When we lose people who seemingly have everything, it can be very scary. I know first-hand that appearances can be deceiving. Right before my suicide attempt, I looked like I had it all. A job in my field, a good relationship, and the first undergraduate published in The New York Times. I also thought about killing myself constantly. Why didn’t I reach out to anyone? Well, the truth is that I had. I quietly tried to share that I was struggling, but instead of help, I was told that I was selfish, being dramatic, and needed to pray. None of these messages was helpful. I felt like a burden and learned to hide my pain and pretend. After a sexual assault, the years of stuffing down my feelings eventually erupted into a suicide attempt. Healing looks different for everyone, but for me it was connecting with the feelings I had avoided, developing boundaries, trauma-informed therapy, mental health advocacy, and removing toxic people from my life. I’ve built a safety net of family and friends to catch me when I fall. When someone is physically sick, we know exactly what to do. We bring casseroles, send flowers and cards, and think about what someone who is in pain needs. I don’t think it should be any different for folks who are feeling suicidal. Personally, I need connection and to interrupt the isolation and negative thoughts in my head. So, please be good to each other. Check on your strong friends who have it together, and your friends who are “too busy” and disappeared. If we can create a world where the stigma of suicide is decreased so people speak out when they are in pain, maybe we can prevent anyone else from dying.
I lost a friend to suicide for the first time when I was 15. I was already having suicidal thoughts, but losing Bryan took my abstract suffering and made suicide suddenly very real to me. Over the next few years, I continued to struggle with those thoughts, and with self-injury, both anchored down by a deep depression. I questioned my sexuality and I was outed against my will. I went away to college and failed my first semester. That was the first time anyone suggested that I might need to see a doctor. Prior to that, what I was feeling was labeled “teenage angst,” and I was told to get over it. This began a years-long journey with therapists, psychiatrists, and medications —- none of which worked -- and I had a hard time staying the course. When I was 23, at the end of an emotionally and physically abusive relationship, after two years of being deeply suicidal, I tried to take my life. I couldn’t sleep. I couldn’t eat. I was codependent and couldn’t tear myself away from this relationship that was so terrible for me. I was convinced I was crazy, and a burden, and that everyone hated me. I couldn’t feel anything but hurt, like I was burning from the inside out. I couldn’t see a future for myself, even though I’d just been accepted to a Ph.D. program.Ending it seemed like the only way to make the pain stop.This month will be 12 years since my suicide attempt. I created a career I love out of thin air. I have an incredible wife and a new son. We own a house on a block we love. I have a wonderful therapist and feel more mentally healthy than I ever have. And I still struggle with suicidal thoughts. The difference now is that I have better coping skills, I have an amazing support system, and I know how to ask for help when I need it.
I was sixteen when I attempted suicide.I’d fallen into a deep depression after moving across the country with my family, and there was a voice in my head telling me I was worthless. It said no one loved me. That I was weak. Sick. Broken. Wrong. It said I didn’t belong. That I had nothing to give. It told me I was a burden on the people I loved. That my sick, weak, broken self was infecting everyone else. Ruining their lives. It said nothing would ever get better. And it was so loud I couldn’t hear anything else. I’d love to tell you that a hospital stay, some medication, and a couple therapy sessions silenced the voice, but that wouldn’t be true. I did all those things, of course, and more. And slowly, over many years, things did get better. I got better. I danced with my friends. I sang karaoke. I swam in two oceans. I learned Spanish (and forgot it). I learned the guitar (and forgot that, too.) I earned a master’s degree. I got married. I had children. I built a life that I love. One I want to keep living. And yet, over 20 years later, even after the death of my only sibling by suicide, even after the births of my two beautiful kids, I still hear that voice sometimes. It’s not nearly as loud or as frequent, but it’s still there. I think it always will be. I don’t try to silence it, anymore. I know that what it says isn’t true. I try to sit with it. I try to find the feeling underneath the words. Is it loneliness? Exhaustion? Fear? I try to hold it, gently, like a bird with broken wings. I listen, and then I let it go.


| DOSE: |
15 mg |
smoked |
MET |
(powder / crystals) |
| |
|
oral |
MET |
(powder / crystals) |
This was my first time (besides an allergy test and tiny trial dose) using the substance MET, Methylethyltryptamine. Based on my experience I think this has definite therapeutic potential. Recreationally its not bad either.
I vaped a smaller dosage than I've read is normally recommended and because of that I didn't have a lot of visual hallucinations. But vaping a rather modest dose, around 15-20 mg produced significant effects.
The MET was in powder form. I put it in a oil burning pipe (meth pipe or crack if you're old school) and started to heat it, rolling the pipe a bit.  Then I put a bit more in the pipe and tried again. This time I successfully got 2 big hits (like a hit and half of weed, but I take big hits) off the pipe.
0:00-0:05 - The come up was about 2-3 times as slow as the come up on DMT. It was strangely reminiscent of something, which I eventually realized was the come-down from DMT. This 'I'm not in the crazy fractalizing weirdness any more but I still feel pretty damn odd' feeling came first, but was almost immediately accompanied by a deeply satisfying feeling of body warmth and comfort.  That's ok. You’re good and its going to be swell. And that was the flavor of the whole experience. MET at least for me, at this fairly low dose, was just downright comforting.
0:05-0.10 - I had been worrying about a number of things - interpersonal issues, lifestyle issues - and MET was extremely therapeutic material for me to look at my problems and basically tell myself that they were not really a big deal, and this was why. I felt like serious, serious therapeutic insights were just there for the taking, and I took a few. I just hope I can remember them. There was a huge sense of self-love and self-acceptance with this kind of speedy psychedelic edge going on. Wow. Interestingly, this was almost all closed-eyed and involved very little visualization. But tactile sense was unbelievable. I could feel little pulses of energy going through my body, each bringing pleasure. MET has absolutely definite erotic potential.
0:10-0:20 I needed cannabis by this point. The slight acidy sense of manic was a bit much, and also I wondered if cannabis would potentiate the MET and induce some CEVs. It didn't, but it felt great.
0:20-0:30 Coming down, but still with a great feeling of quiet energy and body full of bliss. Able to search out the Erowid submissions page to start work on this.
0:55 - finishing up writing this and still feeling some mild effects. I really think if used at low dosages this could be a hugely therapeutic medicine. It has an intensity which brings up emotions with a sense of acceptance and calm at the same time. The duration is short enough that one can have a meaningful experience and hopefully reintegrate some of it, but not so short it's like being launched into outer space. I think this also could possibly be useful therapeutically for some people experiencing issues related to sexuality.
0:60 - urge to redose. Not sure what sort of tolerance there is.
1:05 - redosed with one more large vaporized hit. Hmmm. Some tolerance but not so much.
1:40 - again, therapeutic, and recreational, and creative as I'm now working on some music and no urge to redose again.
Excellent low-dose working material. I'm going to up it a little next time and see if I can take it more into the visual realm.
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.


Use "getElementsByTagName('*')" to create an array of all html elements of the page.
Then create the function "getElementByClass('myclass')" and pass the class as the
parameter when calling the function.
Loop through the array and use HTML DOM javascript commands to change attributes for a given class.
Move the cursor over the first image or click on any of the remaining images to enlarge.
This will call the function "getElementByClass('myimg')" and resize all images of class "myimg"
to 1/3 of the window width.
Press F5 or reload the page and restore the original size.
For some strange reason Google Chrome considers the initial image to have a zero width, other browsers show it correctly as 512 px
replace the value of one element with the value of another one
Field1:a navigation menu line:
Click on these parameters to refresh values after resizing the window
This page will not validate in XHTML 1.1 due to the ruby tag, which is only supported as of HTML 5
This is a paragraph within the section
q tag example Link
to my personal page.
An input box with a datalist. This HTML5 feature is implemented differently in the various browsers.
The datalist element is not supported in Internet Explorer v9, Google v15.0.874.106 and Safari v5.0.5.
list all attributes of element Button_2 on the fun stuff tab
List of all script tags on this page
parse the page for all script elements and display their innerHTML value
this behavese differently in Opera, where it breaks at the i<x.length in the script below, while in other browsers it breaks after (document.getElementById('MyBody').bgColor).
Select tag with 2 optgroups and 2 option tags each
Code text
Code superscript and subscript
? This is a paragraph within div 1. It should be dark green. It actually is.
this should be dark green again
This works fine. End of div2paragraph will as well be dark green
End of div1no longer be dark green
| HOME |


