breastfeeding health Health Nova childhood obesity policy {border-style Canada need px;} #ffffff;} Code Scotia focus breastfeeding, unsupportive culture support collab: leadership provincial work Breastfeeding Canadian importance multiple promote public supportive International Public challenges don’t important strategy understanding BFI World challenge formula groups particularly px; viewed way {font-family Healthy Marketing action district evaluation exclusive font-size including it’s just know lack members mothers physicians process role seen Obesity Organization address committees double; environment environments healthy implementation involved key mother needs senior supported theme themes think (accessed Available Breast-milk Promotion Scotia, accreditation achieving addressing border-color border-width breastfeed child designation development engaging epidemic felt greater initiation leadership, national norms online: people perceived physicians, population-based prevention promoting promotion province rates really resources social solid; strong time value women {font-style {font-weight (WHO) Agency Department Geneva, NS, ON, Ottawa, Provincial Research Scotia. Strategy Substitutes Support Switzerland accepted awareness bold breastfeeding. breastfeeding; broader care challenging change come committee courier; data duration efforts em; findings free global health. held interviews long months number obesity. offers participants point policy. prevention. provide providing pt;} required research risk spoke strategies that, widely you’re


Hi, So the emonTXv3 works, but I want to have a play. Plug in the USB to UART cable, and expected the TX to run off of the USB power so I could see the serial output, but the RF transmission isn't running and nothing on the Serial Monitor in the Arduino IDE. The docs say to remove the AC power, which I have, but the board seems dead. How do you get to see the debug log on serial output to see the emon working? I tried removing the AC - DC jumper but still no life. Removing the USB to UART cable and a reset and it's back to life. I don't want to force upload the latest firmware incase it's not setup correctly and that destroys it.
Using a MAC with the UART driver loaded, it comes up as either /dev/cu.SLAB_USBtoUART or /dev/tty.SLAB_USBtoUART, tried with both but I was assuming the tty one is the right one.
The reason I'm wanting to play is I assume I need to calibrate it? The readings are a little high, the Vrms is over 255 and the clip on meter I already have on the mains shows about 20% less power usage than the emon does.
Cheers, Darren
Re: How to talk to the emonTX V3 via the serial monitor
Ah, facepalm! Had the header plugged in the wrong way round. Assumed the LED was meant to be up. Seems to be doing a lot better now...
Re: How to talk to the emonTX V3 via the serial monitor
Good to hear you got it working, we'll do some testing ourselves next week and check the voltage calibration
Re: How to talk to the emonTX V3 via the serial monitor
Cheers. I've set Vcal to 269.66 after some checks with my MM and the figures are closer. I also tested 2 CTs on the same wire to a 100W bulb (with 3 windings) and they were showing different values, 310 - 318. Is that normal? I remember seeing a page with comparrison figures, do you use that to set Ical? I'll do some more calibration tomorrow and see what it needs on each CT to get them to read the same.
Another odd thing I noticed, the 1wire doesn't work. I tried with 2 different DS18B20's neither are recognized. Should the OneWire DS18x20_Temperature example sketch work with the emonTXv3? I set the OneWire pin to 19 although that seems to be Analogue 5 for the power, so also tried digital 5, which seems to be data. Not sure if I'm using the right port, or maybe I have that wired in wrong too :-$ I tested with VDD coming direct from the 5V supply also...
Re: How to talk to the emonTX V3 via the serial monitor
You can expect 3 CTs on 3 inputs to have slightly different Ical values. There's a 1% tolerance on the burden resistor and a 3% tolerance on the CT itself, so two CT's calibration values might be 8% different.
Re: How to talk to the emonTX V3 via the serial monitor
Hi Robert, Since the 3 CTs, 1-3 on the V3 use the same Ical, is it worth trying to calibrate each CT fixed on a specific input to account for individual variations or is that going a bit too far ;-) I think I ended up seeing +6% on one input and -5% on another, which might be stable over a wide range of powers so easy to balance out.
Re: How to talk to the emonTX V3 via the serial monitor
I'm answering pretty much the same question here: http://openenergymonitor.org/emon/node/3292
Yes, I think you do need separate calibration constants, and I've writtten to Glyn.


Test Case: Set of related test functions
testCaseFunction([ {assertion1}, {assertion2}, {assertion3}, ... ])


A Programmer
I am a Web Developer and I am currently working in the feild of Interner Of Things.
⭐⭐⭐
⭐⭐⭐⭐
© 2020 Sayantan Kundu.


|
App Engine Python SDK
v1.6.9 rev.445
The Python runtime is available as an experimental Preview feature.
|
Public Member Functions | |
| def | indent |
| def | start_serialization |
| def | end_serialization |
| def | start_object |
| def | end_object |
| def | handle_field |
| def | handle_fk_field |
| def | handle_m2m_field |
| Public Member Functions inherited from google.appengine._internal.django.core.serializers.base.Serializer | |
| def | serialize |
| def | get_string_value |
| def | start_serialization |
| def | end_serialization |
| def | start_object |
| def | end_object |
| def | handle_field |
| def | handle_fk_field |
| def | handle_m2m_field |
| def | getvalue |
Public Attributes | |
| xml | |
| Public Attributes inherited from google.appengine._internal.django.core.serializers.base.Serializer | |
| options | |
| stream | |
| selected_fields | |
| use_natural_keys | |
Additional Inherited Members | |
| Static Public Attributes inherited from google.appengine._internal.django.core.serializers.base.Serializer | |
| internal_use_only = False | |
Serializes a QuerySet to XML.
| def google.appengine._internal.django.core.serializers.xml_serializer.Serializer.end_object | ( | self, | |
| obj | |||
| ) |
Called after handling all fields for an object.
| def google.appengine._internal.django.core.serializers.xml_serializer.Serializer.end_serialization | ( | self | ) |
End serialization -- end the document.
| def google.appengine._internal.django.core.serializers.xml_serializer.Serializer.handle_field | ( | self, | |
| obj, | |||
| field | |||
| ) |
Called to handle each field on an object (except for ForeignKeys and ManyToManyFields)
| def google.appengine._internal.django.core.serializers.xml_serializer.Serializer.handle_fk_field | ( | self, | |
| obj, | |||
| field | |||
| ) |
Called to handle a ForeignKey (we need to treat them slightly differently from regular fields).
| def google.appengine._internal.django.core.serializers.xml_serializer.Serializer.handle_m2m_field | ( | self, | |
| obj, | |||
| field | |||
| ) |
Called to handle a ManyToManyField. Related objects are only serialized as references to the object's PK (i.e. the related *data* is not dumped, just the relation).
| def google.appengine._internal.django.core.serializers.xml_serializer.Serializer.start_object | ( | self, | |
| obj | |||
| ) |
Called as each object is handled.
| def google.appengine._internal.django.core.serializers.xml_serializer.Serializer.start_serialization | ( | self | ) |
Start serialization -- open the XML document and the root element.


Buttons Tests
Regular Buttons
An anchor Button
Disabled Buttons
An anchor Button
Active Buttons
An anchor Button
Primary/Selected Buttons
An anchor Button
Customizing Buttons
An anchor Button
Custom Button
Custom Button
Start Playing Movie
Primary Form Buttons
Anchor
Primary Form Buttons (Custom Fonts)
Anchor


ws
: A nimble webserver for prototyping
Home
ws at github
License
TUTORIAL.md
TODO.md
me
ABOUT.md
NOTES.md
SEARCH-CONTACT_AND_COMMENTS.md
Self-Signed-OpenSSL.md
SOMEDAY-MAYBE.md
TODO.md
TUTORIAL.md


Pride & Prejudice -- Reader Club
Home
Movie&TV
About Writer
Contact
FAQ
I am Wicky, a web developer.
Pride and Prejudice influenced me a lot during my whole childhood.
If you want to contact me, you can use the following e-mail address:
<EMAIL_ADDRESS>Wicky Gao
Web Developer
Auckland, New Zealand
PH:<PHONE_NUMBER>
beini.gao@gmail.com


Do you have any questions? Please do not hesitate to contact us directly. Our team will come back to you within a matter of hours to help you.
San Francisco, CA 94126, USA
+ 01 234 567 89
<EMAIL_ADDRESS>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Voluptatibus, ex, recusandae. Facere modi sunt, quod quibusdam.


{% extends "common/page_base.html" %} {# common/page_base.html extends layout.html #} {% block content %}
Seq2Flux is a web-based computational platform for analyzing the transcriptomics data to accurately predict comprehensive phenotypes (e.g., fold changes of growth rate, sugar utilization, chemical synthesis rate, etc.) via an advanced artificial intelligence (AI) model (i.e., auto-encoder neuron network) with a high-throughput manner.
{% if not current_user.is_authenticated %}To view the User page, you must Sign in or Register.
{% endif %}
Seq2Flux is implemented with the following unique features:
1. Comprehensive and accurate phenotype predictions from large-scale transcriptomics data
2. High-throughput analysis of transcriptomics data
3. User-friendly interface and job management system
You can upload either processed transcriptomics data (i.e., fold changes of gene expression levels with corresponding statistical values) or raw sequencing machine readouts (i.e. RNA-seq sequencing data) and click the "submit the job" button! The comprehensive physiological simulations of your samples will be sent to your email box immediately. You can also follow your job status via the job management system in your account. With Seq2Flux platform, you will experience the power of artificial intelligence applying to biology.
{% endblock %}


The Site Builder gradle plugin is a static site generation tool. It's goal is to avoid writing HTML pages directly, but at the same time to let an high degree of site customization.
The plugin has only two configurable properties, accessible through the site
object in the build.gradle
script.
site {
root '/static/blog/' // the site deploy path on the target host, default is /
indentation 2 // the tab width used to indent HTML tags, default is 4 spaces
}
The project files must be placed inside the following directories.
src/main/groovy
: groovy classes used by the site scripts.
src/main/site
: where the document generation scripts are placed.
The relative path and the name of a script are used as the path and name of the generated page.
For example the script post.groovy
in the blog/posts/
directory generates the blog/posts/post.html
page.
src/main/resources
: contains site resources like images, CSS and JavaScript files.
These files are copied to the site target directory keeping their folder structure.
Every script has access to a context object referenced by a variable called site
.
The site object has the following fields:
site.builder
: the HTML builder currently in use, that is also the script delegate object.site.root
: the absolute path where the site will be deployed on the target host.site.page
: the relative path of the page currently being generated.The builder defines three special tags, called metatags. They are used to request the builder a special behaviour when translating their content.
The simplest one is the text metatag, an underscore that accepts a string as argument. The string it contains will be formatted in the target document as it is, without en enclosing tag.
A metatag that also uses _
as its identifier is the inlining metatag.
It is different from the text metatag in the fact that it accepts a closure as argument instead of a string.
The tags it encloses are formatted on a single line.
For example
_ { _ 'Inlines ' em 'multiple' _ ' tags and text.' }
translates to
Inlines multiple tags and text.
The last one is the extension metatag. It is identified by the dollar sign and accepts a callable object as its argument. The extension is invoked by passing it the HTML builder currently in use. Therefore it can be used to include snippets, which work also as templates if they are configurable. For example
def anchor = { name ->
{ document -> document.a(name: name, '') }
}
_ { $ anchor('section') h2 'Section' }
translates to

Section

Here is presented a script example to explain how the Site Builder works. Given the already defined configuration, the following code
def homeLink = { it.a(href: site.root, 'Home') }
'!DOCTYPE'(html: null)
html(lang: 'en') {
head {
title 'Example Page'
}
body {
h3 'How the Site Builder works'
_ { p { _ 'The link ' $ homeLink _ ' will take you to the homepage.' } }
}
}
generates the HTML below:
<!DOCTYPE html>

<title>Example Page</title>

How the Site Builder works

The link Home will take you to the homepage.

