support, please see the Localization Support section.
Default value: "en"
Example (the weather widget with the default attributes):


.
News from the Chapter
Archaeology in Monroe County
Recently an archaeologist called the MCC of OGS president, Karen Romick, about a mine near Powhatan, Monroe County, Ohio, near the Ohio River. He is researching the mine and is trying to find family members of the men killed in the mine explosion in 1926. Over the years this tragic accident has become faded in most of our memories leaving only the newspaper accounts and hurts of family members and friends. Some among us may have wondered about those stone arches which serve as memorials. If you have any information, clippings, pictures of other memorabilia about this event, please contact the MCC of OGS president at the postal, e-mail, or phone number given in the "Contact Us" section of the MCC of OGS home page.
New publications available:
A new cemetery publication planned for this
summer is now available. The cemetery inscriptions of Benton and Washington
Townships is our latest addition cemetery inscriptions. The Benton and Washington Townships Cemetery Book was
published on schedule in September 2001. (See our Publications for Sale for a
list of other cemetery inscription books.)
Wayne Township, Monroe County, Ohio, Cemetery
Inscriptions and Gleanings is now available. This publication lists the
tombstone inscriptions from the cemeteries of Barber Ridge, Christy-Sloan,
Conner Ridge Methodist, New Conner Ridge, Hartshorn Ridge and
Winland-Pryor. Also includes additional information such as obituaries
and notes on selected burials. Indexed, soft cover, 72 pp, $9.00.
Add $2.00 postage and Ohio residents add 59 cents sales tax.
1900 Census Project
Members are working on the 1900 census for a
future publication. But help is still needed! If you would like to volunteer to
copy a township in the 1900 census, please contact us for a list of townships
still available. We ll look forward to hearing from you.
Highlights of the 2002 Navigator
NAVIGATOR HIGHLIGHTS -- If you
haven t yet joined our chapter, you may be missing some outstanding
genealogical materials. Click on the Navigator Highlights to see what you may have missed
in this year s issues of the Navigator. Of course, if you join now, you will
get all of the 2002 back issues of the Navigator and you will not miss all this
good material. Where can you get more for an annual investment of just $10?
Research Room
The Research Room receives many
visitors each year. It is operated and maintained by local volunteers. If you
would like to help maintain the MCC of OGS Research Room, please contact Karen
Romick at:
Monroe County Genealogical Society
P.O. Box 641
Woodsfield, OH 43793-0641
or, Telephone us at:
(740) 483-1481, Karen
Romick
or, e-mail us at:Karen Romick
Wayne Township
Monroe County, Ohio
Cemetery Inscriptions
Wayne Township
Wayne
Township, Monroe County, Ohio, Cemetery Inscriptions and Gleanings. This volume lists
the tombstone inscriptions from the cemeteries of Barber Ridge,
Christy-Sloan, Conner Ridge Methodist, New Conner Ridge, Hartshorn Ridge and
Winland-Pryor. Also includes additional information such as obituaries
and notes on selected burials. Indexed, soft cover, 72 pp, $9.00.
Add $2.00 postage and Ohio residents add 59 cents sales tax.
Benton & Washington Townships
Monroe County, Ohio
Cemetery Inscriptions
Benton & Washington Townships
Our fifth
volume of cemetery inscriptions includes all 26 known cemeteries of these two
townships. We ve also included Carson Cemetery in Grandview Township,
Washington County, Ohio, as it was the burial site of many residents of the
village of Cochransville, Monroe County, Ohio. Cemetery records are extremely
important because they are often the only record of the birth and death of an
individual who lived before 1867.
Indexed, soft cover,
154 pages. Price is $16.00 plus $2.00 postage. OHIO residents add $1.04 tax.
Order your copy from the Monroe Chapter OGS, Box 641, Woodsfield, OH 43793.
Researchers urgently needed
Researchers who can help with lookups, and
conduct research in the Research Room and the Monroe County courthouse are
urgently needed. Volunteers and researchers-for-hire please investigate further
by clicking here: Researchers
Click here for the Short-Cut Table of Contents


Creates a session object in your WSGI environment.
Use like:
..code-block:: Python
environ[‘paste.session.factory’]()
This will return a dictionary. The contents of this dictionary will be saved to disk when the request is completed. The session will be created when you first fetch the session dictionary, and a cookie will be sent in that case. There’s current no way to use sessions without cookies, and there’s no way to delete a session except to clear its data.
@@: This doesn’t do any locking, and may cause problems when a single session is accessed concurrently. Also, it loads and saves the session for each request, with no caching. Also, sessions aren’t expired.