The file provides a function that returns VIS related information such as pixel size, dark current, gain, zeropoint, and sky background.
| requires: | NumPy |
|---|---|
| requires: | numexpr |
| author: | Sami-Matias Niemi |
| contact: |<EMAIL_ADDRESS>|
| version: | 0.7 |
This function provides a non-linearity model for a VIS CCD273.
The non-linearity is modelled based on the results presented in MSSL/Euclid/TR/12001 issue 2. Especially Fig. 5.6, 5.7, 5.9 and 5.10 were used as an input data. The shape of the non-linearity is assumed to follow a parabola (although this parabola has a break, see the note below). The MSSL report indicates that the residual non-linearity is on the level of +/-25 DN or about +/- 0.04 per cent over the measured range. This function tries to duplicate this effect.
Note
There is a break in the model around 22000e. This is because the non-linearity measurements performed thus far are not extremely reliable below 10ke (< 0.5s exposure). However, the assumption is that at low counts the number of excess electrons appearing due to non-linearity should not be more than a few.
| Parameters: | data (ndarray) – data to which the non-linearity model is being applied to |
|---|---|
| Returns: | input data after conversion with the non-linearity model |
| Return type: | float or ndarray |
This function provides a theoretical non-linearity model based on sinusoidal error with a given amplitude, phase and number of waves (multi).
| Parameters: |
|
|---|---|
| Returns: | input data after conversion with the non-linearity model |
| Return type: | ndarray |
Returns a dictionary describing VIS. The following information is provided (id: value - reference):
apCorrection: 0.925969 - derived using VIS system PSF (see EUCL-MSS-RP-6-001)
aperture_size:<PHONE_NUMBER>1416876 - derived (radiometric_model_reference_phase4_JA110415_2_MSSL_version)
beta: 0.6 - CDM03 (Short et al. 2010)
bias: 500.0 - ROE Requirements Specification (EUCL-MSS-RD-6-009)
cosmic_bkgd: 0.172 - derived (radiometric_model_reference_phase4_JA110415_2_MSSL_version)
dark: 0.001 - CCD spec EUCL-EST-RS-6-002
diameter: 1.3 - radiometric_model_reference_phase4_JA110415_2_MSSL_version
dob: 0 - CDM03 (Short et al. 2010)
e_adu: 3.1 - ROE Requirements Specification (EUCL-MSS-RD-6-009)
fullwellcapacity: 200000 - CCD spec (for simulator)
fwc: 200000 - CCD spec EUCL-EST-RS-6-002 (for CDM03)
gain: 3.1 - ROE Requirements Specification (EUCL-MSS-RD-6-009)
galaxy_fraction: 0.836 - radiometric_model_reference_phase4_JA110415_2_MSSL_version
magzero:<PHONE_NUMBER>5.3279 - derived, see belowCDM (VIS ETC)
ovrscanx: 20 - ROE Requirements Specification (EUCL-MSS-RD-6-009) (req: CalCD-B)
peak_fraction: 0.261179 - derived
pixel_size: 0.1 - CCD spec EUCL-EST-RS-6-002
prescanx: 50 - CCD spec EUCL-EST-RS-6-002 (also in CalCD-B)
rdose:<PHONE_NUMBER>0.0 - derived (above the PLM requirement)
readnoise: 4.5 - WL requirement (PERD R-VIS-P-021)
readout: 4.5 - WL requirement (PERD R-VIS-P-021)
readtime: 88.0 - derived; ROE Requirements Specification (EUCL-MSS-RD-6-009)
sfwc: 730000.0 - CDM03 (Short et al. 2010), see also the CCD spec EUCL-EST-RS-6-002
sky_background: 22.3203 - radiometric_model_reference_phase4_JA110415_2_MSSL_version
sky_high: 21.7206 - radiometric_model_reference_phase4_JA110415_2_MSSL_version
sky_low: 22.9207 - radiometric_model_reference_phase4_JA110415_2_MSSL_version
st: 5e-06 - CDM03 (Short et al. 2010)
star_fraction: 0.928243 - derived using VIS system PSF (see EUCL-MSS-RP-6-001)
svg: 1e-10 - CDM03 (Short et al. 2010)
t: 0.01024 - CDM03 (Short et al. 2010)
trapfile: cdm_euclid.dat - CDM03 (derived, refitted to CCD204 data)
vg: 6e-11 - CDM03 (Short et al. 2010)
vth: 11680000.0 - CDM03 (Short et al. 2010)
xsize: 2048 - CCD spec EUCL-EST-RS-6-002
ysize: 2066 - CCD spec EUCL-EST-RS-6-002
zeropoint: 25.50087633632 - VIS ETC
zeropointNoObscuration: 25.57991044453 - radiometric_model_reference_phase4_JA110415_2_MSSL_version
zodiacal: 22.3203 - VIS ETC
The magzero was calculated as follows:
1./10**(-0.4*(25.45338546114)) =<PHONE_NUMBER>1.225231
The throughput input values are derived from two Excel Spreadsheets namely:
- 110413_EUC_TN_00051_SYS_PERF_REF_iss4.xlsx
- radiometric_model_reference_phase4_JA110415_2_MSSL_version
| Returns: | instrument model parameters |
|---|---|
| Return type: | dict |
A simple test to plot the current non-linearity model.
These functions can be used to address the CCD requirements, which are written for an MTF while PERD requirements are for a PSF.
Note
The frequency nu_0 is the Nyquist limit for the CCD, which is defined as: nu_0 = 1 / (2p) , where p is the pixel pitch in mm. Hence, for VIS the nu_0 is about 41.666.
Some links: http://www.dspguide.com/CH25.PDF http://home.fnal.gov/~neilsen/notebook/astroPSF/astroPSF.html#sec-5 http://mathworld.wolfram.com/FourierTransformGaussian.html https://github.com/GalSim-developers/GalSim/wiki/Optics-Module-usage http://www.e2v.com/e2v/assets/File/documents/imaging-space-and-scientific-sensors/Papers/ccdtn105.pdf http://aberrator.astronomy.net/html/mtf.html
Calculates FWHM from sigma assuming a Gaussian profile.
| Parameters: | sigma – standard deviation |
|---|---|
| Returns: | FWHM |
Animation showing how MTF changes as the size of the Gaussian PSF grows.
| Parameters: |
|
|---|---|
| Returns: | None |
Derives an MTF from pupil image.
| Parameters: | wf – |
|---|---|
| Returns: | MTF |
Derives a PSF from pupil image.
| Parameters: |
|
|---|---|
| Returns: |
Create a circular symmetric Gaussian centered on x, y.
| Parameters: | sigma (float) – standard deviation of the Gaussian, note that sigma_x = sigma_y = sigma |
|---|---|
| Returns: | circular Gaussian 2D |
| Return type: | ndarray |
Compares an analytical derivation of FWHM - MTF relation to numerical solution. This is only valid for Gaussian PSFs.
| Parameters: |
|
|---|---|
| Returns: | None |
Return the tuple kx, ky corresponding to the DFT of a unit integer-sampled array of input shape.
Uses the SBProfile conventions for Fourier space, so k varies in approximate range (-pi, pi]. Uses the most common DFT element ordering conventions (and those of FFTW), so that (0, 0) array element corresponds to (kx, ky) = (0, 0).
See also the docstring for np.fftfreq, which uses the same DFT convention, and is called here, but misses a factor of pi.
Adopts Numpy array index ordering so that the trailing axis corresponds to kx, rather than the leading axis as would be expected in IDL/Fortran. See docstring for numpy.meshgrid which also uses this convention.
@param array_shape the Numpy array shape desired for kx, ky.
Generates a pupil image.
| Parameters: |
|
|---|---|
| Returns: |
Plots the requirements, both for PSF and MTF and compares them.
| Parameters: |
|
|---|---|
| Returns: | None |
Perform a 2D roll (circular shift) on a supplied 2D numpy array, conveniently.
@param image the numpy array to be circular shifted. @param (iroll, jroll) the roll in the i and j dimensions, respectively.
@returns the rolled image.


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Simulation is important topic for statistical applications and scientific purposes in general.
The first step in simulation is to generate random values based on your variables distribution. R has a large number of functions that generate the standard random variables.
for Normal random variable generation
rnorm() simulate a simple random normal variable with a given mean and standard deviation and generate as many random values as requested.
When generating any random variable setting the random number seed with set.seed ensures reproducibility. In the following example, notice that the first and the third sets are identical (both called set.seed with 1 before generating the numbers), and the second is different (because we didn’t set the seed)
for Poisson random variable generation
rpois() simulate a Poisson random variable with a given rate (lambda) and generate as many random values as requested.
The sample function draws randomly from a specified set of (scalar) objects allowing you to sample from arbitrary distributions (sample space).
if you didn’t specified the number of values you want, sample will give you random permutation of the sequence.
as you noticed, by default sample will not repeat values, set parameter replace= TRUE to make it repeat .
Probability distributions usually have four functions associated with them. The functions are prefixed with:
some useful distributions are:
function names are : prefix + distribution nickname. rnorm, rpois, rbinom,….. –> for random number generation, and so on.
In this note we introduced the basic functions for random number generation based on variable distribution type, arbitrary random sampling, and the associated probability distributions functions. Later will present each of them in detail.
Stay tuned for more R notes.