How does the code above translate to the generated HTML? Actually every tag is a groovy method call, which accepts a list of optional named parameters and an optional string parameter. Here is a detailed description of the script behaviour.
def homeLink = { it.a(href: site.root, 'Home') }
it
) to construct a link to the homepage.
The value of site.root
is the absolute path where the site will be deployed on the target host, that can be specified in the plugin configuration.
'!DOCTYPE'(html: null)
!DOCTYPE
can be added by writing it as '!DOCTYPE'
.
The arguments inside the parenthesis are a list of key-value pairs separated by a colon.
If the value is a non-null value, the pair is translated to a tag attribute key="value"
, otherwise the key is translated to a tag flag without a value.
html(lang: 'en') { ... }
title 'Example Page'
_ { p { ... } }
_
does not generate additional HTML code, but it influences how it is formatted.
It requests the builder to format all its children tags on a single line.
This improves the readability of the generated HTML, especially when alternating plain text and tags.
_ 'The link '
$ homeLink
$
includes a snippet in the generated document.
The snippet is a groovy closure that accepts the HTML builder as its argument.
The result is the same as if the closure body were directly inserted inside the script.


Uber's London license hangs in the balance on expiry day
by ReutersBy Costas Pitas
LONDON (Reuters) - London's transport regulator said on Monday it has yet to decide on whether to renew Uber's (N:UBER) operating license in the city after a media report said the taxi app would lose its right to take rides in Britain's capital.
LBC radio reported on Monday, the day the Silicon Valley firm's license expires in London, that Uber will not receive a new license in one of its most important markets, citing several unnamed sources.
A spokesman Transport for London said no decision has been made. Uber declined to comment.
Uber and TfL have been engaged in a long-running battle since the authority rejected a renewal request in 2017, citing shortcomings it said it found in the firm's approach to reporting serious criminal offences and driver background checks, prompting legal action.
A judge in 2018 then granted Uber (N:UBER) a probationary 15-month license, after the Silicon Valley-based company had made several changes to its business model.
In September, TfL gave Uber just a two-month extension, far short of the maximum possible five years, and imposed further conditions covering ride-sharing, appropriate insurance and driver document checks.
Ahead of the latest decision, Uber said it would introduce measures such as a discrimination button enabling drivers and riders to report abuse, enhanced safety training for drivers and a direct connection to the emergency services.


Get started
API Endpoint https://api.westeros.com/
The Westeros API provides programmatic access to read Game of Thrones data. Retrieve a character, provide an oauth connexion, retrieve a familly, filter them, etc.
To use this API, you need an API key. Please contact us at<EMAIL_ADDRESS>to get your own API key.
get characters
# Here is a curl example
curl \
-X POST http://api.westeros.com/character/get \
-F 'secret_key=your_api_key' \
-F 'house=Stark,Bolton' \
-F 'offset=0' \
-F 'limit=50'
To get characters you need to make a POST call to the following url :
payquick-develop.herokuapp.com/api/v1
Result example :
{
query:{
offset: 0,
limit: 50,
house: [
"Stark",
"Bolton"
],
}
result: [
{
id: 1,
first_name: "Jon",
last_name: "Snow",
alive: true,
house: "Stark",
gender: "m",
age: 14,
location: "Winterfell"
},
{
id: 2,
first_name: "Eddard",
last_name: "Stark",
alive: false,
house: "Stark",
gender: "m",
age: 35,
location: 'Winterfell'
},
{
id: 3,
first_name: "Catelyn",
last_name: "Stark",
alive: false,
house: "Stark",
gender: "f",
age: 33,
location: "Winterfell"
},
{
id: 4,
first_name: "Roose",
last_name: "Bolton",
alive: false,
house: "Bolton",
gender: "m",
age: 40,
location: "Dreadfort"
},
{
id: 5,
first_name: "Ramsay",
last_name: "Snow",
alive: false,
house: "Bolton",
gender: "m",
age: 15,
location: "Dreadfort"
},
]
}
QUERY PARAMETERS
| Field | Type | Description |
|---|---|---|
| phone | String | (required) Account phone number with internationna standard format |
| password | String | (required) Valide password. |
| brand | String | (optional) a string array of houses: |
| manufaturer | String | (optional) a boolean to filter alived characters |
| uuid | String | (required) Uniq device identified |
| os | String | (required) Operating system |
| version | String | (required) Operating System version |
| appversion | String | (required) a boolean to filter alived characters |
| latitude | String | (required) a boolean to filter alived characters |
| logitude | String | (required) a boolean to filter alived characters |
| gender | String |
(optional) a string to filter character by gender: m: male f: female |
| offset | Integer | (optional - default: 0) A cursor for use in pagination. Pagination starts offset the specified offset. |
| limit | Integer | (optional - default: 10) A limit on the number of objects to be returned, between 1 and 100. |
send 2FA
# Here is a curl example
curl \
-X POST http://payapi.westeros.com/character/get \
-F 'secret_key=your_token_key' \
-F 'house=Stark,Bolton' \
-F 'offset=0' \
-F 'limit=50'
To get characters you need to make a POST call to the following url :
payquick-develop.herokuapp.com/api/v1
Result example :
{
query:{
offset: 0,
limit: 50,
house: [
"Stark",
"Bolton"
],
}
result: [
{
id: 1,
first_name: "Jon",
last_name: "Snow",
alive: true,
house: "Stark",
gender: "m",
age: 14,
location: "Winterfell"
},
{
id: 2,
first_name: "Eddard",
last_name: "Stark",
alive: false,
house: "Stark",
gender: "m",
age: 35,
location: 'Winterfell'
},
{
id: 3,
first_name: "Catelyn",
last_name: "Stark",
alive: false,
house: "Stark",
gender: "f",
age: 33,
location: "Winterfell"
},
{
id: 4,
first_name: "Roose",
last_name: "Bolton",
alive: false,
house: "Bolton",
gender: "m",
age: 40,
location: "Dreadfort"
},
{
id: 5,
first_name: "Ramsay",
last_name: "Snow",
alive: false,
house: "Bolton",
gender: "m",
age: 15,
location: "Dreadfort"
},
]
}
QUERY PARAMETERS
| Field | Type | Description |
|---|---|---|
| code | String | (required) Account phone number with internationna standard format |
| uuid | String | (required) Valide password. |
| token_key | String | (optional) a string array of houses: |
Transactions histories
# Here is a curl example
curl \
-X POST http://payapi.westeros.com/character/get \
-F 'secret_key=your_token_key' \
-F 'house=Stark,Bolton' \
-F 'offset=0' \
-F 'limit=50'
To get characters you need to make a POST call to the following url :
payquick-develop.herokuapp.com/api/v1
Result example :
{
query:{
offset: 0,
limit: 50,
house: [
"Stark",
"Bolton"
],
}
result: [
{
id: 1,
first_name: "Jon",
last_name: "Snow",
alive: true,
house: "Stark",
gender: "m",
age: 14,
location: "Winterfell"
},
{
id: 2,
first_name: "Eddard",
last_name: "Stark",
alive: false,
house: "Stark",
gender: "m",
age: 35,
location: 'Winterfell'
},
{
id: 3,
first_name: "Catelyn",
last_name: "Stark",
alive: false,
house: "Stark",
gender: "f",
age: 33,
location: "Winterfell"
},
{
id: 4,
first_name: "Roose",
last_name: "Bolton",
alive: false,
house: "Bolton",
gender: "m",
age: 40,
location: "Dreadfort"
},
{
id: 5,
first_name: "Ramsay",
last_name: "Snow",
alive: false,
house: "Bolton",
gender: "m",
age: 15,
location: "Dreadfort"
},
]
}
QUERY PARAMETERS
| Field | Type | Description |
|---|---|---|
| code | String | (required) Account phone number with internationna standard format |
| uuid | String | (required) Valide password. |
| token_key | String | (optional) a string array of houses: |
customer Solde
# Here is a curl example
curl \
-X POST http://payapi.westeros.com/character/get \
-F 'secret_key=your_token_key' \
-F 'house=Stark,Bolton' \
-F 'offset=0' \
-F 'limit=50'
To get characters you need to make a POST call to the following url :
payquick-develop.herokuapp.com/api/v1
Result example :
{
query:{
offset: 0,
limit: 50,
house: [
"Stark",
"Bolton"
],
}
result: [
{
id: 1,
first_name: "Jon",
last_name: "Snow",
alive: true,
house: "Stark",
gender: "m",
age: 14,
location: "Winterfell"
},
{
id: 2,
first_name: "Eddard",
last_name: "Stark",
alive: false,
house: "Stark",
gender: "m",
age: 35,
location: 'Winterfell'
},
{
id: 3,
first_name: "Catelyn",
last_name: "Stark",
alive: false,
house: "Stark",
gender: "f",
age: 33,
location: "Winterfell"
},
{
id: 4,
first_name: "Roose",
last_name: "Bolton",
alive: false,
house: "Bolton",
gender: "m",
age: 40,
location: "Dreadfort"
},
{
id: 5,
first_name: "Ramsay",
last_name: "Snow",
alive: false,
house: "Bolton",
gender: "m",
age: 15,
location: "Dreadfort"
},
]
}
QUERY PARAMETERS
| Field | Type | Description |
|---|---|---|
| code | String | (required) Account phone number with internationna standard format |
| uuid | String | (required) Valide password. |
| token_key | String | (optional) a string array of houses: |
Errors
The Westeros API uses the following error codes:
| Error Code | Meaning |
|---|---|
| X000 | Some parameters are missing. This error appears when you don't pass every mandatory parameters. |
| X001 |
Unknown or unvalid secret_key . This error appears if you use an unknow
API key or if your API key expired.
|
| X002 |
Unvalid secret_key for this domain. This error appears if you use an API
key non specified for your domain. Developper or Universal API keys doesn't have domain checker.
|
| X003 |
Unknown or unvalid user token . This error appears if you use an unknow
user token or if the user token expired.
|


See here what our compiler can and cannot (e.g. if and while loops are supported, system output (scanf, printf) and for loops are not).
| stack | |
|---|---|
| {{location.address}} | {{showOutput(location.value)}} |
| stack registers | |
|---|---|
| Base pointer %14 | {{cpu.stack.bp}} |
| Stack pointer %15 | {{cpu.stack.sp}} |
| memory | |
|---|---|
| {{location.address}} | {{showOutput(location.value)}} |
| global variables | ||
|---|---|---|
| {{location.address}} | {{location.name}} | {{showOutput(location.value)}} |
| none defined |
| flags | ||||
|---|---|---|---|---|
| ZF | PF | SF | CF | OF |
| {{value}} |
| registers | |
|---|---|
| %{{$index}} | {{showOutput(value)}} |