Adds a middleware that handles sessions for your applications. The session is a peristent dictionary. To get this dictionary in your application, use environ['paste.session.factory']() which returns this persistent dictionary.
Configuration:
- session_expiration:
- The time each session lives, in minutes. This controls the cookie expiration. Default 12 hours.
- expiration:
- The time each session lives on disk. Old sessions are culled from disk based on this. Default 48 hours.
- cookie_name:
- The cookie name used to track the session. Use different names to avoid session clashes.
- session_file_path:
- Sessions are put in this location, default /tmp.
- chmod:
- The octal chmod you want to apply to new sessions (e.g., 660 to make the sessions group readable/writable)
Each of these also takes from the global configuration. cookie_name and chmod take from session_cookie_name and session_chmod


Methods are considered empty if they are empty themselves and if they are overridden or
implemented by empty methods only. Note that methods containing only comments and the super()
call with own parameters are
also considered empty.
The inspection ignores methods with special annotations, for example, the javax.ejb.Init
and javax.ejb.Remove
EJB annotations .
The quick-fix safely removes unnecessary methods.
Configure the inspection:


Ph.D., Meteorology and Physical Oceanography
Postdoc Fellow
Naval Research Laboratory
Marine Meteorology Division
About Me
Welcome to my website! Here, you can learn a little about my research - aimed at better
understanding the physical feedbacks between surface fluxes and the overlying atmosphere near land - find my CV,
send me an email, and/or reach me on other platforms across the
web.
Outside of my research, I enjoy spending as much time as I can outdoors, whether it be distance running, hiking, rock climbing,
or hanging out at the park with my dog Mira.
Yes! Hurricanes require deep, warm ocean waters to power the storm. Warm sea surface temperatures (SSTs)
favor storm development, assuming favorable atmospheric conditions. However, a negative feedback occurs
between the wind strength and SSTs. The greater the SSTs, the stronger the wind field may potentially become, which drives
stronger ocean current shear below the surface. These currents aid in mixing cooler waters at depth towards the surface, which
typically result in a cooling of the surface temperatures. Because the SST lacks information about the ocean temperatures below the
surface, ocean heat content (OHC) is often a better estimate of how SSTs will change, because this metric takes into
account the depth of waters containing high heat. Forecasters consider not only the SSTs,
but also how much total heat exists in the upper ocean when predicting whether a storm may strengthen or not.
The amount of cooling under a storm depends on how stable - or resist towards mixing - the upper ocean is.
In some regions of the global tropical oceans, not only is temperature important in determining the density profile and
thus the stability, but also salinity can also affect the structure of the upper ocean.
Previous studies have looked at how upper ocean vertical salinity gradients can essentially dampen
wind-driven ocean mixing and SST cooling, since layers of large increases in salinity with depth,
called "oceanic barrier layers", increase the ocean's stability, or in other words, resistence to vertical fluid movement.
However, there is much disagreement as to whether the changes in SST cooling brought on solely by salinity gradients can
appreciably affect the intensification of a passing hurricane.
Using highly complex numerical model simulations, I've tested how barrier layer thickness modifies the
relationship between a hurricane's wind field and the cooling response of the upper ocean. Through ensemble simulations of idealized
hurricanes, I've shown that vertical barrier layer profiles representative of observations of the Amazon-Orinoco freshwater
plume region in the Western Atlantic can favor additional hurricane intensification, even when vertical wind shear and SSTs are
marginally favorable.
Hurricanes are most dangerous close to and over land, where loss of life and property can occur.
In fact, 8 of the top 10 costlist U.S. natural disasters before 2018 were due to landfalling hurricanes1.
The number of landfalling storms hasn't changed much since 1900, however, the number of people living near the coast
is higher now than at any point in history, and continues to grow. Therefore, damages associated with
landfalling storms has grown exponentially over the past few decades, and this vulnerability will continue
progressing forward. Storm surge and inland flooding are the main causes of loss of life associated with
hurricanes, however intense winds also bring about substantial damages even far inland from the coast.
Despite the threat hurricanes pose towards society,
not much is known about changes in structure before, during, and after landfall. This is because rapid changes in the surface
conditions during landfall occur first at very small scales which are difficult to observe using current methods.
My research has consisted of performing numerical simulations of storms over land masses of differing soil moisture and roughness
characteristics to better understand how these two parameters affect the inland decay of the broader wind field. Additionally, the
coastline forces changes to the storm wind field that drastically differ from observations over the open ocean.
The overall goal of my work is to shed light on the landfall process and how properties
of the underlying surface affect the rate of storm decay, which has implications for understanding inland and coastal
losses.