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6 Hypothesis Testing
6.1 Some Theory
Statistics cannot prove anything with certainty. Instead, the power of statistical inference derives from observing some pattern or outcome and then using probability to determine the most likely explanation for that outcome (Wheelan 2013).
In its most abstract form, hypothesis testing has a very simple logic: the researcher has some theory about the world and wants to determine whether or not the data actually support that theory. In hypothesis testing, we want to:
- explore whether parameters in a model take specified values or fall in certain ranges
- detect significant differences, or differences that did not occur by random chance
To address these goals, we will use data from a sample to help us decide between two competing hypotheses about a population. These two completing hypotheses are:
- a null hypothesis (\(H_0\)) that corresponds to the exact opposite of what we want to prove
- an alternative hypothesis (\(H_1\)) that represents what we actually believe.
The claim for which we seek significant evidence is assigned to the alternative hypothesis. The alternative is usually what the researcher wants to establish or find evidence for. Usually, the null hypothesis is a claim that there really is “no effect” or “no difference.” In many cases, the null hypothesis represents the status quo or that nothing interesting is happening.
For example, we can think of hypothesis testing in the same context as a criminal trial. A criminal trial is a situation in which a choice between two contradictory claims must be made.
- The accuser of the crime must be judged either guilty or not guilty.
- Under the rules of law, the individual on trial is initially presumed not guilty.
- Only strong evidence to the contrary causes the not guilty claim to be rejected in favor of a guilty verdict.
The phrase “beyond a reasonable doubt” is often used to set the cut-off value for when enough evidence has been given to convict. Theoretically, we should never say “The person is innocent” but instead “There is not sufficient evidence to show that the person is guilty.” That is, technically it is not correct to say that we accept the null hypothesis. Accepting the null hypothesis is the same as saying that a person is innocent. We cannot show that a person is innocent; we can only say that there was not enough substantial evidence to find the person guilty.
6.2 Types of Inferential Statistics
We assess the strength of evidence by assuming the null hypothesis is true and determining how unlikely it would be to see sample statistics as extreme (or more extreme) as those in the original sample. Using inferential statistics allows us to make predictions or inferences about a population from observations based on a sample. We can calculate several inferential statistics:
- Whether a sample mean is equal to a particular value:
- One sample t-test (\(H_0: \mu=\text{value}\))
- Whether two sample means are equal:
- Independent samples t-test (\(H_0: \mu_1=\mu_2\) or \(H_0: \mu_1-\mu_2=0\))
- Repeated measures t-test (\(H_0: \mu_D=0\) or \(H_0: \mu_1-\mu_2=0\))
- Whether three or more groups have equal means:
- ANOVA (\(H_0: \mu_1=\mu_2=\mu_3=\dots=\mu_k\))
6.3 One-Sample \(t\) Test
Suppose we wish to test for the population mean (\(\mu\)) using a dataset of size (\(n\)), and the population standard deviation (\(\sigma\)) is not known. We want to test the null hypothesis \(H_0:\mu = \mu_0\) against some alternative hypothesis, with (\(\alpha\)) level of significance.
The test statistic will be:
\[t = \frac{\bar{x} - \mu_0}{\frac{S}{\sqrt{n}}}\]
with \(df = n - 1\) degrees of freedom. In the formula:
- \(\bar{x}\) is the sample mean
- \(\mu_0\) is the population mean that we are comparing against our sample mean
- \(S\) is the sample standard deviation
- \(n\) is the sample size
If \(t > t_{critical}\) at the \(\alpha\) level of significance, we reject the null hypothesis; otherwise we retain the null hypothesis.
6.3.1 Example
Let’s see one-sample t-test in action. All the patients in the medical
dataset received a mental test at the baseline (when they were accepted to the study). The researcher who created the mental test reported that the mean score on this test for people with no mental issues should be around 35. Now we want to know whether the mean mental test for our sample of patients differs from the mean mental test score for the general population. Our hypotheses are:
- \(H_0: \mu = 35\)
- \(H_1: \mu \neq 35\)
# Let's see the mean for mental1 in the data
mean(medical$mental1)
[1] 31.68
It looks like our sample mean is less than the population mean of 35. But, the question is whether it is small enough to conclude that there is a statistically significant difference between the sample mean (i.e., 31.68) and the population mean (i.e., 35).
t.test(medical$mental1, mu = 35, conf.level = 0.95, alternative = "two.sided")
One Sample t-test
data: medical$mental1
t = -4.2, df = 245, p-value = 4e-05
alternative hypothesis: true mean is not equal to 35
95 percent confidence interval:
30.11 33.25
sample estimates:
mean of x
31.68
The lsr
package (Navarro 2015) does the same analysis and it provides more organized output:
# Install the activate the package
install.packages("lsr")
library("lsr")
# Run one-sample t test
oneSampleTTest(x=medical$mental1, mu=35, conf.level=0.95, one.sided=FALSE)
One sample t-test
Data variable: medical$mental1
Descriptive statistics:
mental1
mean 31.680
std dev. 12.486
Hypotheses:
null: population mean equals 35
alternative: population mean not equal to 35
Test results:
t-statistic: -4.17
degrees of freedom: 245
p-value: <.001
Other information:
two-sided 95% confidence interval: [30.112, 33.248]
estimated effect size (Cohen's d): 0.266
Conclusion: With the significance level of \(\alpha=.05\), we reject the null hypothesis that the average mental score in the medical
dataset is the same as the average mental score in the population, \(t(240)=-4.2\), \(p < .001\), \(CI_{95}=[30.11, 33.25]\).
6.4 Independent-Samples \(t\) Test
Suppose we have data from two independent populations, \(x_1 \sim N(\mu_{x_1}, \sigma_{x_1})\) and \(x_2 \sim N(\mu_{x_2}, \sigma_{x_2})\). We wish to determine whether the two population means, \(\mu_{x_1}\) and \(\mu_{x_2}\), are the same or different. We want to test the null hypothesis \(H_0: \mu_{X_1} = \mu_{X_2}\) against some alternative hypothesis, with \(\alpha\) level of significance. The test statistic will be:
\[t =\dfrac{ (\bar{x}_1 - \bar{x}_2)}{ \sqrt{\dfrac{{S_p}^2}{n_1} + \dfrac{{S_p}^2}{n_2}} }\]
and
\[S_p = \frac{(n_1-1)S_1^2 + (n_2-1)S_2^2}{n_1+n_2-2}\]
with \(df = n_1 + n_2 - 2\) degrees of freedom. In the formula:
- \(\bar{x}_1\) is the sample mean response of the first group
- \(\bar{x}_2\) is the sample mean response of the second group
- \(S_1^2\) is the sample variance of the response of the first group
- \(S_2^2\) is the sample variance of the response of the second group
- \(S_p\) is the pooled variance
- \(n_1\) is the sample size of the first group
- \(n_2\) is the sample size of the second group
If \(t > t_{critical}\) at the \(\alpha\) level of significance, we reject the null hypothesis; otherwise we retain the null hypothesis.
6.4.1 Example
Using the medical
dataset, we want to know whether there was a significant difference between male and female patients’ depression levels at the baseline. We will use sex
and depression1
to investigate this question. Before we test this question, let’s see the boxplot for these two groups:
boxplot(formula = depression1 ~ sex,
data = medical,
main = "Depression Scores by Sex",
ylab = "Depression at the baseline",
names = c("Female", "Male"))
It seems that female patients have higher levels of depression on average, compared to male patients. Next, we will create two new small datasets (male
and female
) that consist of only depression scores for each gender group.
<- subset(medical, sex == "male", select = "depression1")
male <- subset(medical, sex == "female", select = "depression1")
female t.test(male, female, conf.level = 0.95, alternative = "two.sided")
Welch Two Sample t-test
data: male and female
t = -2.5, df = 87, p-value = 0.02
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-8.418 -0.928
sample estimates:
mean of x mean of y
31.50 36.18
We can again use the lsr
package to get a better output. independentSamplesTTest
function does not require us to separate the dataset for each group. We only need to specify the group variable, which is sex
in our example. The only thing we need to make sure that the group variable is a factor.
$sex <- as.factor(medical$sex)
medical
independentSamplesTTest(formula = depression1 ~ sex,
conf.level = 0.95,
one.sided = FALSE,
data = medical)
Welch's independent samples t-test
Outcome variable: depression1
Grouping variable: sex
Descriptive statistics:
female male
mean 36.175 31.503
std dev. 12.675 11.758
Hypotheses:
null: population means equal for both groups
alternative: different population means in each group
Test results:
t-statistic: 2.48
degrees of freedom: 87.09
p-value: 0.015
Other information:
two-sided 95% confidence interval: [0.928, 8.418]
estimated effect size (Cohen's d): 0.382
Conclusion: With the significance level of \(\alpha=.05\), we reject the null hypothesis that the average depression score for male and female patients is the same in the population, \(t(87)=-2.5\), \(p < .05\), \(CI_{95}=[-8.42, -0.93]\).
6.5 \(t\)-test with Paired Data
Suppose we have paired data, \(X_1\) and \(X_2\) with \(D=X_1-X_2 \sim N(\mu_D, \sigma)\). We wish to determine whether the two population means (or a single population across two time points), \(\mu_{X_1}\) and \(\mu_{X_2}\), are the same (i.e., \(\mu_D = 0\)) or different (i.e., \(\mu_D \neq 0\)). We test the null hypothesis \(H_0: \mu_X = \mu_Y\) against some alternative hypothesis, with \(\alpha\) level of significance. The test statistic will be:
\[t = \frac{\bar{D}}{\frac{S_D}{\sqrt{n}}}\]
with \(df = n -1\) degrees of freedom. In the formula:
- \(D\) is the difference between two populations (or two time points)
- \(S_D\) is the sample standard deviation of the difference
- \(n\) is the sample size of the second group
If \(t > t_{critical}\) at the \(\alpha\) level of significance, we reject the null hypothesis; otherwise we retain the null hypothesis.
6.5.1 Example
Using the medical
dataset, this time we want to know whether patients’ depression scores at the baseline (depression1
) are the same as their depression scores after 6 months (depression2
). First, let’s see the means for the two variables.
mean(medical$depression1)
[1] 32.59
mean(medical$depression2)
[1] 22.72
It seems that the scores at month 6 are much lower. Let’s see if the difference is statistically significant.
t.test(medical$depression1, medical$depression2,
paired = TRUE, alternative = "two.sided",
conf.level = 0.95)
Paired t-test
data: medical$depression1 and medical$depression2
t = 11, df = 245, p-value <2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
8.021 11.719
sample estimates:
mean of the differences
9.87
Let’s repeat the same analysis with the lsr
package.
pairedSamplesTTest(formula = ~ depression1 + depression2,
conf.level = 0.95,
one.sided = FALSE,
data = medical)
Paired samples t-test
Variables: depression1 , depression2
Descriptive statistics:
depression1 depression2 difference
mean 32.585 22.715 9.870
std dev. 12.112 14.287 14.727
Hypotheses:
null: population means equal for both measurements
alternative: different population means for each measurement
Test results:
t-statistic: 10.51
degrees of freedom: 245
p-value: <.001
Other information:
two-sided 95% confidence interval: [8.021, 11.719]
estimated effect size (Cohen's d): 0.67
Conclusion: With the significance level of \(\alpha=.05\), we reject the null hypothesis that the average depression scores for the baseline and 6th month are the same in the population, \(t(245)=10.51\), \(p < .001\), \(CI_{95}=[8.02, 11.72]\).
6.6 Analysis of Variance (ANOVA)
Independent-samples \(t\)-test that we have seen earlier is suitable for comparing the means of two independent groups. But, what if there are more than two groups to compare? One could suggest that we run multiple \(t\)-tests to compare all possible pairs and make a decision at the end. However, each statistical test that we run involves a certain level of error (known as Type I error) that leads to incorrect conclusions on the results. Repeating several \(t\)-tests to compare the groups would increase the likelihood of making incorrect conclusions. Therefore, when there are three or more groups to be compared, we follow a procedure called Analysis of Variance – or shortly ANOVA.
Suppose we have \(K\) number of populations. Collect a random sample of size \(n_1\) from population 1, \(n_2\) from population 2, …, \(n_K\) from population \(k\). We assume all populations have the same standard deviation (and they are normally distributed). We wish to test the following null hypothesis:
\[H_0: \mu_1=\mu_2=\mu_3=\dots=\mu_K\] against
\[H_1: H_0 \text{ is false}\]
which means that all of the groups would have equal means in their populations. If at least one of the groups has a significantly different mean, then we would reject the null hypothesis and run post-hoc tests to find out which group(s) are different.
Let \(N = \sum_{k = 1}^{K} n_k\) be the grand total, \((\overline{x}_k = \frac{1}{n_k} \sum_{i = 1}^{n_k} x_{ki}\)) be the sample mean for sample \(k\), and \(\overline{x}_{\cdot} = \frac{1}{N} \sum_{k = 1}^{K} \sum_{i = 1}^{n_k} x_{ki}\) be the grand mean. Then, the \(F\)-statistic is
\[F = \frac{\sum_{k = 1}^{K}\left(\overline{x}_k - \overline{x}_{\cdot}\right)^2/(K - 1)}{\sum_{k = 1}^{K} \sum_{i = 1}^{n_k} \left(x_{ki} - \overline{x}_k\right)^2/(N - K)}\]
with degrees of freedom of \(df_1 = K - 1\) and \(df_2 = N - K\).
If \(F > F_{critical}\) at the \(\alpha\) level of significance, we reject the null hypothesis; otherwise we retain the null hypothesis.
6.6.1 Example
Using the medical
dataset, we want to investigate whether patients with different types of substance addition had the same level of depression at the baseline. We will use depression1
and substance
for this analysis. Before we begin the analysis, let’s visualize the distribution of depression1
by substance
to get a sense of potential group differences.
boxplot(formula = depression1 ~ substance,
data = medical,
main = "Depression Scores by Substance Type",
ylab = "Depression at the baseline")
The boxplot shows that the means are close across the three groups but we cannot tell for sure if the differences are negligible to ignore. We will use the aov
function – which is part of base R.
# Compute the analysis of variance
<- aov(depression1 ~ substance, data = medical)
aov_model1
# Summary of the analysis
summary(aov_model1)
Df Sum Sq Mean Sq F value Pr(>F)
substance 2 1209 605 4.23 0.016 *
Residuals 243 34733 143
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Conclusion: With the significance level of \(\alpha=.05\), the output shows that \(F(2, 243) = 4.23, p < .05\), indicating that the test is statistically significant and thus we need to reject the null hypothesis of equal group means. This finding also suggests that at least one of the groups is different from the others.
As the ANOVA test is significant, now we can compute Tukey HSD (Tukey Honest Significant Differences) for performing multiple pairwise-comparison between the means of groups. The function TukeyHD()
takes the fitted ANOVA as an argument and gives us the pairwise-comparison results.
TukeyHSD(aov_model1)
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = depression1 ~ substance, data = medical)
$substance
diff lwr upr p adj
cocaine-alcohol -4.62684 -8.7730 -0.4807 0.0245
heroin-alcohol -0.08964 -4.7249 4.5456 0.9989
heroin-cocaine 4.53720 -0.1281 9.2025 0.0586
In the output above,
- diff: difference between means of the two groups
- lwr, upr: the lower and the upper end point of the confidence interval at 95%
- p adj: p-value after adjustment for the multiple comparisons
It can be seen from the output that only the difference between cocaine and alcohol is significant with an adjusted p-value of 0.0245.
We can add other variables into the ANOVA model and continue testing. Let’s include sex
as a second variable.
# Substance + Sex
<- aov(depression1 ~ substance + sex, data = medical)
aov_model2 summary(aov_model2)
Df Sum Sq Mean Sq F value Pr(>F)
substance 2 1209 605 4.36 0.0138 *
sex 1 1199 1199 8.65 0.0036 **
Residuals 242 33534 139
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Substance + Sex + Substance x Sex
<- aov(depression1 ~ substance*sex, data = medical)
aov_model3 summary(aov_model3)
Df Sum Sq Mean Sq F value Pr(>F)
substance 2 1209 605 4.38 0.0135 *
sex 1 1199 1199 8.69 0.0035 **
substance:sex 2 435 218 1.58 0.2083
Residuals 240 33098 138
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The output shows that sex
is also statistically significant in the model; but the interaction of sex
and substance
is not statisticall significant.
6.7 Exercise 9
Now you will run two hypothesis tests using the variables in the medical
dataset:
Run an independent-samples \(t\)-test where you will investigate whether the average depression scores at the baseline (i.e.,
depression1
) are the same for suicidal patients (i.e.,suicidal == "yes"
) and non-suicidal patients (i.e.,suicidal == "no"
).You will conduct an ANOVA to investigate whether the patients’ physical scores at the baseline (i.e.,
physical1
) differ depending on their race (i.e.,race
).
6.8 Additional Packages to Consider
There are also additional R packages that might be useful when conducting \(t\)-tests. One of these packages is report (https://github.com/easystats/report). This package automatically produces reports of models and data frames according to best practices guidelines (e.g., APA’s style), ensuring standardization and quality in results reporting.
# Install the activate the package
install.packages("report")
library("report")
# Run an independent-samples t test and create a report
report(t.test(male, female, conf.level = 0.95, alternative = "two.sided"))
# Run ANOVA and create a report
report(aov(depression1 ~ substance + sex, data = medical))
Effect sizes were labelled following Cohen's (1988) recommendations.
The Welch Two Sample t-test testing the difference between male and female (mean of x = 31.50, mean of y = 36.18) suggests that the effect is negative, significant and medium (difference = -4.67, 95% CI [-8.42, -0.93], t(87.09) = -2.48, p < .05; Cohen's d = -0.53, 95% CI [-0.96, -0.10])
The ANOVA (formula: depression1 ~ substance + sex) suggests that:
- The main effect of substance is significant and small (F(2, 242) = 4.36, p = 0.014; Eta2 (partial) = 0.03, 90% CI [4.06e-03, 0.08])
- The main effect of sex is significant and small (F(1, 242) = 8.65, p = 0.004; Eta2 (partial) = 0.03, 90% CI [6.71e-03, 0.08])
Effect sizes were labelled following Field's (2013) recommendations.
Finally, report also includes some functions to help you write the data analysis paragraph about the tools used.
report(sessionInfo())
Analyses were conducted using the R Statistical language (version 4.0.4; R Core Team, 2021) on Windows 10 x64 (build 19043), using the packages skimr (version 2.1.2; Elin Waring et al., 2020), ggplot2 (version 3.3.3; Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.), dplyr (version 1.0.5; Hadley Wickham et al., 2021), kableExtra (version 1.3.4; Hao Zhu, 2021), car (version 3.0.10; John Fox and Sanford Weisberg, 2019), carData (version 3.0.3; John Fox, Sanford Weisberg and Brad Price, 2019), report (version 0.2.0; Makowski et al., 2020) and lsr (version 0.5; Navarro, 2015).
References
----------
- Elin Waring, Michael Quinn, Amelia McNamara, Eduardo Arino de la Rubia, Hao Zhu and Shannon Ellis (2020). skimr: Compact and Flexible Summaries of Data. R package version 2.1.2. https://CRAN.R-project.org/package=skimr
- H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
- Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.5. https://CRAN.R-project.org/package=dplyr
- Hao Zhu (2021). kableExtra: Construct Complex Table with 'kable' and Pipe Syntax. R package version 1.3.4. https://CRAN.R-project.org/package=kableExtra
- John Fox and Sanford Weisberg (2019). An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. URL: https://socialsciences.mcmaster.ca/jfox/Books/Companion/
- John Fox, Sanford Weisberg and Brad Price (2019). carData: Companion to Applied Regression Data Sets. R package version 3.0-3. https://CRAN.R-project.org/package=carData
- Makowski, D., Ben-Shachar, M.S., Patil, I. & Lüdecke, D. (2020). Automated reporting as a practical tool to improve reproducibility and methodological best practices adoption. CRAN. Available from https://github.com/easystats/report. doi: .
- Navarro, D. J. (2015) Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.5) University of Adelaide. Adelaide, Australia
- R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.