Islamic Jihad's military leaders in Gaza face Abu al-Attar's fate
With the death of Baha Abu al-Attar, there are several candidates to rise in the ranks of the organization in the Strip, which could put them firmly in Israel's sights however much they try to keep out of the spotlight
by Elior LevyThe top brass of Islamic jihad military wing in the Gaza Strip, among them Baha Abu al-Ata, who was assassinated by Israel in a predawn strike, are very devoted to keeping their whereabouts unknown.
They try to stay out of the spotlight, trying not reveal their names or release their images.
Abu al-Ata's exposure by Israel seven months ago was a sign of impending actions against him by the IDF, and with Chief of Staff Aviv Kochavi warning that further targeted killings are not off the table, his associates could also find themselves in Israel's crosshairs.
Head of the Military Wing: Akram al-Ajouri
As part of his role as the leader of the organization's military wing, Ajouri is essentially the deputy to Islamic Jihad leader Ziyad al-Nakhalah.
His house in Syria was allegedly attacked by Israel on Tuesday morning as part of the Abu al-Ata hit, resulting in his son's death. It is still not clear whether Ajouri was hurt as well.
Ajouri was born in Gaza and lives in Syria and Lebanon. He first started as a member of the Islamic Jihad international branch, where he climbed up the ranks to become a senior member of the body's military council.
His other role is as the main caretaker for the organization's funding. He keeps very close ties with Iran, and specifically with the leader of the Revolutionary Guard's Al-Quds division Qasem Soleimani and Hezbollah chief Hassan Nasrallah.
Head of the Southern Gaza Division: Halil Batini
Batini resides in the Tuffah neighborhood of Gaza. He belongs to a family long synonymous with the organization and its ideology. Palestinian sources tell Ynet that Batini is an impressive soldier.
His training was conducted in Iran, traveling through Egypt on his way back, he was detained for several months by the Egyptian police.
It should be noted that this was the same way he reached Iran, from Cairo to Tehran, where his first time around was untroubled by the authorities. Upon his release, Islamic Jihad celebrated his return to Gaza.
From an early age, Batinin was marked for greatness in Islamic Jihad. His brother, Muhamad Batini, who was an armed activist in the organization, was killed by the IDF in 2006 following Israel's withdrawal from the Gaza Strip.
During Operation Protective Edge in 2014, the IDF destroyed his house in an airstrike.
Those who have met him say he acts like a wanted man and rarely goes to see his family.
He was responsible for several attacks against IDF targets, which helped quicken his rise through the ranks.
He is described as a religious man, though not as religious as expected from a senior official in an organization that made fanatical religiosity its mission statement.
Head of the Rafah Division: Khaled Mansur
Mansur oversees the southern sector of the Gaza Strip, including the smuggling routes moving in weapons from Iran, via Egypt.
He's one of the main proponents for cooperation with Hamas, although he was a sworn enemy of Hamas several years ago and was arrested by them several times.
In contrast to Hamas, Islamic Jihad has no centralized leadership in Gaza. The leaders of the various divisions meet in a council to coordinate their actions, receiving direct orders from the group's leaders in Syria.
According to sources, Abu al-Ata was top dog in Gaza, and it is uncertain who will replace him.
Generally, the northern division always had more influence and power than its southern counterpart, making it even more unclear who will rise in the organization's Gaza branch.


Pet
Ramanudom
Computer Science + Women and Gender Studies
Wellesley College Junior
Thai-American
Student Researcher at
Wellesley Center for Women and
Wellesley College CS Department
Passionate about Education and Social Justice
Self-identifies as a Grade-A foodie
Frequent hair color changer


Warning: some users have reported high memory and CPU usage when running the extension on large audits.
This extension passively scans requests looking for values containing hashes using common algorithms (SHA, MD5, etc.). Observed hashes are reported to the user.
Additionally, the extension keeps track of submitted parameter values, such as usernames, email addresses, and ID numbers. When a hash of a previously submitted value is identified, this is also reported to the user.
Here is a brief video that explains the concept: https://youtu.be/KdgeipzmESE.
Requires Java version 8


Last Updated 11:59 p.m. on March 15
Commentary by Nathan Lawrence, KBIA Digital Content Director
GOP Presidential Nomination
3041 of 3041 precincts reported.
Trump
Though election night data provided by the Missouri Secretary of State's office should still be considered unofficial, final counts Tuesday night put Trump slightly edging out Cruz 40.828 percent to 40.653 percent.
Dem. Presidential Nomination
3041 of 3041 precincts reported.
Clinton
Though election night data provided by the Missouri Secretary of State's office should still be considered unofficial, final counts Tuesday night put Clinton slightly edging out Sanders 49.613 percent to 49.365 percent.
City of Columbia Proposition One
38 of 38 precincts reported.
Yes
Reports from the Boone County Clerk's office show the measure, which postpones possible changes to the way Columbia handles trash collection, passed by a wide margin.


---
layout: post
title: "My Week in Review"
date: 2015-08-23 10:50:00 +0000
categories:
tags:
image: https://s3.amazonaws.com/audreywatters/2015-08-23-maine.jpg
imagecredits: https://www.flickr.com/photos/smilla4/7130162301
---
Travel
- Amsterdam to LAX; LAX to Toronto; Toronto to Brooklin (Maine); Brooklin (via Bangor) to Hermosa Beach
- Upcoming week: Hermosa Beach
Hack Education
Essays Elsewhere
Speaking
- I spoke at my Granny’s memorial service in Toronto (The transcript from my eulogy is below)
Known Notes and Fragments
Other Projects
- I’ve offered a comment to Anya Kamenetz on a story about “what’s next” for Pearson
- I talked to Sarika Bansal, the editor of the Medium publication Bright, about a story I am working on about Knewton and promises of adaptive learning – needless to say, Knewton is not pleased I’m writing an article on the topic
- I was on Little Bird's list of "125 Top Women Futurists," and I chatted with the company's CEO Marshall Kirkpatrick about feminism and futurism
- I turned 44
Reading
- I finished reading Sherry Turkle’s Alone Together
Family and Friends
- I saw lots of friends and family at Granny’s memorial in Toronto, including my cousin Pete and his family and my cousin Lucy and hers
- After my quick trip to Toronto, Kin met me at the Philadelphia airport and we flew on to Bangor, Maine, staying a few days with my brother and his family. While there, we saw our friend Damian and his family too. And on my birthday, I also visited Seymour Papert.


Ice 'Em
Play
VR:
Look using cursor and wave fingers over leap motion or click mouse button to shoot.
Icy:
Ice the planes. Everytime you successfully Ice 'Em, your hand becomes colder, use this power carefully ;)


Deuteronomy 1:40
But as for you, turn you, and take your journey into the wilderness by the way of the Red sea.


A basic example of markers with labels. Note that an information window appears whether you click the marker portion or the label portion of the MarkerWithLabel. The two markers shown here are both draggable so you can easily verify that markers and labels overlap as expected.


--- layout: page title: "about me" description: "a little description" ---
My name is Jaison Vieira from Rio de Janeiro, Brazil.
I am a developer working for more than 5 years in the IT field having a strong background on TCP/IP networks and operating systems as Windows and Linux.
Specialized in: iOS, Objective-C, Swift, Ruby on Rails, Java, HTML, CSS, REST, Git, Linux, OS X, Windows.


Download And Install Skype For Business On Windows Skype For Business
Install Skype For Business Office Support
How To Install Skype For Business Server 2015 On Windows Server 2016
Skype For Business Download
Skype For Business Plugin Free Lensfasr
Skype For Business をインストールする Office サポート
Free Skype For Business Basic Client Download Tom Talks
Skype For Business Is Now On Ios Office Support
Skype For Business Client Force Address Book Download Y0av With A Zero
Key Health Indicators Khi For Skype For Business 2019 Skype For Business Server 2015 And Lync Server 2013 Tom Talks
Explore Skype For Business Office Support
Download Skype For Business For Windows Free 16 0 4795 1000
Skype For Business Protocol Workloads Posters Available For Download It Pro
Skype For Business 2015 Client Technical Preview Greiginsydney Com
Get Skype For Business Microsoft Store
Skype For Business Server 2015 Firewall Diagram
Skype For Business Is Now On Android Skype For Business Android
Skype For Business Wikipedia
Skype For Business Topology Builder Could Not Read The Topology Hab S Blab
Installing And Using Skype For Business Web App Youtube
Sql Management Studio With Skype For Business Jeff Schertz S Blog
How To Gather And View Client Side Traces On Skype For Business Client Tom Talks
Skype For Business 2015 Download Gudang Sofware
Skype For Business Server Control Panel Interface Download Scientific Diagram
Microsoft Skype For Business Review Pcmag
How To Update Skype For Business 12 Steps With Pictures


Next: remhash, Previous: hash-table-test, Up: Hash Tables Dictionary
gethash
key hash-table &optional default ⇒ value, present-p
(setf ( gethash
key hash-table &optional default) new-value)
key—an object.
hash-table—a hash table.
default—an object. The default is nil.
value—an object.
present-p—a generalized boolean.
Value is the object in hash-table whose key is the same as key under the hash-table’s equivalence test. If there is no such entry, value is the default.
Present-p is true if an entry is found; otherwise, it is false.
setf may be used with gethash to modify the value associated with a given key, or to add a new entry.
When a gethash form is used as a setf place, any default which is supplied is evaluated according to normal left-to-right evaluation rules, but its value is ignored.
(setq table (make-hash-table)) ⇒ #<HASH-TABLE EQL 0/120 32206334> (gethash 1 table) ⇒ NIL, false (gethash 1 table 2) ⇒ 2, false (setf (gethash 1 table) "one") ⇒ "one" (setf (gethash 2 table "two") "two") ⇒ "two" (gethash 1 table) ⇒ "one", true (gethash 2 table) ⇒ "two", true (gethash nil table) ⇒ NIL, false (setf (gethash nil table) nil) ⇒ NIL (gethash nil table) ⇒ NIL, true (defvar *counters* (make-hash-table)) ⇒ *COUNTERS* (gethash 'foo *counters*) ⇒ NIL, false (gethash 'foo *counters* 0) ⇒ 0, false (defmacro how-many (obj) `(values (gethash ,obj *counters* 0))) ⇒ HOW-MANY (defun count-it (obj) (incf (how-many obj))) ⇒ COUNT-IT (dolist (x '(bar foo foo bar bar baz)) (count-it x)) (how-many 'foo) ⇒ 2 (how-many 'bar) ⇒ 3 (how-many 'quux) ⇒ 0
The secondary value, present-p, can be used to distinguish the absence of an entry from the presence of an entry that has a value of default.
Next: remhash, Previous: hash-table-test, Up: Hash Tables Dictionary


We are a team of engineers who want to solve the employment crisis in
India.
Almost 60% of the youth who are employed in the country are unhappy with
their present jobs. This in turn makes the employer unhappy as stuns the
company's growth. We believe that the root cause of this is that people are
pushed into roles which are not suitable for them. Hence we take a holistic
approach to assess a candidate's profile.
Read our tweet here:
We, The Technocrats, are really excited to share our take on the Job crisis in India.
— Ankita Roy (@ankitaroy21) July 20, 2020
CareerConnect, a one stop solution for candidates and recruiters.
A big thanks to Govt. of Puducherry, @PMOIndia , @DrRPNishank, @abhayjere , @mhrd_innovation #SmartIndiaHackathon2020 pic.twitter.com/dbmcQssZRM
Our portal is designed in way to access the skill set and capacity of the Appplicant accurately and recommend jobs to the suitable candidate against a requirement from the industry.
The best way to access an applicant's inclination towards a particular domain is not just by looking at their resume. This is why along with the resume we ask the candidate to answer relevant questions which can be processed to give factual data.
Our assessment consists of three rounds. The first round is a generic round to determine which domain the candidate would excel the most at. The next round has questions on the domain that got selected in the previous round. Here, knowledge of all the sub domains are tested and the top two sub domains are taken into consideration for the third and final round.
Along with the quiz, we also have a chatbot which acts as a Career Counsellor. The applicants who are extremely confused about which path they should choose can interact with the chatbot and get their queries resolved.
For more information, you can check out the documentation of our portal.
{{ section.desc }}


Agile Lamp Driver
Get Version
0.1.0What
A user-space driver for the Agile Lamp USB Lava Lamp
Installing
sudo gem install agilelamp-driver
The basics
After installing the gem, you should be able to control the Agile Lamp while it is plugged in:
agilelamp-driver red # red means bad!
agilelamp-driver green # green means good!
agilelamp-driver various # meaningless cycling colors
agilelamp-driver bell # the bell is very unpleasant
Source code
You can fetch the source from tarball or git repository:
- rubyforge: http://rubyforge.org/scm/?group_id=6281
- gitorious: website — git://gitorious.org/agile-lamp/mainline.git
git clone git://gitorious.org/agile-lamp/mainline.git
Build and test instructions
cd agilelamp-driver rake test rake install_gem
License
This code is free to use under the terms of the MIT license.
Contact
Comments are welcome. Send comments or questions to Joseph Method.
Joseph Method, 20th May 2008
Theme extended from Paul Battley


Branching is a more advanced command to create a professional project. For example, if there is a complicated function that needs a lot of adjustments,
developers should create a separated branch. This branch is not included in the source code and can only be added to the source code once all the functions are working nicely.
To create a branch, you enter [git branch “branch name”] in the terminal. Branch can be added to the source code by the merge command [git merge “branch that you want to merge"].
To access a branch, we use the checkout command [git checkout "branch name"]
Other commands
Branch can be added to the source code by the merge command [git merge "branch that you want to merge"].
However, in real life working, there will always be one member that is responsible for the whole project
and manage the source code, so instead of merging the branch directly, users should use a command called pulled request. This will allow the project manager to check
the code and add comments to team members before merging the branch into the source code.
One command that is also used commonly is [git status]. Users simply use this command to check the status of a file (whether it is added, committed) so they can perform the suitable tasks.