1Klotzbach, P.J., Bowen, S.G., Pielke Jr, R. and Bell, M., 2018. Continental US hurricane landfall
frequency and associated damage: Observations and future risks. Bulletin of the American Meteorological
Society, 99(7), pp.1359-1376.
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Controller
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Mollitia neque assumenda ipsam nihil, molestias magnam, recusandae quos quis inventore quisquam velit asperiores, vitae? Reprehenderit soluta, eos quod consequuntur itaque. Nam.
Locked Safe
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Mollitia neque assumenda ipsam nihil, molestias magnam, recusandae quos quis inventore quisquam velit asperiores, vitae? Reprehenderit soluta, eos quod consequuntur itaque. Nam.
Submarine
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Mollitia neque assumenda ipsam nihil, molestias magnam, recusandae quos quis inventore quisquam velit asperiores, vitae? Reprehenderit soluta, eos quod consequuntur itaque. Nam.


| Home | Libraries | People | FAQ | More |
There a two primary types of radix-based sorting:
Most-significant-digit Radix sorting (MSD) divides the data recursively based upon the top-most unsorted bits. This approach is efficient for even distributions that divide nicely, and can be done in-place (limited additional memory used). There is substantial constant overhead for each iteration to deal with the splitting structure. The algorithms provided here use MSD Radix Sort for their radix-sorting portion. The main disadvantage of MSD Radix sorting is that when the data is cut up into small pieces, the overhead for additional recursive calls starts to dominate runtime, and this makes worst-case behavior substantially worse than 𝑶(N*log(N)).
By contrast,
,
integer_sort
,
and float_sort
all check to see whether Radix-based or Comparison-based sorting have better
worst-case runtime, and make the appropriate recursive call. Because Comparison-based
sorting algorithms are efficient on small pieces, the tendency of MSD
radix sort
to cut the problem up small is turned into an advantage by these hybrid
sorts. It is hard to conceive of a common usage case where pure MSD radix sort would
have any significant advantage over hybrid algorithms.
string_sort
Least-significant-digit radix sort (LSD) sorts based upon the least-significant bits first. This requires a complete copy of the data being sorted, using substantial additional memory. The main advantage of LSD Radix Sort is that aside from some constant overhead and the memory allocation, it uses a fixed amount of time per element to sort, regardless of distribution or size of the list. This amount of time is proportional to the length of the radix. The other advantage of LSD Radix Sort is that it is a stable sorting algorithm, so elements with the same key will retain their original order.
One disadvantage is that LSD Radix Sort uses the same amount of time to sort "easy" sorting problems as "hard" sorting problems, and this time spent may end up being greater than an efficient 𝑶(N*log(N)) algorithm such as introsort spends sorting "hard" problems on large data sets, depending on the length of the datatype, and relative speed of comparisons, memory allocation, and random accesses.
The other main disadvantage of LSD Radix Sort is its memory overhead. It's only faster for large data sets, but large data sets use significant memory, which LSD Radix Sort needs to duplicate. LSD Radix Sort doesn't make sense for items of variable length, such as strings; it could be implemented by starting at the end of the longest element, but would be extremely inefficient.
All that said, there are places where LSD Radix Sort is the appropriate
and fastest solution, so it would be appropriate to create a templated
LSD Radix Sort similar to
and integer_sort
.
This would be most appropriate in cases where comparisons are extremely
slow.
float_sort


| DOSE: |
3.3 g |
buccal |
Kratom |
(leaves) |
Oooh... feeling rather floaty. Maybe I should explain why - I think this little foray into kitchenology may help some of you internet-pundits out there use your drugs a little more palatably.
 Seeking a reboot, and perhaps a little chemical comfort. Time to break out my pharmacopea and see what can turn my brain to adequate slidginess...
After a little ponderance at my box of tricks I yanked out a good ten grammes of kratom, some cocoa beans and some neem leaves, and decided that I would make myself a culinary frankenstein... here's how it went:
I divided the ten grams (after shredding and de-veining) of kratom into three equal piles (just to eyeball a suitable 'quid' measure). Then mixed them all back together and whacked them into a cup. Added the juice of half a lemon and some brown sugar, threw in the cocoa beans whole. Nuked the concoction in an 800w microwave for about 45 seconds (after which the wonder sludge smelled like a lemon crisp bar, if those actually exist, after being dumped into a bag of peat). I spooned this into three partially dried large sweet neem leaves, added some cinnamon and other little goodies. Also some cocoa beans decided to jump in there. Wrapped up the leaves and loaded one into my lower facial orifice.
Sucked
Chewed
Sucked some more.
I Tried not to swallow what saliva resulted, as I was keen to see whether mitragynine would permeate through the mouth's membranes.