We take pride in claiming to be one of the leading Heavy Fabricators, machining of heavy jobs and the equipment manufacturing company. Over the years, We have Specialized in manufacturing of critical equipments, Heavy Fabrication, Huge and boast machining jobs, erection, profile formation, dishing, hoist mechanisms, columns and towers, Storage tanks and components for process industries like Cement, Steel, Sugar, Chemical and Power etc.
Our slogan "Nothing is Impossible" Stands proven through the critical and challenging jobs executed in the past and as a driving force for our endeavors in the future.
We have ll the and machining facilities in house, located in Belgaum, our works "Unit 1" and "Unit II" are equipped with modern machinery and equipments to maximize automation, with huge network of dependable vendors, we ensure smooth progress of our work on our shop floor to honour our most sacrosanct commitment of "Timely Delivery".
We have ll the and machining facilities in house, located in Belgaum, our works "Unit 1" and "Unit II" are equipped with modern machinery and equipments to maximize automation, with huge network of dependable vendors, we ensure smooth progress of our work on our shop floor to honour our most sacrosanct commitment of "Timely Delivery".
At TMP, we continuosly refine quality management process, calling on the market expertise of every one from front line manufacturing and development personell to marketing and administration, from material suppliers to customers- all together in pursuit of a common goal "Superior Customer Satisfaction"


Market Pulse on November 8th
by Olivier Rigot, EMC Gestion de FortuneS&P 500 3093,08 +7,90.
Over the last week, the stock market continued to creep higher with less momentum and deteriorating sentiment. The market is clearly overbought, according to various indicators we monitor and our global risk profile index is hitting the highs of its historical trading band. The best scenario would call for a quick pullback to the breaking point in the area of 3000-3040.


2_blin00.py
#!/opt/python3.9/bin/python3
from bisect import *
from collections import *
from functools import *
from heapq import *
from typing import *
import sys
INF = float('inf')
T = lambda: defaultdict(T)
class Solution:
def maximizeXor(self, nums: List[int], queries: List[List[int]]) -> List[int]:
N = len(nums)
M = len(queries)
trie = T()
def bignum_dp(n):
return bin(n)[2:].zfill(32)
nums.sort()
queries = sorted(enumerate(queries), key=lambda x: x[1][1])
result = [None] * len(queries)
last = 0
for query_idx, (x, limit) in queries:
while last < len(nums) and nums[last] <= limit:
n = nums[last]
reduce(dict.__getitem__, bignum_dp(n), trie)['#'] = n
last += 1
guh = bignum_dp(x)
r = 0
node = trie
for c in guh:
c = int(c)
wanted = c ^ 1
if str(wanted) in node:
node = node[str(wanted)]
elif str(wanted ^ 1) in node:
node = node[str(wanted ^ 1)]
else:
node = None
break
if node is None:
result[query_idx] = -1
else:
result[query_idx] = node['#'] ^ x
return result


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Swap rows
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PLANET SICK-BOY: http://www.sick-boy.com "We Put the SIN in Cinema"
Some people complained about German-born Roland Emmerich being chosen to direct The Patriot – the country's premiere film about our fight for independence.
A wise woman once asked `But what about the children?' (okay, it was
Reverend Lovejoy's wife on The Simpsons). The U.S.'s latest kiddie
flick to hit theatres is set in Paris and directed by
Luckily, Rugrats in Paris is a pretty decent film. As an adult, I enjoyed this sequel a lot more than the first Rugrats picture, especially the hilarious send-up of The Godfather's opening scene. Instead of Connie Corleone's pending nuptials, this film begins with a wedding between Grandpa Lou and his new bride Lulu (voiced by Debbie Reynolds, although Talia Shire would have been a perfect fit). The festivities are highlighted by a mother-son dance, in which the perpetually scared, two-toothed redhead named Chuckie wishes aloud that his mother was still around to dance with him (apparently, she's been dead for some time).
In the meantime, Tommy's father Stu is called to France, where his giant mechanical Reptar has malfunctioned, potentially delaying the opening of a stage performance (a la `Miss Saigon') in Euro-Reptar Land, a Japanese theme park in Paris. For some reason, Stu decides to bring not only his whole family, but all of his friends and their families, too. A mildly amusing montage of the packing, passport application process and air flight ensues.
When the gang hits Paris (where wee-wee becomes oui-oui), we're introduced to Coco (Susan Sarandon), the director of the Reptar stage extravaganza who is under the impression that she needs to find a family in order to receive a promotion within the Japanese conglomerate that owns the theme park. Coco latches onto Chuckie's father, Chas, who seems like a better match for Coco's pretty Japanese assistant, Kira (Melanie Griffith).
If this jumble of characters seemed confusing to you, you've probably never seen the show. While Paris may still be enjoyable if you haven't, it probably helps if you've taken in at least a few episodes. The kids spurt out funny malapropisms, like `over my dad's potty,' instead of `over my dead body,' just like the television series. Paris also includes some really slick animation, save the crudely drawn characters, especially with shadows and reflections.
I was really enjoying the film until they started in with the `Who Let the Dogs Out' song (not a great way to teach little kids to keep their traps shut while they're in a movie theatre). Also glaringly evident was the fact that Paris had more fart/shit/piss gags than Adam Sandler's new film (which was rated PG-13 to Paris' G). While the film's story would probably have been better suited for an hour-long television special (with commercials) than an 80-minute film, Paris is still heads and shoulders above most recent children's films.
1: 20 – G but contains a lot of fart, piss and shit gags
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--- title: Toggle Switch section: Components ---
toggle-switch-text
or Toggle Switch with Data Attributes.
Addsimple-toggle-switch-reverse
tosimple-toggle-switch
to align the label text on the left. This utility should be used when you only have limited access to the toggle switch markup.
Use data attributes
data-label-on=""
anddata-label-off=""
on``````
to display specific text when the switch is on and off.
Add additional text with class
toggle-switch-text
.
Add an icon to the switch for the on position with
``````
.Add an icon to the switch for the off position with
``````
.
Add an icon to the switch for the on position with
``````
.Add an icon to the switch for the off position with
``````
.
Disable a toggle-switch by adding the attribute
disabled
to``````
.
These are examples of
toggle-switch
using the``````
pattern.