Below, the essence of POSSA
’s power (and type 1 error
rate, T1ER) calculation for sequential designs is explained using the
simple example of a t-test. Of course, for t-tests specifically, you
could use other and potentially more user-friendly software – however,
the point here is to understand how the POSSA
works, and,
subsequently, it should be possible for you to extend this example to
virtually any statistical null hypothesis test. Cases of multiple
hypotheses (and recording descriptives) are explained on a separate
page, but these too are straightforward extensions of the present
examples.
This tutorial presupposes the basic conceptual understanding of sequential analyses (and the basics or statistical power and T1ER). There are several related free tutorials online (see e.g. Lakens, 2014).
All parameters mentioned below are described in detail in the full
documentation (see ?sim
and ?pow
).
So let’s say you want to compare the means of two independent
continuous variables with a simple t-test. Your smallest effect size of
interest (SESOI) is 5
units (arbitrary units that could
represent anything – e.g., the height of a plant in cm, or the time of
performing a task in seconds, etc.).
First, write a function that simulates the samples (i.e., observed values) for a single instance of the hypothetical experiment in case the null hypothesis is true (no actual difference between the two population means) as well as in case the alternative hypothesis is true (there is in fact difference).
If the null hypothesis “H0” is true, the two samples have the same
mean, let’s say 0
units; and let’s assume normally
distributed values an SD of 10
units for each sample. For
this, two samples can each be simulated via the function
rnorm(sampleSize, mean = 0, sd = 10)
(where
sampleSize
is a variable to be provided via another
function, as explained later). You can assign this to
sample1
, which represents a baseline variable (e.g., “group
A”), and, separately, to sample2_h0
, which represents a
sample that does not differ from the baseline (“H0” true). If the null
hypothesis “H1” is true, the baseline sample may again have a mean of
0
, but the other sample should have the SESOI,
5
units (again assuming an SD of 10
) – which
can be simulated as rnorm(sampleSize, mean = 5, sd = 10)
.
This can be assigned to sample2_h1
, which represents a
sample that does differ from the baseline (“H1” true). (The baseline
sample has identical properties in case of H0 and H1, so it’s enough to
simulate it only once, and use that same resulting sample for both cases
– for the sake of brevity as well as of computational speed.)
All this is to be put into a function, which takes a
sampleSize
argument and returns the simulated samples as a
named list, as follows.
= function(sampleSize) {
customSample list(
sample1 = rnorm(sampleSize, mean = 0, sd = 10),
sample2_h0 = rnorm(sampleSize, mean = 0, sd = 10),
sample2_h1 = rnorm(sampleSize, mean = 5, sd = 10)
) }
Note that the customSample
and sampleSize
variables were written with camel case (likeThis), while the
_h0
and _h1
endings used underscores
(like_this). This alternate notation is used in the present tutorial to
help distinguish what names can be freely chosen, and what is
necessarily named so for the POSSA
package: custom names
are all camel case, while underscores indicate notation necessary for
POSSA
. Here specifically, the returned list’s element names
for the sample that varies depending on whether the null (H0) and
alternative (H1) hypothesis is true must be indicated with
_h0
and _h1
endings, respectively, with a
common root (here: sample2_h
). The root name or the other
variable names (customSample
and sampleSize
)
could be named differently, and everything would work just as well.
Now, write a function that performs the test; here, a one-sided t-test (with equal variances assumed – this is just for the example’s outcome’s comparability for other software, but normally Welch’s test should be preferred).
The function’s parameters must be identical to the names of the list
elements returned by the sample function (here: sample1
,
sample2_h0
, and sample2_h1
). The returned
value must be a named vector that contains the derived p values. The
name of each p value must start with p_
, and end with
_h0
for the “H0” outcome and with _h1
for the
“H1” outcome. (Below, it’s simply p_h0
/p_h1
,
but it could just as well be, e.g.,
p_my_t.test_h0
/p_my_t.test_h1
).
= function(sample1, sample2_h0, sample2_h1) {
customTest c(
p_h0 = t.test(sample1, sample2_h0, 'less', var.equal = TRUE)$p.value,
p_h1 = t.test(sample1, sample2_h1, 'less', var.equal = TRUE)$p.value
) }
(Other information, in particular descriptives such as means or SDs, can also be returned via this function – but this is described in a separate vignette.)
To make sure the two functions are working correctly, as a quick test you can run the following line (with any applicable number argument).
do.call(customTest, customSample(55))
#> p_h0 p_h1
#> 0.67172109 0.03385126
This passes 55
as (arbitrary) sample size to
customSample
, which passes the created samples to
customTest
, which finally returns the named vector with the
p_h0
and p_h1
p values. (Given the
randomization, if you run this line repeatedly, the outcome should
differ each time – however, with large enough numbers, p_h1
is likely to be noticeably smaller than p_h0
.)
Time to load POSSA
.
library('POSSA')
Now, the POSSA::sim
function can be used with
customSample
for fun_obs
(function for
observation values) and customTest
for
fun_test
(function for statistical testing), and any
desired numbers of observation. (This runs the simulation
45000
times by default, so it takes a while. For quick
testing, set the number to a lower value via the n_iter
parameter, e.g., n_iter = 500
.)
= sim(fun_obs = customSample,
dfPvals n_obs = 80,
fun_test = customTest)
The returned dfPvals
contains all the simulated p
values, and can be directly passed to POSSA::pow
to get the
power for any specified alpha level.
pow(dfPvals)
This prints to the console the following.
#> # POSSA pow() results #
#> N(average-total) = 160.0 (if H0 true) or 160.0 (if H1 true)
#> (p) Type I error: .05140; Power: .93327
#> Local alphas: (1) .05000
This is a fixed sample design with 80 observations per each of the
two groups. The function uses an alpha of .05
by default,
this may be modified e.g. to .01
below.
pow(dfPvals, alpha_global = 0.01)
#> # POSSA pow() results #
#> N(average-total) = 160.0 (if H0 true) or 160.0 (if H1 true)
#> (p) Type I error: .00991; Power: .79069
#> Local alphas: (1) .01000
So far, we can easily get the same information from other software,
e.g. pwr::pwr.t.test(n = 80, d = 0.5, alternative = 'greater')
(returns power = 0.93368
) and
pwr::pwr.t.test(n = 80, d = 0.5, sig.level = 0.01, alternative = 'greater')
(returns power = 0.79068
).
However, sequential designs are extremely easy to be expanded from
the above sim
and pow
examples. One just has
to specify multiple instances of observation numbers for the
n_obs
in sim
and specify a local alphas in
pow
, e.g. as below.
= sim(fun_obs = customSample,
dfPvalsSeq n_obs = c(27, 54, 81),
fun_test = customTest)
pow(dfPvalsSeq, alpha_locals = NA)
#> # POSSA pow() results #
#> N(average-total) = 158.6 (if H0 true) or 98.8 (if H1 true)
#> (p) Type I error: .05000; Power: .89922
#> Local alphas: (1) .02288; (2) .02288; (3) .02288
#> Likelihoods of significance if H0 true: (1) .02296; (2) .01631; (3) .01073
#> Likelihoods of significance if H1 true: (1) .42324; (2) .32298; (3) .15300
The alpha_locals = NA
input specifies that all local
alphas are initially NA
, in which case they will all be
calculated to have a single identical number (corresponding to Pocock’s
correction in case of equally spaced looks). Rounding to the
conventional 3 fractional digits, the adjusted constant local alpha
number corresponds to the exact statistics (.023
) provided
in other software,
e.g. gsDesign::gsDesign(k = 3, test.type = 1, alpha = 0.05, sfu = 'Pocock')
.
The console output contains only the most important information, but,
when assigned, the pow
function returns a data frame with
detailed information per each look (and their totals), including the
specified sample sizes, the numbers of iterations, etc. The printed
information includes
p
, the root derived
from p_h0
/p_h1
),The look numbers are always in parenthesis, such that
“(1)
” designates the first look, “(2)
” the
second look, etc.
The console output can also be called as
print(dfPvalsSeq)
. The number of fractional digits to which
the values are to be rounded can be changed via a round_to
argument, such as e.g. print(dfPvalsSeq, round_to = 2)
.
POSSA
helps obtain the local alphas, in sequential
designs, that result in a specified overall (global) T1ER, which may
also be called “global alpha”. This procedure is automatized for most
practical cases, but still users should ideally have a basic idea of how
this works. However, impatient readers (who do not want to have
customized adjustments) can skip to the Specifying initial local
alphas section below.
The essential mechanism is a staircase procedure, where the local
alphas are continually increased when the T1ER is smaller than specified
and decreased when the T1ER is larger then specified, and each direction
change (from decrease to increase or vice versa) moves onto a smaller
step size in the adjustment. For example, the simplest case is when all
local alphas are provided as NA
, as above: in this case,
the initial replacement value (which may be modified via the
adj_init
parameter) is by default the global alpha divided
by the maximum number of looks, as a rough initial estimation.
Subsequently, the given p values are tested with this setting, and a
global T1ER is calculated. If it is larger than specified, the
replacement value is decreased, or vice versa. The amount of decrease is
the first step, by default 0.01
. Then the testing is
repeated, and the change is repeated in the same direction until the
obtained T1ER passes the specified T1ER (e.g., becomes smaller, when
initially larger). Then the replacement value is increased with a
smaller second step size, by default 0.05
. And so on, until
either there are no more steps (as specified) or (more typically) the
obtained T1ER is close enough to the specified one (e.g., by default,
matches it up to 5 fractional digits; may be specified via
alpha_precision
), at which point the procedure stops and
the obtained local alphas (each having the same value) and the
corresponding results are returned and printed.
The adjustment actually happens via a function that can be specified
via the adjust
parameter, but, by default, whenever there
is at least one NA
value among the given
alpha_locals
argument, the function implements the
above-described procedure, and looks as follows.
= function(adj, prev, orig) {
adjust is.na(orig)] = adj
prev[return(prev)
}
In this function, adj
means the adjustment value (in
this case a replacement value), prev
the previous vector of
local alphas (obtained in the last adjustment), and orig
the original vector of local alphas as provided for
alpha_locals
. (For the first adjustment, prev
is the same as orig
). Any sort of custom function may be
provided as adjust
, but it cannot contain any parameter
other than these three (adj
, prev
, and
orig
).
By default, if no NA
value is found among the given
alpha_locals
, the adjust
function uses
multiplication to adjust the given values (with adj_init
set to 1
), as follows.
= function(orig, adj) {
adjust return(orig * adj)
}
By default, there are altogether 11 step sizes, chosen in a way that
typically results in 5-fractional-digit T1ER match before running
getting to the last step, and the calculation typically takes only a few
seconds. (Step sizes can be modified via the
staircase_steps
parameter.)
Given the above-described default mechanisms, there are, without
modifying the adjustment procedure, two easy ways to provide the
argument for alpha_locals
(for any given p value): (a) a
vector that includes (or consist entirely of) NA
values
that are to be replaced with a constant number that results in the
desired global T1ER, or (b) a vector with all numeric values that are
each to be multiplied with a number in order to obtain a vector of
alphas that again result in the desired global T1ER.
However, you can also simply check the results, without any
adjustment, of any given set of local alphas, by setting
adjust = FALSE
.
Here are some examples, all using the data frame
“dfPvalsSeq
” created above with
POSSA::sim()
.
To use Haybittle–Peto boundary (all interim local alphas
.001
), you can specify .001
for each local
alpha except for the last, which could be NA
to be adjusted
for the given global alpha (here: .025
), as below.
pow(dfPvalsSeq, alpha_locals = c(0.001, 0.001, NA), alpha_global = 0.025)
#> # POSSA pow() results #
#> N(average-total) = 161.8 (if H0 true) or 140.4 (if H1 true)
#> (p) Type I error: .02500; Power: .88411
#> Local alphas: (1) .00100; (2) .00100; (3) .02431
#> Likelihoods of significance if H0 true: (1) .00111; (2) .00067; (3) .02322
#> Likelihoods of significance if H1 true: (1) .09224; (2) .21596; (3) .57591
In this specific scenario, despite noticeably decreased average
sample in case of H1 (140.4
instead of the fixed
162
), the adverse effect of interim stops on T1ER is so
minimal (see, in case of H0, the tiny ratio significant [false positive]
findings at the first and second looks), that the last look’s alpha
(.02431
) hardly differs from the specified global alpha
(.025
).
You can also check what happens without adjustment, using
adjust = FALSE
.
pow(dfPvalsSeq, alpha_locals = c(0.001, 0.001, 0.025), alpha_global = 0.025, adjust = FALSE)
#> # POSSA pow() results #
#> N(average-total) = 161.8 (if H0 true) or 140.4 (if H1 true)
#> (p) Type I error: .02551; Power: .88618
#> Local alphas: (1) .00100; (2) .00100; (3) .02500
#> Likelihoods of significance if H0 true: (1) .00111; (2) .00067; (3) .02373
#> Likelihoods of significance if H1 true: (1) .09224; (2) .21596; (3) .57798
Which leads to the same conclusion from the reverse perspective: the
T1ER hardly exceeds .25
.
Nonetheless, more substantial sample reduction can be achieved with
somewhat more liberal interim local alphas; besides, normally it makes
more sense to increase the local alphas gradually (because, in brief,
given the greater uncertainty of the initial smaller samples, beginning
with lower local alphas and gradually increasing them provides the most
efficient “information gain” for the same eventual T1ER). For this, one
could base the adjustment on one of the popular boundary methods used
for parametric tests, such as the O’Brien-Fleming bounds. These bounds
for parametric tests can be easily calculated via other packages. They
may not apply to nonparametric tests in exactly the same way, but the
principle of exponentially increasing local alphas remains the same. (To
decide what fits your specific scenario best, you could also check the
outcomes of various versions of the Wang-Tsiatis bounds by changing its
Delta parameter; e.g., in gsDesign::gsDesign()
,
set sfu = 'WT'
for Wang-Tsiatis, and vary its Delta via
sfupar
.) Most importantly in any case, the adjustment of
alphas in POSSA
will ensure that the T1ER is at the
specified level. This will only make a difference for other custom tests
and multiple hypotheses, but not for the present t-test example.
For our example of three equally spaced interim looks,
O’Brien-Fleming bounds can be calculated e.g. via
gsDesign::gsDesign(k = 3, test.type = 1, alpha = 0.05, sfu = 'OF')
,
giving the local alphas of 0.0015
, 0.0181
, and
0.0437
. (The same is returned by
rpact::getDesignGroupSequential(typeOfDesign = "OF", informationRates = c(1/3, 2/3, 1), alpha = 0.05)
.)
To merely calculate the T1ER and power for any specific tests, again
use adjust = FALSE
.
pow(dfPvalsSeq, alpha_locals = c(0.0015, 0.0181, 0.0437), alpha_global = 0.05, adjust = FALSE)
#> # POSSA pow() results #
#> N(average-total) = 160.8 (if H0 true) or 118.7 (if H1 true)
#> (p) Type I error: .04951; Power: .93087
#> Local alphas: (1) .00150; (2) .01810; (3) .04370
#> Likelihoods of significance if H0 true: (1) .00162; (2) .01809; (3) .02980
#> Likelihoods of significance if H1 true: (1) .11531; (2) .57211; (3) .24344
(To verify the power via other software, see
e.g. summary(rpact::getSampleSizeMeans(rpact::getDesignGroupSequential(typeOfDesign = "OF", informationRates = c(1/3, 2/3, 1), alpha = 0.05, beta = 1-0.93087), alternative = 0.5))
.)
Since these local alphas were specifically chosen to have a T1ER of
.05
for the t-test comparison used here, they will hardly
change even with adjust = TRUE
. However, to test the
adjustment function, we can set a different global alpha,
e.g. .01
, so that the adjustment will reduce each local
alpha value by multiplication.
pow(dfPvalsSeq, alpha_locals = c(0.0015, 0.0181, 0.0437), alpha_global = 0.025)
#> # POSSA pow() results #
#> N(average-total) = 161.4 (if H0 true) or 126.9 (if H1 true)
#> (p) Type I error: .02498; Power: .87487
#> Local alphas: (1) .00071; (2) .00862; (3) .02080
#> Likelihoods of significance if H0 true: (1) .00089; (2) .00876; (3) .01533
#> Likelihoods of significance if H1 true: (1) .07629; (2) .49747; (3) .30111
(Cf.
summary(rpact::getSampleSizeMeans(rpact::getDesignGroupSequential(typeOfDesign = "asUser", informationRates = c(1/3, 2/3, 1), userAlphaSpending = c(.00071, .0089, .0244), beta = 1-.87487), alternative = 0.5))
.)
Finally, just for quick demonstration, for the same scenario, here is
a function that adjusts all values via additions (constant
increase/decrease of all local alpha values). Also, here the global
alpha is set to be larger (alpha_global = 0.1
), just so as
to make the local alphas increase too in this case.
pow(
dfPvalsSeq,alpha_locals = c(0.0015, 0.0181, 0.0437),
alpha_global = 0.1,
adjust = function(adj, prev, orig) {
return(orig + adj)
}
)
#> # POSSA pow() results #
#> N(average-total) = 161.4 (if H0 true) or 126.9 (if H1 true)
#> (p) Type I error: .02498; Power: .87487
#> Local alphas: (1) .00071; (2) .00862; (3) .02080
#> Likelihoods of significance if H0 true: (1) .00089; (2) .00876; (3) .01533
#> Likelihoods of significance if H1 true: (1) .07629; (2) .49747; (3) .30111
By default, the interim local alphas will all be zero (i.e., no stopping for significance), and the final local alpha will equal the given global alpha – meaning a simple fixed design (with the maximum specified observation number).
pow(dfPvalsSeq)
#> # POSSA pow() results #
#> N(average-total) = 162.0 (if H0 true) or 162.0 (if H1 true)
#> (p) Type I error: .04860; Power: .93636
#> Local alphas: (1) none; (2) none; (3) .05000
#> Likelihoods of significance if H0 true: (1) 0; (2) 0; (3) .04860
#> Likelihoods of significance if H1 true: (1) 0; (2) 0; (3) .93636
This is to make the setting of futility bounds alone easy
and straightforward. For instance, to stop collection for futility
whenever the p value exceeds .5
(during interim looks), one
can simply add fut_locals = 0.5
.
pow(dfPvalsSeq, fut_locals = 0.5)
#> # POSSA pow() results #
#> N(average-total) = 101.1 (if H0 true) or 158.3 (if H1 true)
#> (p) Type I error: .04516; Power: .91622
#> Local alphas: (1) none; (2) none; (3) .05000
#> Likelihoods of significance if H0 true: (1) 0; (2) 0; (3) .04516
#> Likelihoods of significance if H1 true: (1) 0; (2) 0; (3) .91622
#> Futility bounds: (1) .50000; (2) .50000
#> Likelihoods of exceeding futility alpha if H0 true: (1) .03011; (2) .00176
#> Likelihoods of exceeding futility alpha if H1 true: (1) .03324; (2) .00180
This is equivalent to specifying each futility bound. Since futility
bound is meaningless for the final look, and the present example
(dfPvalsSeq
) contains two interim looks, two values are
required. Hence the
pow(dfPvalsSeq, fut_locals = c(0.5, 0.5))
gives results
equivalent to pow(dfPvalsSeq, fut_locals = 0.5)
(as
above).
Of course, once again, a constant value is not ideal; the futility bound is more effective with values decreasing by each look. The specific choices depends on how much power one is willing to sacrifice in exchange for reduced sample sizes (which, as seen in the outputs, will help primarily in case H0 is true).
pow(dfPvalsSeq, fut_locals = c(0.6, 0.3))
#> # POSSA pow() results #
#> N(average-total) = 100.9 (if H0 true) or 159.3 (if H1 true)
#> (p) Type I error: .04587; Power: .92331
#> Local alphas: (1) none; (2) none; (3) .05000
#> Likelihoods of significance if H0 true: (1) 0; (2) 0; (3) .04587
#> Likelihoods of significance if H1 true: (1) 0; (2) 0; (3) .92331
#> Futility bounds: (1) .60000; (2) .30000
#> Likelihoods of exceeding futility alpha if H0 true: (1) .01549; (2) .01307
#> Likelihoods of exceeding futility alpha if H1 true: (1) .01789; (2) .01336
Importantly, alphas stopping for significance and bounds stopping for futility can be combined in any desired way.
For instance:
pow(
dfPvalsSeq,alpha_locals = c(0.002, 0.018, 0.044),
fut_locals = c(0.6, 0.3)
)
#> # POSSA pow() results #
#> N(average-total) = 99.7 (if H0 true) or 114.3 (if H1 true)
#> (p) Type I error: .05000; Power: .92229
#> Local alphas: (1) .00211; (2) .01897; (3) .04636
#> Likelihoods of significance if H0 true: (1) .00216; (2) .01864; (3) .02920
#> Likelihoods of significance if H1 true: (1) .13907; (2) .55542; (3) .22780
#> Futility bounds: (1) .60000; (2) .30000
#> Likelihoods of exceeding futility alpha if H0 true: (1) .03304; (2) .37542
#> Likelihoods of exceeding futility alpha if H1 true: (1) .01789; (2) .01336
(Note the robust sample size reduction in case of both H0 and H1, with power hardly changed as compared to the fixed design.)
The alpha_locals
functions same as without futility
bounds; e.g., in this last example it was adjusted via multiplication,
but it could also be again given NA
values to be adjusted
via replacements.
To disable, at any given look, stopping either for significance or for futility, just set the local alpha/bound to zero or to one, respectively. For example, the above example can be modified as below to have no stopping for futility at the first look and no stopping for significance at the second look.
pow(
dfPvalsSeq,alpha_locals = c(0.002, 0, 0.044),
fut_locals = c(1, 0.3)
)
#> # POSSA pow() results #
#> N(average-total) = 123.8 (if H0 true) or 145.2 (if H1 true)
#> (p) Type I error: .05000; Power: .93416
#> Local alphas: (1) .00235; (2) none; (3) .05167
#> Likelihoods of significance if H0 true: (1) .00247; (2) 0; (3) .04753
#> Likelihoods of significance if H1 true: (1) .14633; (2) 0; (3) .78782
#> Futility bounds: (1) none; (2) .30000
#> Likelihoods of exceeding futility alpha if H0 true: (1) 0; (2) .01867
#> Likelihoods of exceeding futility alpha if H1 true: (1) 0; (2) .01916
(Of course, once again, this example hardly makes sense in a real case; it serves only as a demonstration here.)
Consider the sample creation given in the beginning:
= function(sampleSize) {
customSample list(
sample1 = rnorm(sampleSize, mean = 0, sd = 10),
sample2_h0 = rnorm(sampleSize, mean = 0, sd = 10),
sample2_h1 = rnorm(sampleSize, mean = 5, sd = 10)
) }
Here, we have two different groups (denoted as “sample1” and
“sample2”), but, since their sample sizes (numbers of observations) are
the same, for convenience the sample size can be passed via a single
parameter (here: sampleSize
). It could also be specified
separately, passing via two parameters. In such a case, the parameter
names must exactly correspond to the sample root names (i.e., the names
without any final 0
/1
that specify “H0” or
“H1”); here, sample1
and sample2_h
.
= function(sample1, sample2_h) {
customSampleTwoParam list(
sample1 = rnorm(sample1, mean = 0, sd = 10),
sample2_h0 = rnorm(sample2_h, mean = 0, sd = 10),
sample2_h1 = rnorm(sample2_h, mean = 5, sd = 10)
) }
Now, the previous sim
and pow
procedures
can be run just the same, and the results will be identical as in the
first example (with single parameter passed).
= sim(fun_obs = customSampleTwoParam,
dfPvalsSeqTwoParam n_obs = c(27, 54, 81),
fun_test = customTest)
pow(dfPvalsSeqTwoParam, alpha_locals = NA)
Here, given the n_obs = c(27, 54, 81)
with a single
vector, this is automatically assigned to each of the two given sample
parameters (sample1
and sample2_h
). These can
also be assigned separately, and the results will once again be
identical.
= sim(
dfPvalsSeqTwoParamSeparate fun_obs = customSampleTwoParam,
n_obs = list(
sample1 = c(27, 54, 81),
sample2_h = c(27, 54, 81)
),fun_test = customTest
)
pow(dfPvalsSeqTwoParamSeparate, alpha_locals = NA)
Importantly, this also allows specifying different observation numbers for each of the two samples.
= sim(
dfPvalsSeqTwoParamSeparate fun_obs = customSampleTwoParam,
n_obs = list(
sample1 = c(17, 44, 71),
sample2_h = c(37, 64, 91)
),fun_test = customTest
)
pow(dfPvalsSeqTwoParamSeparate, alpha_locals = NA)
Now, the outcome is of course different (but would be especially affected in case of unequal variances).
Incidentally: all sequential design examples so far used two interim looks – but there could of course be any other number. For instance, here is a design with a senseless amount of six interim looks (for two differently sized samples), just for the fun of it.
= sim(
dfPvalsManyLooks fun_obs = customSampleTwoParam,
n_obs = list(
sample1 = c(8, 16, 24, 32, 40, 48, 56),
sample2_h = c(10, 20, 30, 40, 50, 60, 70)
),fun_test = customTest
)
pow(dfPvalsManyLooks, alpha_locals = NA)
#> # POSSA pow() results #
#> N(average-total) = 122.4 (if H0 true) or 78.8 (if H1 true)
#> (p) Type I error: .04991; Power: .78164
#> Local alphas: (1) .01381; (2) .01381; (3) .01381; (4) .01381; (5) .01381; (6) .01381; (7) .01381
#> Likelihoods of significance if H0 true: (1) .01478; (2) .01036; (3) .00682; (4) .00600; (5) .00449; (6) .00378; (7) .00369
#> Likelihoods of significance if H1 true: (1) .10896; (2) .14676; (3) .14107; (4) .12364; (5) .10562; (6) .08716; (7) .06844
In a real experiment, you may not know in advance the exact number of observations at the stopping point. For example, you plan (and preregister) to open an initial 30 slots for participation, after which you would perform the first testing (first interim “look”) – however, some participants will probably have to be excluded (e.g. because of failing attention checks), expected to be only a few, but cannot be known exactly in advance.
Assuming that minor variations in power do not matter, you can simply
calculate with the approximate observation numbers (at the given planned
looks) in advance, and, when the actual observation numbers are known,
the calculation is repeated with these latter numbers to ensure that the
T1ER remains as desired. For example, taking into account some necessary
exclusions, the expected observation numbers with two interim looks are
27
(first look), 54
, and 81
.
This may be calculated as before, using O’Brien-Fleming bounds (as
provided by, e.g.,
gsDesign::gsDesign(test.type = 1, alpha = 0.05, sfu = 'OF', timing = c(27, 54, 81)/81)
,
then adjusted by POSSA
).
= sim(fun_obs = customSample,
dfPvals_planned n_obs = c(27, 54, 81),
fun_test = customTest)
pow(dfPvals_planned, alpha_locals = c(0.0015, 0.0181, 0.0437), alpha_global = 0.05)
#> # POSSA pow() results #
#> N(average-total) = 160.8 (if H0 true) or 118.5 (if H1 true)
#> (p) Type I error: .05000; Power: .93164
#> Local alphas: (1) .00152; (2) .01830; (3) .04419
#> Likelihoods of significance if H0 true: (1) .00164; (2) .01831; (3) .03004
#> Likelihoods of significance if H1 true: (1) .11604; (2) .57309; (3) .24251
Now, let’s say that, due to somewhat more exclusions than expected,
the initial sample contains only 24 valid observations. You can rerun
the simulation with the modified first observation number, and run the
power calculation with initial alphas corrected with
gsDesign::gsDesign(test.type = 1, alpha = 0.05, sfu = 'OF', timing = c(24, 54, 81)/81)
.
= sim(fun_obs = customSample,
dfPvals_first n_obs = c(24, 54, 81),
fun_test = customTest)
pow(dfPvals_first, alpha_locals = c(0.0009, 0.0182, 0.0438), alpha_global = 0.05)
#> # POSSA pow() results #
#> N(average-total) = 161.0 (if H0 true) or 120.6 (if H1 true)
#> (p) Type I error: .05000; Power: .93484
#> Local alphas: (1) .00091; (2) .01850; (3) .04453
#> Likelihoods of significance if H0 true: (1) .00087; (2) .01760; (3) .03153
#> Likelihoods of significance if H1 true: (1) .07056; (2) .61742; (3) .24687
Let’s say that the p value was not below the local alpha
(.02295
), so you move open another 30 slots, and once again
end up with more exclusions than expected, resulting in only
49
valid observations. The adjusted calculation is then as
follows.
::gsDesign(test.type = 1, alpha = 0.05, sfu = 'OF', timing = c(24, 49, 81)/81)
gsDesign# gives new initial local alphas
= sim(fun_obs = customSample,
dfPvals_second n_obs = c(24, 49, 81),
fun_test = customTest)
pow(dfPvals_second, alpha_locals = c(0.0009, 0.0145, 0.0447), alpha_global = 0.05)
#> # POSSA pow() results #
#> N(average-total) = 161.0 (if H0 true) or 119.6 (if H1 true)
#> (p) Type I error: .04996; Power: .93153
#> Local alphas: (1) .00090; (2) .01453; (3) .04479
#> Likelihoods of significance if H0 true: (1) .00091; (2) .01360; (3) .03544
#> Likelihoods of significance if H1 true: (1) .07009; (2) .53733; (3) .32411
(And, if needed, a similar modified recalculation could be done for the last look.)
As can be seen, the power differences are very small. Nonetheless, if it is important to keep the same level of power with high precision (e.g., up to 3 fractional digits), you can plan for (and preregister) to conditionally adjust (increase/decrease) the subsequent interim (if any) and final sample sizes to achieve the same power. (Technically, another option is to conditionally adjust the alpha level, i.e., the T1ER, but that’s rather questionable.)
Go to the next
(and the only other important) POSSA
vignette see how to
record descriptives, easily check varying factors, and test multiple
hypotheses.