Sucked a little more, chewed and finally swallowed.
This was perhaps twenty minutes from zero, so perhaps this method (I surmised this from other reports) is not as quickly uptaking as making tea, as reports suggest as little as five minutes for effects to begin.
I began to feel mellow. I mean triple-distilled 99.99% BP ubermellowness, and slitheredd my way to my laptop to watch some neon genesis evangelion. Chewed the last quid at about t+1 hour, by which time movement seemed an alien and morally repugnant concept. Goddamned dynamic universe.
However, there was a marked difference in effect - I've tried it in every form other than suppositories and this time I completely missed the stimulating effect (which normally exhibits itself for around ninety minutes) -this instead was an instant ticket to mellowness.
However, the effects seem combined now - that whole tweeky / sedatey dichotomy seem to be superimposed upon one another, occasionally alternating in 'waves' - As I write this verbatim, my hands are fluent upon the keyboard, and yet I don't even remember the home position. Marked analgesia, apparent bronchial dilation. Visual flanging (only very marginal) and the faeries seem intent on pulling me deeper. Fareies are good - my last session with this unique plant resulted in the untimely binning of good trousers, as I fell asleep in my clothes and decided to lay some free range batchelor seed.
Aside from wasted sperm, I've had nothing but pleasant experiences with kratom - however, I once used it on five sequential evenings and ended up with a mild, but unabating and annoying face-ache as a reward for my plant loyalty.
I won't conclude that the effects are different to other ingesion methods until I've 'bioassayed' a few more quids - as most druggies know, there are too many variables in the soup to allow for that. Next time, however, I plan to make an extract of the leaves first.
(To whoever read this - Apologies for the convoluted style and general pretentious language - some drugs just do that to me).
So, the tripe's in the cooking pot and the sun's rising on the cabbage farm - and it's left me feeling pretty smashed.
Peace, love, and wandering thoughts
The emissary of the slime duchy.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Privacy policy
Ticket Tailor ("we") are committed to protecting and respecting your privacy and keeping your personal information secure.
This policy (together with our Website Terms of Use https://www.tickettailor.com/website-terms-of-use, our Terms and Conditions of Service https://www.tickettailor.com/terms-and-conditions/ and any other documents referred to on it) sets out:
- details of the personal information that we may collect from you;
- information about how we use your personal information;
- information about how we store your information; and
- information about your rights.
Please read this policy carefully to understand our views and practices regarding your personal data and how we will treat it.
For the purpose of the Data Protection Act 1998 (the Act), the data controller is Zimma Ltd of Kemp House 160 City Road, London EC1V 2NX (registered at Companies house with company number 07583551 and trading as Ticket Tailor).
Information we may collect from you
We may collect and process the following data about you:
-
Information that you provide to us. You may give us information about you by filling in forms on tickettailor.com (or sub-domains of tickettailor.com) or our apps or by corresponding with us by phone, email or otherwise. This includes information you provide when you register to use our website or our app, subscribe to our service, respond to any surveys that we send to you to complete, post materials, requesting further services and when you report a problem with our website. This information may include:
- your name;
- your address;
- your email address and phone number;
- details of any opinions or complaints you raise regarding the service; and
- details of transactions you carry out through our site and of the fulfilment of your orders.
If you contact us, we may keep a record of that correspondence.
We may also ask you to complete surveys that we use for research purposes, although you do not have to respond to them.
-
Other information we collect about your visit to our website or use of our apps. With regard to each of your visits to our website, we may automatically collect details of your visit, including, but not limited to, traffic data, location data, weblogs and other communication data. We may also collect information about your computer, including where available your IP address, operating system and browser type, for system administration and to report aggregate information to our advertisers. We may also collect details of the resources that you access on our website. This information is used for admin purposes.
When using our paid service, you will be asked for financial details such credit / debit card information. The processing of these payments are carried out by third parties and on a separate secure server. We do not store any credit or debit card information on our servers.
How we use your personal information
We use information held about you for the following purposes:
- to provide you with information or services that you request from us;
- to carry out our obligations arising from any contracts entered into between you and us;
- to send you newsletters about our service;
- to provide you with information about services that are similar to those that you have already purchased or enquired about;
- to allow you to participate in interactive features of our service, when you choose to do so;
- to notify you about changes to our service; and
- to ensure that content from our website is presented in the most effective manner;
- to administer our site and for internal operations, including troubleshooting, data analysis, research and statistical purposes;
- as part of our efforts to keep our website safe and secure and to monitor actual or suspected fraudulent activity;
- to carry out re-targeting advertising;
- to measure the effectiveness of our service.