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Preparation time
overnight
Cooking time
over 2 hours
Serves
Serves 8
This rich and rib-sticking risotto is the real deal, combining bold flavours with superior homemade stock.
Place the beef shin pieces in a bowl and cover with red wine. Cover the bowl with cling film and chill in the fridge overnight. The next day, remove the beef from the red wine and pat dry with kitchen paper. Reserve the red wine.
Preheat the oven to 150C/130C Fan/Gas 2.
Heat a tablespoon of oil in a flameproof casserole over a medium heat. Fry the beef shin pieces for a few seconds on all sides, or until nicely browned all over. Remove from the pan and set aside.
Add the carrot, celery and onion to the casserole and fry gently for 4-5 minutes, stirring regularly, until softened. Add the bay leaves and star anise, then lay the browned meat on top of the vegetables. Sprinkle over the salt and pour in the reserved red wine. Bring the mixture the boil.
Add the chicken stock and return the mixture to the boil. Cover the casserole with a lid and transfer to the oven. Cook for 3½-4 hours, or until the beef shins are very tender.
Remove the beef shins from the casserole. Pull the meat off the bone and break into flakes. Extract the marrow from the bones. Set aside and keep warm. Strain the stock from the casserole through a fine sieve and keep warm.
For the risotto, heat the oil and butter in a saucepan over a medium heat. Add the onion and garlic and fry for 3-4 minutes, or until soft but not coloured.
Stir in the risotto rice and cook for 1-2 minutes, or until coated in the fat and slightly translucent.
Add the warm beef stock a ladleful at a time, stirring well after each addition and allowing almost all of the liquid to be absorbed before adding the next ladleful.
Continue to add the stock until the rice is just tender (you may not need to use all the beef stock).
Remove the risotto from the heat and stir in the flaked meat, a knob of butter, the blue cheese and the thyme leaves. Add the bone marrow, to taste and stir well until the butter and cheese have melted, then serve in shallow bowls.
Estevez Chilean Pinot Noir 2013, Primarius Oregon Pinot Noir 2011 USA and Fairbank Pinot Noir 2013 South East Australia.


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1 Samuel 19:13
And Michal took an image, and laid it in the bed, and put a pillow of goats' hair for his bolster, and covered it with a cloth.


Hi, I am Kathan Desai.
I believe in Learn. Experiment. Fail. Improve.
I believe in Learn. Experiment. Fail. Improve.
I'm a 18-year-old Entrepreneur,UI/UX Developer,App Developer,and Photographer from New Delhi.I am the Co-founder and CTO of Ananda.
I love to make fully functional websites/apps.I am very passionate
about my work and like to learn new things.
Also I love to eat.
Since few years, I have been learning different languages which help me in developing proper UI. I have chosen science stream in class 11 with computer science. I like to innovate things be it with long lines of codes or some complex electronics. I would like you to see some of my skills :
Arduino,Raspberry Pi,
soldering
HTML,CSS,JS,
AngularJS,Ionic
C++,JavaScript,Node.js,
Express,and
Mongoose,SQL,Firebase
Ionic,Cordova,
AngularJs,Typescript
I am practicing photography and videography from a year now.
You can watch some of my work in the gallery section.
Have some project idea to start with? Let’s meet over a cup of coffee to discuss more about your dreams and
how we can take it to next level through design.
Mail me at :
kthndesai@gmail.com


Holisticly synergize maintainable intellectual
capital via viral interfaces. Energistically
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View DetailsCredibly build virtual materials for fully researched paradigms. Authoritatively plagiarize long-term high-impact infomediaries with goal-oriented core competencies. Conveniently visualize process-centric applications through leading-edge testing procedures. Quickly build backend customer service for real-time metrics.
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Credibly build virtual materials for fully researched paradigms. Authoritatively plagiarize long-term high-impact infomediaries with goal-oriented core competencies. Conveniently visualize process-centric applications through leading-edge testing procedures. Quickly build backend customer service for real-time metrics.
Efficiently transform an expanded array of methodologies rather than customized infomediaries.
Credibly build virtual materials for fully researched paradigms. Authoritatively plagiarize long-term high-impact infomediaries with goal-oriented core competencies. Conveniently visualize process-centric applications through leading-edge testing procedures. Quickly build backend customer service for real-time metrics.
Efficiently transform an expanded array of methodologies rather than customized infomediaries.
Credibly build virtual materials for fully researched paradigms. Authoritatively plagiarize long-term high-impact infomediaries with goal-oriented core competencies. Conveniently visualize process-centric applications through leading-edge testing procedures. Quickly build backend customer service for real-time metrics.
Efficiently transform an expanded array of methodologies rather than customized infomediaries.


Preguntas frecuentes
Pregunta número uno
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Pregunta número dos
Intently sniff hand intrigued by the shower need to chase tail or need to chase tail for throwup on your pillow, throwup on your pillow. Why must they do that. Run in circles stare at ceiling but sleep on keyboard. Chew foot. Claw drapes. Why must they do that flop over find something else more interesting lick butt hopped up on goofballs.
Pregunta número tres
Intently sniff hand intrigued by the shower need to chase tail or need to chase tail for throwup on your pillow, throwup on your pillow. Why must they do that. Run in circles stare at ceiling but sleep on keyboard. Chew foot. Claw drapes. Why must they do that flop over find something else more interesting lick butt hopped up on goofballs.
Pregunta número cuatro
Intently sniff hand intrigued by the shower need to chase tail or need to chase tail for throwup on your pillow, throwup on your pillow. Why must they do that. Run in circles stare at ceiling but sleep on keyboard. Chew foot. Claw drapes. Why must they do that flop over find something else more interesting lick butt hopped up on goofballs.
Pregunta número cinco
Intently sniff hand intrigued by the shower need to chase tail or need to chase tail for throwup on your pillow, throwup on your pillow. Why must they do that. Run in circles stare at ceiling but sleep on keyboard. Chew foot. Claw drapes. Why must they do that flop over find something else more interesting lick butt hopped up on goofballs.


Adyen API Library for Ruby
Set up a client and set the credentials to the Adyen platform.
require 'adyen'
adyen = Adyen::Client.new
The example connects you to the Adyen test platform. If you want to use our live platform use:
adyen.env = :live
You can now use the following services:


Function call performance measurements using @MeasureSpeed and measureSpeed():
@MeasureSpeed
measureSpeed()
You can debug the SpringType ecosystem using the developer console:
console
window.$st
window.$ste


| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
We strive to maintain backwards compatibility and to provide deprecated versions of old classes and methods. However, sometimes when a new version is released, changes are made that require programmers to change their code and rarely the on-disk database formats.
| 6.1 Notes About Version 5.1 | Notes about version 5.1 | |
| 6.2 Notes About Version 5.0 | Notes about version 5.0 | |
| 6.3 Notes About Version 4.49 | Notes about version 4.49 | |
| 6.4 Upgrade to Version 4.45 | Upgrade to version 4.45 | |
| 6.5 Problems With Version 4.43 | Problems with version 4.43 | |
| 6.6 Upgrade to Version 4.37 | Upgrade to version 4.37 | |
| 6.7 Upgrade to Version 4.30 | Upgrade to version 4.30 | |
| 6.8 Upgrade to Version 4.20 | Upgrade to version 4.20 | |
| 6.9 Upgrade to Version 4.14 | Upgrade to version 4.14 | |
| 6.10 Upgrade to Version 4.10 | Upgrade to version 4.10 | |
| 6.11 Upgrade to Version 4.0 | Upgrade to version 4.0 | |
| 6.12 Upgrade to Version 3.2 | Upgrade to version 3.2 | |
| 6.13 Upgrade to Version 2.4 | Upgrade to version 2.4 |
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
In version 5.0, we moved to using English for the field and method name capitalization because of problems with how certain languages capitalize certain characters. Some of the capitalization points were missed however and this version fixes a couple of additional spots where field and method capitalization was still using the default locale.
If you want ORMLite to use the old way to keep compatibility, you should generate your connection source with a custom database type that uses your default locale by overriding the following methods:
upCaseEntityName(String)
upCaseString(String, boolean)
downCaseString(String, boolean)
These methods should just return string.toUpper()
or string.toLower()
which will use your default
locale. You can also specify a specific locale if necessary.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
A number of things have changed and it’s been a long time since the last published ORMLite release, so a .0 number on the release seemed to be warranted. See the following details.
Locale.ENGLISH
. This means that your schema may need to change. To go back
to the old behavior, you should override the appropriate DatabaseType
and override the
upCaseEntityName(String)
method.
java.io.Closeable
interface in a number of places for Java 7 support. This means
that you may not get warnings about unclosed iterators and other things. Watch for and pay attention to them. They are
important. Java 7 is not required.
ConnectionSource
interface so now the
table name is specified which allows the ConnectionSource
to make decisions about which database to connect to
depending on the table.
java.sql.Timestamp
and java.sql.Date
as strings
in the database. If you were using this types before you may have to add a persisterClass = TimeStampType.class
or SqlDateType.class
.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
The 4.49 release took a long time to put out unfortunately so it included a lot of changes, a couple of which need to be specifically enumerated. For more details, please check the change-log.
dao.callBatchTasks(...)
calls from multiple threads when dealing with a single database connection. ORMLite
now makes an attempt to detect a single-connection connection-source and will enforce locking so that only one batch
task can be running at a time.
CloseableIterator
and other classes extend java.io.Closeable
. If
you are extending any of these classes then you may have to tweak your code.
maxForeignAutoRefreshLevel
configuration setting which was particularly impacting
Android users.
Again, please check the change-log for the entire list.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
To fix the date-string format bug introduced in version 4.43, I’ve decided to revert back to the date-string of
yyyy-MM-dd HH:mm:ss.SSSSSS
from the one introduced in 4.43 which was yyyy-MM-dd HH:mm:ss.SSS
. This means
that for folks doing date comparisons or using the version = true
feature, if you have created data under 4.43
you will have to add the following to your date fields to make it work.
@DatabaseField(version = true, format="yyyy-MM-dd HH:mm:ss.SSS", dataType=DataType.DATE_STRING) private Date date; |
If you have data that was created both in 4.42 or before and 4.43 or 4.44 then you will have to convert some of
the data. Something like the following UPDATE
statement should work:
UPDATE your-table SET your-date-field = CONCAT(SUBSTRING(your-date-field, 1, 20), "000", SUBSTRING(your-date-field, 20, 3)) WHERE LENGTH(your-date-field) = 23; |
My sincere apologies for this mess up.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
In the process of looking into adding the timezone to the date-string, I made a change to the date-string format from
yyyy-MM-dd HH:mm:ss.SSSSSS
to yyyy-MM-dd HH:mm:ss.SSS
– changing the milliseconds output from 6 digits to 3.
This relatively small change broke the equality checking for date-strings which meant that Where.eq(...)
and
version = true
field settings ceased to work correctly. The version processing code uses equality to verify that
the object date is the same with the database and the new string value ...###
is not equal to the old database
format of ...######
.
We have fixed this problem in 4.45 which reverts the string format. If you want to fix it permanently, you can use the
format
specifier on your date-string fields:
@DatabaseField(version = true, format="yyyy-MM-dd HH:mm:ss.SSSSSS", dataType=DataType.DATE_STRING) private Date date; |
My apologies for this problem.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
For Android users, in 4.37 we fixed a problem with the looking up of column names with imbedded periods in them by implementing
our own lookup and not using the Android API. This had the unintended consequence of making the field name lookups be case
sensitive. If you used ORMLite to generate your tables or if you used the @DatabaseField
columnName
to match the case then you would not be affected by this issue. But if you were working with an existing database with
field names that did not match the case of the Java fields, then as of 4.37 you would be seeing the following exception.
java.sql.SQLException: Unknown field 'accountName' from the Android sqlite cursor, not in:[accountname, ...]" |
Since other parts of the system are also case sensitive, we made the decision to not fix this problem but to encourage
our users to properly use the @DatabaseField
columnName
if the case of your database does not match your
Java fields. See columnName.
For example, before 4.37 your Android SQLite database might have the column accountname
although your Java field might
actually be accountName
. As of 4.37, when you look up your Java fields you will have to add a columnName
value
like the following:
@DatabaseField(columnName = "accountname") private String accountName; |
We made the decision to force this change because there are other parts of ORMLite that are already case sensitive.
For example, if you had mismatched case in your field names then using the dao.queryForMatching(obj)
method would
not work without the case matching the database. If you were building a custom query, you would have to say
queryBuilder.where().eq("columnname", value)
and could not use columnName
.
Sorry for not recognizing this incompatibility earlier.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
For Android users, in 4.30 we added some reflection hacks to make the processing of the @DatabaseField
annotations
a lot faster. For this reason the following changes must be made:
DatabaseTableConfig
instead. This means that if you have defined custom Dao classes, you will need to add
a new constructor:
public class AccountDaoImpl extends BaseDaoImpl<Account, String> implements AccountDao { public AccountDaoImpl(ConnectionSource connectionSource) throws SQLException { super(connectionSource, Account.class); } // NOTE: this constructor is needed under Android in 4.30 public AccountDaoImpl(ConnectionSource connectionSource, DatabaseTableConfig<Account> tableConfig) throws SQLException { super(connectionSource, tableConfig); } } |
@DatabaseField
annotation we have deprecated the
@DatabaseFieldSimple
and other annotations that were added in version 4.26. Sorry for the reversal on this but
they were also causing confusion.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
In 4.20 we made a couple of changes that bear some note.
strings.xml
resource file is still supported although it is not necessary if you are using
OrmLiteBaseActivity
or the other base classes. If you are extending those classes, ORMLite will detect the helper
class automagically. The SqliteOpenHelperFactory
mechanism, although still supported, has been deprecated.
See database open helper wiring.
uniqueCombo
setting for uniqueness across field combinations. We also changed the SQL that was generated
if you are using the unique
setting. The old SQL was correct but the new versions are more compatible with the SQL standard
and the various database types. See unique constraints.
TableUtil
methods. Thanks much to various
folks on the user list who significantly helped with this process.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
In 4.14 we added a DatabaseType.BYTE_ARRAY
which stores byte[]
directly. See BYTE_ARRAY. In the past, this array
would have been stored as a serialized array of bytes. To not break backwards compatibility with the database, fields with the
byte[]
type must now specify either the BYTE_ARRAY
or SERIALIZABLE
types using the dataType
field
on @DatabaseField
– it will not be chosen automatically. See DatabaseField dataType. If we did not do this then
previously stored data would be read from the database improperly.
In addition, serialized types must also now specify their dataType
value. You should use DataType.SERIALIZABLE
to
continue to store serialized objects in the database. This will allow us to add direct support for other serialized types in the
future without breaking backwards compatibility.
If you already have Serializable data (byte[] or other objects) stored in a database then you will need to add something like the following to your fields:
@DatabaseField(dataType = DataType.SERIALIZABLE) Serializable field; |
For newly stored byte[] fields, you could use the BYTE_ARRAY type to store the bytes directly. But any existing data will not be converted automatically.
@DatabaseField(dataType = DataType.BYTE_ARRAY) byte[] field; |
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
4.10 was a reasonably large release containing some feature upgrades and some bug fixes. No data formats were changed, however the following API code was altered:
RawResults
class which is now deprecated and replaced it with the
GenericRawResults
class. See the GenericRawResults
for more information. See section Issuing Raw Queries.
Dao
methods queryForAllRaw()
and iteratorRaw()
are now deprecated. They are replaced with
queryRaw(...)
methods. See the Dao
class javadocs for more information.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
No data formats were changed, however the following API code was altered. Removed any outside usage of the DatabaseType
since
the ConnectionSource
now provides it. Also added features to be able to prepare update and delete statements. To provide
type safety, we’ve moved back to using QueryBuilder
so we can have UpdateBuilder
and DeleteBuilder
. And instead
of a PreparedStmt
there is PreparedQuery
, PreparedUpdate
, and PreparedDelete
. Here are the details:
DatabaseType
.
All you need to set on the DAOs is the ConnectionSource
which provides the database type
internally. To create and drop the tables, also, you only need the ConnectionSource
.
BaseDaoImpl
now self-initializes if it is constructed with a
ConnectionSource
. This validates the class configurations meaning that it now throws a
SQLException
.
JdbcConnectionSource
or DataSourceConnectionSource
also now
throws a SQLException
since they also now self-initialize if they are constructed with the URL.
This creates the internal database type and loads the driver class for it.
createJdbcConnectionSource
method in the DatabaseTypeUtils
and
turned the loadDriver
method into a no-op. You now just instantiate the JdbcConnectionSource
directly and there is no need for referencing the DatabaseTypeUtils
anymore.
Dao.statementBuilder()
method changed (back) to Dao.queryBuilder()
.
Dao.queryBuilder()
returns a QueryBuilder
instead of a StatementBuilder
.
distinct()
, limit()
and offset()
on the QueryBuilder
.
Unfortunately, there are no deprecated methods for them on the StatementBuilder
.
selectColumns()
on the QueryBuilder
instead of columns()
since now we have columns also in the UpdateBuilder
. Unfortunately, there are no deprecated
methods for them on the StatementBuilder
.
QueryBuilder.prepare()
instead of StatementBuilder.prepareStatement()
.
It returns a PreparedQuery
instead of a PreparedStmt
. You pass a PreparedQuery
into the Dao.query()
and Dao.iterator()
methods instead of a PreparedStmt
.
DatabaseTypeFactory
class since it was no longer needed for Spring
configurations.
BaseJdbcDao
since it had been deprecated in 3.X.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
The 3.2 release involved a very large code reorganization and migration. There were no on-disk changes unless you somehow
managed to get ORMLite working previously on Android. The project was basically split into 3 pieces: core functionality, JDBC
database handlers, and the new Android handler. With significant help from Kevin G, we abstracted all of the database calls into
3 interfaces: ConnectionSource
(like a DataSource
), DatabaseConnection
(like a Connection
) and
DatabaseResults
(like a ResultSet
). Once we had the interfaces in place, we wrote delegation classes for JDBC and
Android handlers. This means that as of 3.X we release 3 packages: ormlite-core (for developers), ormlite-jdbc (for people
connecting to JDBC databases), and ormlite-android (for Android users). Both the JDBC and Android packages include all of the
core code as well.
Along the way a number of specific changes were made to the methods and classes:
BaseJdbcDao
to be BaseDaoImpl
in the core package. You will
need to adjust any DAOs that you have.
QueryBuilder
object to be StatementBuilder
. NOTE: this was reverted later.
Dao.queryBuilder()
method to be statementBuilder()
. NOTE: this
was reverted later.
PreparedQuery
object to be PreparedStmt
.
DataSource
is that you no longer set it on the
DAO directly. You need to wrap your DataSource
in a DataSourceConnectionSource
wrapper class which gets
set on the DAO instead.
Again, there were no on-disk changes unless you somehow managed to get ORMLite working previously on Android. Since we were using JDBC before to do the data marshaling and now are doing it by hand, some of the data representations may have changed. Sorry for the lack of detail here.
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
A bug was fixed in 2.4 with how we were handling Derby and Hsqldb. Both of these databases seem to be capitalizing table and field names in certain situations which meant that customized queries of ORMLite generated tables were affected. In version 2.4, all tables and field names are capitalized in the SQL generated for Derby and Hsqldb databases. This means that if you have data in these databases from a pre 2.4 version, the 2.4 version will not be able to find the tables and fields without renaming to be uppercase.
| [ << ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
This document was generated by Gray Watson on February 19, 2018 using texi2html 1.82.