For almost 30 years south Bronx Veterinary has loved and cared for pets all over the New York area. With
Four clinics in total, South Bronx Veterinary has provided and continues to provide each customer with compassionate, quality care.
We treat your
pet like family.
We care for your pet when they need it most. Our Emergency Veterinary care is low cost and top-notch for all beloved pets in a crisis .
Our doctors are board certified vets who specialize in treating a variety of species and illnesses .
From spraying and neutering to vaccinations. We have provide the best pet care practices to ensure your pet friend live a long healthy life.
Round the clock care for your pets in need
We have some of the best doctors in the whole tri-state area
I worked as a technician before attending vet school. I love amimals, and currently share my home with 2 dogs and a cat.
I am a member in good standing of the American Veterinary Medical Association. I've worked with South Bronx Veterinary for ten years. I love my job
Your pet's teeth are of the upmost importance to me. I have a passion for animal dental care. I received my doctorate of Veterinary Medicine from Purdue University of Veterinary Medicine.
I am an ambitious workaholic, but apart from that, a pretty simple person. I love all animals and share my home with a cat, 2 dogs, a parrot and a cute baby gecko
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Jeff Freshman
PatientsFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Jeff Freshman
PatientsFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Jeff Freshman
PatientsFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Jeff Freshman
PatientsFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Jeff Freshman
PatientsWe are here to help your pet and provide them with the best care possible
250 Bedford Park Blvd W, The Bronx, NY 10468