Cookies
Our website uses cookies to distinguish you from other users of our website. This helps us provide you with a good experience when you browse our website and allows us to improve our site and to deliver a better and more personalised service.
Please read our Cookie policy https://www.tickettailor.com/cookie-policy/ to find out about the cookies we use and what they are sued for.
Where we store your personal data
The data that we collect from you may be transferred to, and stored at, a destination outside the European Economic Area ("EEA"). It may also be processed by staff operating outside the EEA who work for us or for one of our suppliers. Such staff maybe engaged in, among other things, the fulfilment of your order, the processing of your payment details and the provision of support services. By submitting your personal data, you agree to this transfer, storing or processing. We will take all steps reasonably necessary to ensure that your data is treated securely and in accordance with this privacy policy.
All information you provide to us is stored on our secure servers. Any payment transactions will be carried out by third parties over encrypted connections using SSL technology. Where we have given you (or where you have chosen) a password or API key which enables you to access certain parts of our site or apps, or you have invited team members to access parts of our site or apps, you are responsible for keeping this password or API key confidential. We ask you not to share a password or API key with anyone.
Unfortunately, the transmission of information via the internet is not completely secure. Although we will do our best to protect your personal data, we cannot guarantee the security of your data transmitted to our site and any transmission is at your own risk. Once we have received your information, we will use strict procedures and security features to try to prevent unauthorised access.
Disclosure of your information
We may disclose your personal information to any member of our group from time to time.
We may disclose your personal information to selected third parties, including:
- in the event that we sell or buy any business or assets, the prospective seller or buyer of such business or assets;
- if Ticket Tailor or substantially all of its assets are acquired by a third party, to the relevant third party;
- business parties and subcontractors for the purposes of providing the Ticket Tailor services;
- analytics providers that assist us in the improvement and optimisation of our website;
- law enforcement agencies or other third parties for the purposes described below.
We may also disclose your personal information to third parties if we are under a duty to disclose or share your personal data in order to comply with any legal obligation, or in order to enforce or apply our Website Terms of Use https://www.tickettailor.com/website-terms-of-use, our Terms and Conditions https://www.tickettailor.com/terms-and-conditions and other agreements, or to protect the rights, property, or safety of Ticket Tailor, our customers, or others. This includes exchanging information with other companies and organisations for the purposes of fraud detection and protection and credit risk reduction.
Use of your customers' data
As explained further in section 11 of the Terms and Conditions, we and you agree that you are the data controller and we are the data processor in relation to the personal data of your customers and event attendees that we process on your behalf as part of the Service ("Customer Data").
Your rights
You have the right to ask us not to process your personal data for marketing purposes. We will usually inform you (before collecting your data) if we intend to use your data for such purposes or if we intend to disclose your information to any third party for such purposes. You can exercise your right to prevent such processing by unsubscribing from our newsletter and other communications. You can also exercise the right at any time by contacting us at<EMAIL_ADDRESS>The Data Protection Act 1998 gives you the right to access information held about you. Your right of access can be exercised in accordance with the Act. Any access request may be subject to a fee of £10 to meet our costs in providing you with details of the information we hold about you.
Links to other websites
Our site may, from time to time, contain links to and from the websites of our partner networks, advertisers and affiliates. If you follow a link to any of these websites, please note that these websites have their own privacy policies and that we do not accept any responsibility or liability for these policies. Please check these policies before you submit any personal data to these websites.
Changes to our privacy policy
Any changes we may make to our privacy policy in the future will be posted on this page. We recommend that you review this page from time to time for updates.
Contact
Questions, comments and requests regarding this privacy policy are welcomed and should be addressed to hi@tickettailor.com.


Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
| Version: | 0.5-10 |
| Depends: | R (≥ 2.1.0) |
| Published: | 2017-10-26 |
| Author: | Tomas J. Aragon [aut], Michael P. Fay [ctb], Daniel Wollschlaeger [ctb], Adam Omidpanah [cre, ctb] |
| Maintainer: | Adam Omidpanah <adam.omidpanah at wsu.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | epitools results |
| Reference manual: | epitools.pdf |
| Package source: | epitools_0.5-10.tar.gz |
| Windows binaries: | r-devel: epitools_0.5-10.zip, r-release: epitools_0.5-10.zip, r-oldrel: epitools_0.5-10.zip |
| OS X binaries: | r-release: epitools_0.5-10.tgz, r-oldrel: epitools_0.5-10.tgz |
| Old sources: | epitools archive |
| Reverse depends: | pCalibrate, PredictABEL |
| Reverse imports: | compareGroups, FSA, morse, NSM3, pubh |
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