Consider the case that the equilibrium distribution is Maxwellian in
velocity space:
|
(76) |
which satisfies the normalization condition
. The derivative of with respect to is written
|
(77) |
Using this, Eq. (75) is written
|
(78) |
Take the initial condition of to be
|
(79) |
which is a Maxwellian distribution that satisfies the normalization
.
Equation (78) with the initial condition Eq. (79) was
solved numerically to obtain the time evolution of (the code is in
/home/yj/project/landau_damping/). Figure 7 compares the velocity
distribution function at
with that at
,
which shows that the distribution function develops fine structures in
velocity space.
Figure:
Comparison of at and
. (a) real part; (b) imaginary part.
.
|
It is ready to realize that the fine velocity space structures are partially
due to the first term on the right-hand side of Eq. (78). When only
this term is retained, Eq. (78) is written
|
(80) |
which has the dispersion relation
|
(81) |
which indicates that Eq. (80) has different eigenfrequencies for
different points in velocity space. Thus, an initially rather smooth velocity
distribution function will become not so smooth after some time due to the
distribution function oscillate with different frequencies at different
velocity points. This is the so-called ``phase mixing''. It is obvious that,
after some time, the phase mixing will make the velocity distribution function
rather messy, which poses a great challenge to the numerical
resolution of
. Given a fixed velocity grids, the numerical
results will become inaccurate when the grids is not fine enough to resolve
the fine velocity distribution structures.
Note that the electric field is related to the integration of , i.e.
|
(82) |
Then it is fairly obvious that the phase mixing have the possibility of
reducing the magnitude of the perturbed electric field. Figure 8
plot the time evolution of (the factor
in
Eq. (82) is removed), which shows that electric field oscillates
with the amplitude decreasing with time. This confirms that the phase mixing
reduces the magnitude of the electric field.
Figure:
(a) time evolution of real and imaginary parts of
. (b) time evolution of
.
.
|
yj
2016-01-26


◄ 24th IPHO 1993 ►
Country results • Individual results
| Contestant | Country | Rank | Award |
|---|---|---|---|
| Junan Zhang | People's Republic of China | 1 | Gold Medal |
| Harald Pfeiffer | Germany | 1 | Gold Medal |
| Linbo Li | People's Republic of China | 3 | Gold Medal |
| Sándor Katz | Hungary | 4 | Gold Medal |
| Calin Ciocarlie | Romania | 5 | Gold Medal |
| Tomáš Kočka | Czech Republic | 6 | Gold Medal |
| Roman Belenov | Russia | 7 | Gold Medal |
| Iosif Bena | Romania | 8 | Gold Medal |
| Vassili Larkin | Russia | 9 | Gold Medal |
| Antonin Machacek | United Kingdom | 10 | Gold Medal |
| Gábor Veres | Hungary | 11 | Gold Medal |
| Salih Adem | Turkey | 12 | Gold Medal |
| Jens Dean | United States of America | 13 | Gold Medal |
| Martin Beneš | Czech Republic | 14 | Gold Medal |
| Lajos Molnár | Hungary | 15 | Gold Medal |
| Stepan Andreenko | Russia | 16 | Gold Medal |
| Renat Jakupov | Ukraine | 17 | Gold Medal |
| Alireza Shahidzadeh | Islamic Republic of Iran | 18 | Gold Medal |
| Martin Kassabov | Bulgaria | 19 | Silver Medal |
| Zhanfeng Jia | People's Republic of China | 20 | Silver Medal |
| Yongjik Kim | Republic of Korea | 21 | Silver Medal |
| Denis Chigirev | Russia | 22 | Silver Medal |
| Thomas Dent | United Kingdom | 23 | Silver Medal |
| Anatoli Olkhovets | Ukraine | 24 | Silver Medal |
| Carsten Geckeler | Germany | 25 | Silver Medal |
| Milos Gaj | Slovakia | 26 | Silver Medal |
| Oleg Shpirko | Ukraine | 27 | Silver Medal |
| Christian Fleischhack | Germany | 28 | Silver Medal |
| Jiří Vaníček | Czech Republic | 29 | Silver Medal |
| Jaromír Fiurášek | Czech Republic | 30 | Silver Medal |
| Tao Wei | People's Republic of China | 31 | Silver Medal |
| Robin Morris | United Kingdom | 32 | Silver Medal |
| Daniel Schepler | United States of America | 33 | Silver Medal |
| Denis Kislovsky | Russia | 34 | Silver Medal |
| Egidijus Anisimovas | Lithuania | 35 | Bronze Medal |
| Reimar Finken | Germany | 36 | Bronze Medal |
| Akshay Venkatesh | Australia | 37 | Bronze Medal |
| Emanuel Tutuc | Romania | 38 | Bronze Medal |
| Oki Gunawan | Indonesia | 39 | Bronze Medal |
| Gregor Veble | Slovenia | 40 | Bronze Medal |
| Ilia Vassilev | Bulgaria | 41 | Bronze Medal |
| Milos Volauf | Slovakia | 42 | Bronze Medal |
| James Anderson | United Kingdom | 43 | Bronze Medal |
| Péter Urbán | Hungary | 44 | Bronze Medal |
| Andrei Alexandru | Romania | 45 | Bronze Medal |
| Sinan Arslan | Turkey | 46 | Bronze Medal |
| Erwin Portuondo Campa | Cuba | 47 | Bronze Medal |
| Chee We Ng | Singapore | 48 | Bronze Medal |
| Hal J. Burch | United States of America | 49 | Bronze Medal |
| Zhining Huang | People's Republic of China | 50 | Bronze Medal |
| Marco Galimberti | Italy | 51 | Bronze Medal |
| Ioannis Vetsikas | Greece | 52 | Bronze Medal |
| Bojan Gornik | Slovenia | 53 | Bronze Medal |
| Yoo Chul Chung | Republic of Korea | 54 | Bronze Medal |
| Andras Major | Germany | 55 | Bronze Medal |
| Robert Kry | Canada | 56 | Bronze Medal |
| Erik Larsson | Sweden | 57 | Bronze Medal |
| Iacopo Carusotto | Italy | 58 | Bronze Medal |
| Xiao-Dong Yang | Canada | 59 | Bronze Medal |
| Jurgen Hissen | Canada | 60 | Bronze Medal |
| Frank Van Lankvelt | Netherlands | 61 | Bronze Medal |
| Thanh Minh Thai | Vietnam | 62 | Bronze Medal |
| Dmitri Linde | United States of America | 63 | Bronze Medal |
| Alexey Kvitsinski | Ukraine | 64 | Bronze Medal |
| Yong-il Shin | Republic of Korea | 65 | Bronze Medal |
| Jan-Alexander Heimel | Netherlands | 66 | Bronze Medal |
| Mehdi Yahya Yahyanejad | Islamic Republic of Iran | 67 | Bronze Medal |
| David Weickhardt | Australia | 68 | Honourable Mention |
| Adrian Muresan | Romania | 69 | Honourable Mention |
| Simon Burton | Australia | 70 | Honourable Mention |
| Ivan Daikov | Bulgaria | 71 | Honourable Mention |
| Paul Mitchener | United Kingdom | 72 | Honourable Mention |
| Kamil Budinsky | Slovakia | 73 | Honourable Mention |
| Chang Shih Chan | United States of America | 74 | Honourable Mention |
| Şeref Burak Özaydemir | Turkey | 75 | Honourable Mention |
| Mariusz Szyposzyński | Poland | 76 | Honourable Mention |
| Anton Darmenov | Bulgaria | 77 | Honourable Mention |
| Mehmet Burak Yılmaz | Turkey | 78 | Honourable Mention |
| Andrea Montanari | Italy | 79 | Honourable Mention |
| Slaven Garaj | Croatia | 80 | Honourable Mention |
| Jarosław Potiuk | Poland | 81 | Honourable Mention |
| Nathaniel Kuang Chern Ng | Singapore | 82 | Honourable Mention |
| Boyko Zlatev | Bulgaria | 83 | Honourable Mention |
| Marco Van Leeuwen | Netherlands | 84 | Honourable Mention |
| Jemmy Wijaya | Indonesia | 85 | Honourable Mention |
| Jaehyuk Choi | Republic of Korea | 86 | Honourable Mention |
| Paul Tupper | Canada | 87 | Honourable Mention |
| Marko Karlusic | Croatia | 88 | Honourable Mention |
| David Por Bragason | Iceland | 89 | Honourable Mention |
| Ewald Roessl | Austria | 90 | Honourable Mention |
| Daniel Svenšek | Slovenia | 91 | Honourable Mention |
| Damir Pajic | Croatia | 92 | Honourable Mention |
| Taner Akbaş | Turkey | 93 | Honourable Mention |
| Quang Long Ngo | Vietnam | 94 | Honourable Mention |
| Khoong Hong Khoo | Singapore | 95 | Honourable Mention |
| Miglius Alaburda | Lithuania | 96 | Honourable Mention |
| Sirisin Janrunguatai | Thailand | 97 | Honourable Mention |
| András Gefferth | Hungary | 98 | Honourable Mention |
| John Fitzgerald | Australia | 99 | Honourable Mention |
| Daniel Průša | Czech Republic | 100 | Honourable Mention |
| Christian Pfaffel | Austria | 101 | Honourable Mention |
| Franz Diwoky | Austria | 102 | Honourable Mention |
| Andrzej Komisarski | Poland | 103 | Honourable Mention |
| Hans Bornefalk | Sweden | 104 | Honourable Mention |
| Kang Looi Choo | Singapore | 105 | Honourable Mention |
| Rahim Bagheri | Islamic Republic of Iran | 106 | Honourable Mention |
| Alireza Nujeh | Islamic Republic of Iran | 107 | Honourable Mention |
| Saviz Saffarian | Islamic Republic of Iran | 108 | Honourable Mention |
Results may be incomplete or incorrect.
Please send the relevant information to the webmaster:
<EMAIL_ADDRESS>If you are a participant you can send a personal page URL to be attached to your name.
If you are a participant you can send a personal page URL to be attached to your name.


Package io.fluentlenium.configuration
package io.fluentlenium.configuration
-
ClassDescriptionAbstract registry of FluentLenium factories.
ConfigurationProperties
based onFluentConfiguration
annotation.Base configuration class supporting setting of the global configuration.Factory ofCapabilities
A registry ofCapabilitiesFactory
.Capabilities registry default implementation.Desired capabilities factory.A configuration composed with a writable configuration and list of read configurations.Base configuration interface that can be read and written.Default configuration implementation.Exception thrown when something wrong occurs because of the configuration of FluentLenium.Creates new configuration from a container class.Provider of configuration factory.Mutation interface of Configuration Properties.FluentLenium can be configured in many ways through configuration properties.Driver lifecycle.Trigger mode for Screenshots and HtmlDump featuresAnnotation configuration custom property.Creates new configuration from a container class.Mark a factory as default factory that can be overriden without throwing an exception.Properties backend based on javaProperties
object.Utility class with default WebDriver factories.Chrome WebDriver factory.Edge WebDriver factory.Firefox WebDriver factory.HtmlUnit WebDriver factory.Internet Explorer WebDriver factory.Opera WebDriver factory.Remote WebDriver factory.Safari WebDriver factory.Properties backend based on environment variables.Marker interface for factories.Define names for a factoryAdd names to a factory.Defines the priority of the factory.Configure a FluentLenium test class with this annotation.Boolean value used in annotation that can has a third DEFAULT value equivalent to null.Classes marked with this annotation will be ignored by automatic registration inFactory
CapabilitiesFactory
based on invocation of a defined method.Configuration
based on JavaBean properties.Reader interface for raw properties access.Abstract properties configuration.A simpleCapabilitiesFactory
that createCapabilities
instances using reflection.Reflective factory.A simpleWebDriverFactory
that createWebDriver
instances using reflection.Properties backend based on java system properties.Factory ofWebDriver
, that can be registered inWebDrivers
registry.A registry ofWebDriverFactory
.WebDrivers registry implementation.