%= require('html-loader!../common/head.html') %>
{{fail_text}}
By signing up, I hereby agree to the Terms and Privacy Policy
Already have an account? Log in


Hey
I'm Dima from Belarus(PST+10)
Four years I'm working like a professional programmer. I have excellent experience: in PHP, JavaScript, CSS and MySQL. All this skills give me a high level WordPress development. I know well how Wordpress functions, shortcodes, templates, plugins, custom fields & taxonomies, widgets, and other advanced features work. Furthermore I am familiar with responsive coding to build layout for all devices, raise site speed and considering this is used performance features. I set up from scratch more than twenty websites using WordPress. I develop my themes following the WordPress Codex and standards and my layout have SEO optimization and contain semantics elements. To make best UI/UX design I copy the best solution and tricks that competitors have tested in them own websites. In pages I set animation for important sections. In addition i’m providing bugs support, during the month, after project completed.
I serve my customers and they users. My methods of work is self-dependence and thinking ahead of time. I don't need you to give me a massive list of instructions just to follow through and do a job. I independently do the job, and be able understand way to give you maximum result. I have all of the ideas on how to do the project. I can anticipate what the challenges will be and tell you, thus you having to know one with the vision and anticipation.
with applied services.
on number of industries.
on various projects.
from all around the world.
| Full Website Creation | Customization | Installation & Setup |
| Bug Fixes | Backup, Cloning & Migration | Performance & SEO |
| Security | Help/ Consultation | Landing Page |


Who are we?
Panda Relief is a Irish-based international relief and conservation charity established in 2017.
The goal of Panda Relief is to prevent the destruction of panda forests and to reintroduce them across eastern Asia once their numbers have grown.
To learn more, we encourage you to visit our About Us page.
Why pandas?
Pandas are very important to the ecosystem as they help to keep mountain forests healthy by spreading seeds in their droppings.
Pandas also suffer from a loss of habitat due to mass deforestation across eastern Asia. Climate change is also negatively affecting their bamboo supplies.
What's new?
November 2020
Panda populations have seen an unprecedented rise of 1,400 births this year in captivity alone.
July 2020
Protesters from Panda Relief along the Chiang River have prevented further deforestation in wild panda regions in order to prevent further wildlife losses.
March 2020
The International Panda Festival in Beijing has been postponed indefinitely following the outbreak of Covid-19. Stay tuned for more information.
For more, see our Projects page.
How can you help?
Panda Relief is always looking for donations to help save the panda species.
Organised coordinated protests, safeguarding pandas from poachers, and giving care to pandas in both captivity and the wild are our top priorities.
However, these activities are expensive, and we can't do it alone. Not without your help.
If you are interested in volunteering, please contact us with the details from here.
Donations as small as even €1 make the difference between a living species and a dying one.