Display any file, group of files, or folder as a bitmap image made from the raw bytes of the file. Save and load pictures, sort them and extract the unique colors. Export a series of images made from a group of files using the Large File Processor.
Latest release: Version 1.0.0 (released August 11, 2017)
DetailsThis project is Alpha and is still in development. IronArc is a virtual machine running a simple custom instruction set. The IronArc host runs multiple VMs and will provide access to the machines using the Debugger. Attach virtual and physical hardware (terminals, I/O, and disk access). This is not meant for production or serious use - it is a learning exercise in low-level programming.
Latest Update: Alpha v0.1 [rev. 2] released, fixing an IronAssembler bug. Next: Working on a disassembler and debugger.
DetailsThis project is pre-Alpha and is still in development. Cix (pronounced "six") is a compiler project for a simple C-like language. Its primary use will be to produce programs for the IronArc platform.
Latest Update: First stages of AST generation (structure definitons and function headers) are in progress.
DetailsA browser-based incremental/clicker game. Click for units to buy items that make you more units! Buy powerful upgrades and unlock achievements! Some upgrades even unlock special powers and features!
Latest Update: Added more upgrades. Up Next: More achievements and rebalancing.
Details


Contour lines allow us to visualise the relief of a mapped area. On South African topographic maps these lines are brown and the contour interval is always 20 m. The bold lines are called index contours and the interval between these is 100 m. The numbers on contour lines can help you to determine whether altitude is increasing or decreasing because they are always written the ‘right way up’. In other words altitude increases above a number on a contour line and decreases below it.
Contour lines never cross each other but the distance between them tells us about the gradient of the terrain. The closer the contour lines, the steeper the slope.


Package ch.rabanti.picoxlsx4j.style
-
Class Summary Class Description AbstractStyle Class represents an abstract style componentBasicStyles Factory class with the most important predefined stylesBorder Class representing a Border entry.CellXf Class representing an XF entry.Fill Class representing a Fill (background) entry.Font Class representing a Font entry.NumberFormat Class representing a NumberFormat entry.Style Class representing a style which consists of several componentsStyleManager Class representing a style manager to maintain all styles and its components of a workbook -
Enum Summary Enum Description Border.StyleValue Enum for the border styleCellXf.HorizontalAlignValue Enum for the horizontal alignment of a cellCellXf.TextBreakValue Enum for text break optionsCellXf.TextDirectionValue Enum for the general text alignment directionCellXf.VerticalAlignValue Enum for the vertical alignment of a cellFill.FillType Enum for the type of the fillFill.PatternValue Enum for the type of the fill patternFont.SchemeValue Enum for the font schemeFont.VerticalAlignValue Enum for the vertical alignment of the text from base lineNumberFormat.FormatNumber Enum for predefined number formats -
Annotation Types Summary Annotation Type Description AppendAnnotation Annotation designated to control the copying of style properties


Claim: "Some Story your Friend told you about"
(Click or hover over
for help )
Predicted Correctness
True
False
Overall Political Leaning
Liberal
Conservative
Source Information
News Source
Source Political Leaning
Source Reputation
Deny/Neutral/Support
abc.com
Liberal
Conservative
0.0
lion.com
Liberal
Conservative
0.75
xyz.com
Liberal
Conservative
1.0
politexas.com
Liberal
Conservative
0.5
usp.com
Liberal
Conservative
0.25


Presented by Jacques Labuschagne
Centralised teams and distributed teams are not so different.
Think about what makes a centralised team run well, and apply those principles to distributed teams.
Standard iterative methods
Plan → Do → Check → Act
Direction
Competence
Satisfaction
It's (almost) always a communication problem.
[…]in order to attain the same communication “bandwidth” that a co-located team can achieve, distributed team members will incur additional time spent in team meetings with other remote team members. To facilitate the distributed teams, a set of tools and an environment to support the tools and teams will also need to be introduced and maintained.
—Monica Yap, Successful Distributed Agile Team Working Patterns.
The hive mind is shared knowledge, traditions, consensus.
The hive mind is connected by all forms of communication — conversation, email, IRC, documentation, …
People will communicate in the most convenient way.
Put them together in the same office and they'll default to spoken conversation, cutting out remote members.
Divert communication back along shared channels so that remote workers are not at a disadvantage.
Decided not to co-locate the Wellington team members.
Much easier for the locals, so stress phone conf etiquette.
Maybe try having everyone join by phone one day.
You need online tools — physical post-it notes are no good.
Set up a mail alias for the team and CC it on everything.*
Don't answer questions in person; update the wiki.†
“Work noisy”
If they have time to code, sound the alarm…
Should focus on policies, architecture, reviews, etc.
Team's interpretation of that output → hive mind QA.
* YMMV, might not make sense for small (1-2 dev) teams.
Workarounds: Set guidelines that empower. Make corrections after the fact.
A difference of more than half a day is a problem.
Workarounds: Minimize contention — try to keep story tasks within one TZ.
Maximize overlap — in extreme cases this could mean shifting working hours.
Holy grail: making timezones work for you — having issues fixed over night for QA to pick up in the morning.
Potential factors:
Workarounds: Boot Camp vs. Rotating Guru/Ambassador patterns.
People should be chatty on IRC, esp. early on.
Team members need to take responsibility for their participation — even more important for remote work.
Workarounds: Guidance, cajoling, careful team selection, trial periods.
Distributed teams are more likely to also be large teams, and large teams have their own set of problems.
See also: Standups vs. Scrum of Scrums, dedicated sub-teams with improved autonomy.
open source technologists


Posted by January 8, 2016
onToday's patch updates come with the standard set of bug fixes. However, this week we've added five new articles to the docs and two new skeletons for Visual Studio users.
Posted by December 17, 2015
onToday's patch updates come with some really sweet benefits! Also, if you love TypeScript, I'm sure you'll be very happy with something new we are announcing today. To update your libraries, simply run jspm update. Read on to learn the details of what these releases include.
Posted by December 4, 2015
onYesterday's patch releases contain some exciting performance improvements! In today's post, Core Team Member Jeremy Danyow shares a bit about how he's worked to improve repaint scenarios.
Posted by December 3, 2015
onNow that the Beta is out, we're focusing our time on bug fixes, performance and polish. This week we've released a number of patches to libraries that fix bugs and enhance performance in certain scenarios. To get the latest, simply run jspm update. You can find the release notes below. Stay tuned for tomorrow's post on performance...
Posted by November 20, 2015
onOn the final day of our beta week announcements, we wanted to leave you with a sneak peek of what we are working on for the future. One of the most common requests we've heard from the community is the desire for a component library. I'm happy to say, we've been thinking of that too...since the very beginning. We've dubbed our component library "Aurelia Interface".
Posted by November 19, 2015
onWe continue our beta release week with some exciting news about our ecosystem. The Aurelia team is hard at work developing resources for all types of Aurelia users from Web Devs to Designers to CIOs. We understand the importance of having a rich ecosystem and that being able to get the information or help you need to stay productive is vital. There are also many external resources that are being developed by our excellent community. We'd like to take a few words to highlight a small portion of the work currently happening to build Aurelia's ecosystem.
Posted by November 18, 2015
onWhen we launched the alpha of Aurelia back in February, we did so with a set of very preliminary documentation. Today we're launching our new docs, which include an updated (simpler) getting started guide, various topical articles, a cheat sheet and a full API reference.


在《暗黑破壞神III》中，你將從7個誓死消滅惡魔的不同冒險者職業中選擇其一來進行扮演英雄。每個英雄都擁有不同樣貌、不同遊戲方式和使用不同的能量來對抗怪物，所以選擇你的職業絕對不僅僅是個外型的選擇。
Hero is sent to discover that the mystery of the falling star is a Stranger who cannot remember who or what he is. He recalls a sword in his possession that broke into 3 pieces that fall to Sanctuary in the vicinity of the Cathedral in Tristram.
The Hero then pursues all three pieces of the sword to re-forge it only to have Deckard Cain realize the sword belongs to none other than Tyrael, the Archangel of Justice. Shortly thereafter, Cain is killed by the demoness Maghda, working under the Lesser Evil, Belial’s command. Maghda then proceeds to kidnap Tyrael and the Hero runs off to rescue him...
Upon arriving in Caldeum, the Hero learns of the turmoil surrounding the desert city. Their child ruler, Emperor Hakkan, has appeared to grow more and more troubled as of late, placing only trust in his suspicious new royal guards.
The heroes then move aid the city in its trouble by freeing the Iron Wolves and learning of what’s been happening around the Kingdom. Leah and Tyrael, in the meantime, go out in search of Leah’s mother who has been said to be hiding somewhere in Caldeum...
Azmodan’s armies are on the march. Leah’s visions and communication with Azmodan confirmed this. The Heroes and Tyrael work to secure Arreat’s defenses and advance the attack. After disarming Azmodan’s artillery, the Heroes take the fight to the Crater.
The Heroes descend into the depths of the Crater in search of the Heart of Sin, in order to call Azmodan out and stop his forces from pouring out into Sanctuary...
The Heroes arrive in time to see Tyrael arguing with Imperius. Imperius will not stand to see humans set foot in the High Heavens. He warns Tyrael of transgressing any further and takes off after Diablo to fight off the corruption taking root.
The Heroes and Tyrael push forward and rescue Itherael and Auriel from Diablo’s corruption, who then aid the Heroes in making their way to the Spire, fighting numerous Lesser Lords along the way, including the reincarnation of Izual...
It begins in Westmarch and ends in the Pandemonium Fortress. Malthael and his Reapers serve as the main antagonists, while Adria returns as a secondary antagonist.
you have to stop the demonic rituals that the reapers are doing on the townsfolk to kill them. This part of the act ends with the boss fight against a Maiden of Flame...


Custom Widgets
BVD has an API to create your own widgets with JavaScript, D3 and jQuery. With a dynamic loading feature you can also load your own SVG library to make widget authoring even easier. Click this link to learn more about custom widgets and start your own creation with one of the provided examples.
Welcome to the BVD community pages.
Here you will find material free of charge to enhance your BVD experience.
Sample Generators
Often you want to start to work with BVD w/o having all data sources in place. E.g. to verify your dashboards or to prepare for demos. We provide a flexible data generator to send simulated real-time data to BVD. Follow this link to find out more.
Data Push Connectors
Data push connectors are code snippets to help linking data sources to the BVD push API. Click this link to learn more
Support or Contact
Want to learn about BVD and the Operations Bridge Suite? Check out our documentation and product page.


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