Emergency services at Girrawheen crash
by Staff WriterEMERGENCY services are at the scene of a serious crash in Girrawheen this afternoon.
There were initial reports of a car crashing into a house but authorities have since been confirmed the incident to be a car striking a tree.
The car has rolled over near a house, around the intersection of Marangaroo Drive and Girrawheen Ave, at about 2.40pm.
A WA Police spokesperson said a person appeared to have received significant injuries.
Marangaroo Drive westbound at Girrawheen Ave have been closed off.
Motorists are advised to avoid the area.


"Nothing...you are normal."
"No, they are not. You are fine."
"Feeling alone is one of the most difficult human experiences, but most of us struggle with it at some point in our lives. So, at least we're not alone in that."
What if I made a mistake?
What if he/she doesn’t like me?
What happens if I don’t get a 4.0?
Am I going to be homeless?
I'm a HORRIBLE person...
STOP OVERTHINKING!!! Take a break and use this ROADMAP to overcome your insecurities.


WITNESS LIST Human Services Committee March 24, 2003-2:30P HB 429 For: Linial, George (Texas Association of Homes and Services for the Aging) Thomason, David (TX Association of Homes and Services for the Aging) Against: Cunningham, Glenn (Self and Texas Trial Lawyers Assoc.) On: Gold, Marc (Texas Dept. of Human Services) HB 546 For: Graves, Tim (Texas Health Care Association) Lauzon, Dy Ann (Kruse Memorial Lutheran Village) Linial, George (Texas Association of Homes and Services for the Aging) Meador, Ann (Methodist Retirement Communities) Taff, Lue (Alzheimers Association Coalition of Texas) Thomason, David (TX Association of Homes and Services for the Aging) Against: Cunningham, Glenn (Self and Texas Trial Lawyers Assoc.) Registering, but not testifying: Against: Ferris, Beth (Texas Advocates for Nursing Home Residents) HB 867 For: Bower, Bruce (TX Chapter, National Academy of Elder Law Attorney) Hawkins, John (Texas Senior Advocacy Coalition) Marshall, Lewis (Self and TSHL) Rachofsky, Marcia (American Jewish Conference) Thomason, David (TX Association of Homes and Services for the Aging) HB 1320 Committee Substitute (Olivo) For: Borel, Dennis (Coalition of Texans with Disabilities) Gladden, Wilhelmina (AARP) Horton, Colleen (TX Center for Disability Studies) Kafka, Bob (ADAPT of Texas) Meinkowsky, John (TX Association of Centers for Independent Living) Murphree, Susan (Advocacy, Inc) Rachofsky, Marcia (TX Senior Advocacy Coalition and TX Legal Services Center) Willis, John (Texas Department on Aging Ombudsman Program) Against: Graves, Tim (Texas Health Care Association) Thomason, David (TX Association of Homes and Services for the Aging) HB 1736 For: Graves, Tim (Texas Health Care Association) Taff, Lue (Alzheimers Association Coalition of Texas) Thomason, David (TX Association of Homes and Services for the Aging) HB 1786 For: Borel, Dennis (Coalition of Texans with Diabilities) Halliger, Mike (Self) Horton, Colleen (TX Center for Disability Studies) Marshall, Susan (Texas Council for Developmental Disabilities) Murphree, Susan (Advocacy, Inc.) Rachofsky, Marcia (Texas Legal Services Center) Against: Conner, Ben (Self) Conner, Gladys (Self) Ferguson, Charles (Texans Supporting State Schools) Garza, J.G. (Self and Texans Supporting State Schools and Parents Assoc. for the Retarded of Texas) Harris, Barbara (Self) Miller, Jim (Self and TSSS) Synder, Ruth (Self) On: Horn, Adelaide (Health and Human Services Commission) McKenny, Ernest (Tx Dept. of Mental Health and Mental Retardation) Waller, Barry (TX Dept. of Mental Health and Mental Retardation) HB 1787 For: Horton, Colleen (TX Center for Disability Studies) Murphree, Susan (Advocacy, Inc.) Rachofsky, Marcia (TX Senior Adocacy Coalition and TX Legal Services Center) Against: Linial, George (Texas Assoc. of Homes and Services for the Aging) Snyder, Ruth (Self) HB 1793 Committee Substitute (Christian) For: Grachek, Marianna (Joint Commission on Accreditation of Health Care Organization - JCAHO) Graves, Tim (Texas Health Care Assoc.) Janowiak, Jo Anne (HCR-Manor Care) Rich, Sid (Texas Association of Residential Care Communities) Thomason, David (TX Association of Homes and Services for the Aging) Against: Bragg, David (AARP) Ferris, Beth (Texas Advocates for Nursing Home Residents) On: Gold, Marc (TX Dept. Human Services) HB 2142 For: Murphree, Susan (Advocacy, Inc.) Rachofsky, Marsha (TX Senior Advocacy Coalition and TX Legal Services Center)


[2] J. Agler, J.E. M\McC Carthy, and M. Stankus, Toral algebraic sets and function theory on polydisks, J. Geom. Anal. 16 (2006), 551-562.


'She leaves him. He kills her.' Thousands in Belgium march to demand end to violence against women
BRUSSELS (Reuters) - Thousands of people protested in Brussels on Sunday against violence targeting women, placing pairs of women’s shoes painted red outside a court to symbolize victims of femicide.
Singing and chanting, protesters carried banners reading “She leaves him, he kills her”, “A dress is not a ‘Yes’” and “My body, my choice, my consent”, a day after thousands also marched in France against domestic violence.
Police said about 10,000 people attended the protest, which took place on the eve of the International Day for the Elimination of Violence Against Women. More events are planned for Monday.
“The red shoes represent women killed by a man,” said Julie Wauters of the Mirabal group, which organized the annual demonstration. “There have been about a hundred of them in the last three years.”
The group said there were no official statistics in Belgium on femicide. One banner on display on Sunday read: “Am I next?”


These are a selection of projects that I have created. Unless otherwise stated, all work on them is done by me.
Only showing {{ filter }} projects. Clear
{{ project.shortDescription }} More


Workspace name formatter
Jenklin slave nodes must each use a unique Perforce workspace.
The format string configures the workspace name by substituting the
following tokens: (at least one token must be used)
- ${node}
- The name given to the slave Jenkins node. (Caution this might
not be unique).
- ${hostname}
- The hostname for the slave Jenkins node.
- ${project}
- The name of the Jenkins build Job. (Caution this is not
unique for slaves).
- ${hash}
- Unique hash code of the Jenkins node.


Impure Blood
By becoming familiar with the chaos, your whole body suffers drastic changes. Among other things, your blood turns impure and protects you from dark magic.
${value}% Slow Resistance
6 11 16 21 26 31 35 40 45 50 55 60
${value}% Vitality Damage Resistance
6 11 16 21 26 31 35 40 45 50 55 60
+${value} Health Regeneration per second
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5
MaxLevel
8
UltimateLevel
12


Cook Islands 'anti-gay' bill needs review
by RNZThe Cook Islands opposition Democratic Party is calling for a special meeting of the Crimes Bill Select Committee.
This is to allow renewed debate of a controversial bill that would criminalise same-sex relations.
In a statement, the Democratic Party said clauses breaching international human rights declarations had gone unchallenged.
A special meeting is needed so the committee can be given legal advice on the Crimes Bill before it makes a recommendation to parliament, it said.
The statement comes after New Zealand LGBTQI advocates threatened to boycott the Cook Islands as a tourism destination over the bill.
The proposed amendment to the Crimes Act 1969 maintains a clause under which Cook Islands men can be jailed for five years for engaging in what's dubbed "indecent acts" with other men. People hosting these acts in their premises face up to ten years jail.
"The full implication of reinstating and expansion of anti-gay law also has to be fully examined," the Democratic Party said in its statement.
"The Democratic Opposition also has concerns regarding breaching the United Nations Declaration of Human Rights to which the Cook Islands is a signatory and the nation's own Constitution."
The select committee had initially struck out the controversial laws, but backed down after seeking advice from the public. LGBTQI advocates said a submission from the country's influential churches, which recommended maintaining criminalisation, was instrumental in the select committee's decision.
Tingika Elikana, the committee's chair, told RNZ Pacific earlier this month the final report to parliament would be submitted in February.


There are numerous ways to denote the same path expression. The trade off is between readability and speed. To maximize readability, Groovy closures are usually the way to go. While using Groovy closures may make a path expression easier to read, unfortunately, its slower than the corresponding Pipe
. However, when no corresponding Pipe
exists, then a closure is the appropriate solution. This section demonstrates the various ways to express the same path and provides some timing statistics on the graph described in Defining a More Complex Property Graph.
g = new TinkerGraph()
g.loadGraphML('data/graph-example-2.xml')
All the examples make use of the following code snippet to determine timing:
t = System.currentTimeMillis(); ...expression... ; System.currentTimeMillis() - t
Gremlin provides field support for grabbing property values of elements (e.g. vertices/edge) by simply providing the property key as a field selector of the instance. However, while this is convenient, it is slow as it requires Java reflection to resolve the field. If performance is the concern, it is always best to use the raw Blueprints API methods even though they tend to be more verbose and less attractive. In general, for REPLing around a graph, use the more concise representation. For production traversals, use native Blueprints API calls and avoid reflection.
~50ms: g.E.sideEffect{it.weight}.iterate()
~5ms: g.E.sideEffect{it.getProperty('weight')}.iterate()
The method call it.getProperty('weight')
is much faster as a direct reference to the appropriate Java method is known. Using it.weight
is a convenient, but slow convention.
~1750ms: g.V.outE.filter{it.weight>0}.inV.outE.filter{it.weight>0}.inV.iterate()
~100ms: g.V.outE.filter{it.getProperty('weight')>0}.inV.outE.filter{it.getProperty('weight')>0}.inV.iterate()
~60ms: g.V.outE.has('weight',T.gt,0).inV.outE.has('weight',T.gt,0).inV.iterate()
Note that this Element.key
is not the same as Pipe.key
. For instance, g.E.weight
is fast as its applied to a Pipe, not to a specific element.
~850ms: g.v(89).out.loop(1){it.loops < 5}.iterate()
~225ms: g.v(89).out.out.out.out.iterate()
The loop
step takes a function that determine whether to loop an object or not (i.e. a “while”). “Unrolling” the loop is more efficient than using loop
. In many situations, knowing how many times to loop is not known and as such, loop
is required. A nifty trick to use is to use code to construct the expression:
gremlin> pipe = g.v(89)._(); null
==>null
gremlin> (1..3).each{
pipe = pipe.out;
}; null
==>null
gremlin> pipe
==>v[81]
==>v[264]
==>v[153]
==>v[160]
==>v[164]
...
gremlin> pipe.toString()
==>[StartPipe, OutPipe, OutPipe, OutPipe]
In many situations, a step looks like an object property from a Java/Groovy perspective. In fact, each step is a method. However, by using the metaprogramming facilities offered by Groovy, its possible to “trick” Groovy into thinking that a property is a method. For example, assume the following pipeline:
v.outE.inV.name
This can be re-written in more Java/Groovy friendly syntax as:
v.outE().inV().name
In the first example, when Groovy realizes that outE
is not a property of v
, it calls the propertyMissing
method of the MetaClass
of Vertex
. Gremlin uses this method to say “oh, outE
is not a property, its a method. Let me resolve that method and return its value.” At which point, Gremlin returns the running Pipeline
with the new OutEdgePipe
appended to it. What makes this possible is dynamic programming in Groovy and the ability to make a property return the evaluation of a method.
Pipe.metaClass.propertyMissing = {final String name ->
if (Gremlin.isStep(name)) {
return delegate."$name"();
}
...
}


Please complete the following form and hit 'Submit Evidence Item'. Fields marked with an asterisk (*) are required.
Entrez Gene ID (e.g., '673' for BRAF)
Description of the type of variant (i.e., V600E, BCR-ABL fusion, Loss-of-function, Exon 12 Mutations). Should be as specific as possible (i.e., specific amino acid change whenever possible).
Origin of variant (germline or somatic)
Disease Ontology ID (DOID) of the specific disease or disease subtype associated with the Evidence Statement (e.g., '1909' for Melanoma)
Description of evidence from published medical literature detailing the association of or lack of association of a Variant with diagnostic, prognostic or predictive value of that Variant in relation to a specific Disease (and Treatment for Predictive Evidence). Data constituting protected health information (PHI) should not be entered. Please familiarize yourself with your jurisdictions definition of PHI before contributing.
PubMed ID for the publication associated with the Evidence Statement (e.g., 23463675)
N/A for Prognostic or Diagnostic evidence. For Predictive evidence, the PubChem ID for relevant drug (e.g., '44462760' for Dabrafenib).
Description of the study performed to produce the Evidence Statement (Levels - A:Validated Association, B:Clinical Evidence, C:Preclinical evidence, D:Inferential association)
Type of clinical outcome associated with the Evidence Statement (Diagnostic, Prognostic, or Predictive)
A indicator of whether the Evidence Statement supports or refutes the clinical significance of an event
Positive or negative association of the Variant with Predictive (Sensitivity/Resistance or NonResponse to drug), Prognostic (Better/Poor Outcome for patient) or Diagnostic (Positive/Negative association with disease) evidence types. If the Variant was not associated with a positive or negative outcome, N/A can be selected (i.e., the presence or absence of mutation had no impact on response to treatment).
Five star rating system indicating individual user’s trust in the source of the Evidence Statement
Optional comment title
Description of this new Evidence Statement. If necessary, provide additional rationale for your submission.


Preparation time
overnight
Cooking time
1 to 2 hours
Serves
Serves 4
Dietary
This simple curry makes an inexpensive main meal served with rice or bread, or could be part of a thali.
Drain the chickpeas and place in a saucepan. Cover with water and bring to the boil. Reduce the heat and simmer for 45 minutes, or until tender. Drain and set aside.
Meanwhile, heat the oil in a heavy bottomed saucepan over a medium heat. When hot, add the onions and cook for a few minutes until starting to soften. Add the ginger, spices, chilli and salt.
Drain the chana dal and add to the pan with 400ml/14fl oz water. Bring to the boil, then simmer for 35–40 minutes, or until the dal is tender. Add a little more water if needed.
Stir in the anardana, garam masala and chickpeas, heat through and adjust the seasoning.
To serve, pick out the whole spices if you wish and garnish with the coriander and pomegranate seeds.


| 1 | package com.twu.biblioteca; | |
| 2 | import java.util.ArrayList; | |
| 3 | public class Biblioteca { | |
| 4 | ||
| 5 | public ArrayList<Book> books = new ArrayList<Book>(); | |
| 6 | public ArrayList<Movie> movies = new ArrayList<Movie>(); | |
| 7 | ||
| 8 | public Biblioteca() { | |
| 9 | books = new ArrayList<Book>() {{ | |
| 10 | add(new Book("Metamorfosis",1960 ,"Franz Kafka", false,"1")); | |
| 11 | add(new Book("It",1987 ,"Stephen King", false, "1")); | |
| 12 | add(new Book("Perfume",1960 ,"Varios", true, "1")); | |
| 13 | }}; | |
| 14 | movies = new ArrayList<Movie>() {{ | |
| 15 | add(new Movie("Fight Club",1999,"David Fincher",8,false)); | |
| 16 | add(new Movie("Forrest Gump",1994 ,"Robert Zemeckis", 8,false)); | |
| 17 | add(new Movie("Star wars",1977 ,"George Lucas", 9, true)); | |
| 18 | }}; | |
| 19 | } | |
| 20 | ||
| 21 | public ArrayList<Book> getListBook(boolean condition) { | |
| 22 | ArrayList<Book> bookList = new ArrayList<Book>(); | |
| 23 | ||
| 24 |
1
1. getListBook : negated conditional → KILLED |
for (Book book : books) { |
| 25 |
1
1. getListBook : negated conditional → KILLED |
if (book.isCheckedOut()==condition) { |
| 26 | bookList.add(book); | |
| 27 | } | |
| 28 | } | |
| 29 |
1
1. getListBook : mutated return of Object value for com/twu/biblioteca/Biblioteca::getListBook to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return bookList; |
| 30 | } | |
| 31 | ||
| 32 | ||
| 33 | public int findBooksByTitle(String bookTitle, ArrayList<Book> books){ | |
| 34 | int i = 0; | |
| 35 | int position = -1; | |
| 36 | ||
| 37 |
1
1. findBooksByTitle : negated conditional → KILLED |
for (Book book : books){ |
| 38 |
1
1. findBooksByTitle : negated conditional → KILLED |
if(book.getTitle().equals(bookTitle)){ |
| 39 | position = i; | |
| 40 | break; | |
| 41 | } | |
| 42 |
1
1. findBooksByTitle : Changed increment from 1 to -1 → KILLED |
i++; |
| 43 | } | |
| 44 | ||
| 45 |
1
1. findBooksByTitle : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return position; |
| 46 | } | |
| 47 | ||
| 48 | public boolean checkoutBook(String bookTitle) { | |
| 49 | ArrayList<Book> availableBooks = getListBook(false); | |
| 50 | int position = findBooksByTitle(bookTitle, availableBooks); | |
| 51 | ||
| 52 |
2
1. checkoutBook : changed conditional boundary → SURVIVED 2. checkoutBook : negated conditional → KILLED |
if (position >= 0) { |
| 53 | ||
| 54 |
1
1. checkoutBook : removed call to com/twu/biblioteca/Book::setCheckedOut → SURVIVED |
availableBooks.get(position).setCheckedOut(true); |
| 55 |
1
1. checkoutBook : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return true; |
| 56 | } | |
| 57 | else { | |
| 58 |
1
1. checkoutBook : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 59 | } | |
| 60 | } | |
| 61 | ||
| 62 | public boolean returnBook(String bookTitle, String libraryNumber) { | |
| 63 | ArrayList<Book> checkOutBooks = getListBook(true); | |
| 64 | ||
| 65 | int position = findBooksByTitle(bookTitle, checkOutBooks); | |
| 66 | ||
| 67 |
2
1. returnBook : changed conditional boundary → KILLED 2. returnBook : negated conditional → KILLED |
if (position >= 0) { |
| 68 | ||
| 69 |
1
1. returnBook : negated conditional → KILLED |
if(libraryNumber.equals(checkOutBooks.get(position).getLibraryNumber())) { |
| 70 |
1
1. returnBook : removed call to com/twu/biblioteca/Book::setCheckedOut → SURVIVED |
checkOutBooks.get(position).setCheckedOut(false); |
| 71 |
1
1. returnBook : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return true; |
| 72 | } | |
| 73 | else | |
| 74 | { | |
| 75 |
1
1. returnBook : replaced return of integer sized value with (x == 0 ? 1 : 0) → NO_COVERAGE |
return false; |
| 76 | } | |
| 77 | } | |
| 78 | else { | |
| 79 |
1
1. returnBook : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 80 | } | |
| 81 | } | |
| 82 | ||
| 83 | public ArrayList<Movie> getListMovies(boolean condition) { | |
| 84 | ArrayList<Movie> movieList = new ArrayList<Movie>(); | |
| 85 | ||
| 86 |
1
1. getListMovies : negated conditional → KILLED |
for (Movie movie : movies) { |
| 87 |
1
1. getListMovies : negated conditional → KILLED |
if (movie.isCheckedOut()==condition) { |
| 88 | movieList.add(movie); | |
| 89 | } | |
| 90 | } | |
| 91 |
1
1. getListMovies : mutated return of Object value for com/twu/biblioteca/Biblioteca::getListMovies to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return movieList; |
| 92 | } | |
| 93 | public int findMoviesByName(String movieTitle, ArrayList<Movie> movies){ | |
| 94 | int i = 0; | |
| 95 | int position = -1; | |
| 96 | ||
| 97 |
1
1. findMoviesByName : negated conditional → KILLED |
for (Movie movie : movies){ |
| 98 |
1
1. findMoviesByName : negated conditional → SURVIVED |
if(movie.getName().equals(movieTitle)){ |
| 99 | position = i; | |
| 100 | break; | |
| 101 | } | |
| 102 |
1
1. findMoviesByName : Changed increment from 1 to -1 → NO_COVERAGE |
i++; |
| 103 | } | |
| 104 | ||
| 105 |
1
1. findMoviesByName : replaced return of integer sized value with (x == 0 ? 1 : 0) → SURVIVED |
return position; |
| 106 | } | |
| 107 | ||
| 108 | public boolean checkoutMovie(String movieName) { | |
| 109 | ArrayList<Movie> availableMovies = getListMovies(false); | |
| 110 | int position = findMoviesByName(movieName, availableMovies); | |
| 111 | ||
| 112 |
2
1. checkoutMovie : changed conditional boundary → KILLED 2. checkoutMovie : negated conditional → KILLED |
if (position >= 0) { |
| 113 |
1
1. checkoutMovie : removed call to com/twu/biblioteca/Movie::setCheckedOut → SURVIVED |
availableMovies.get(position).setCheckedOut(true); |
| 114 |
1
1. checkoutMovie : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return true; |
| 115 | } | |
| 116 | else { | |
| 117 |
1
1. checkoutMovie : replaced return of integer sized value with (x == 0 ? 1 : 0) → NO_COVERAGE |
return false; |
| 118 | } | |
| 119 | } | |
| 120 | ||
| 121 | ||
| 122 | ||
| 123 | } | |
| 124 | ||
| 125 | ||
Mutations | ||
| 24 |
1.1 |
|
| 25 |
1.1 |
|
| 29 |
1.1 |
|
| 37 |
1.1 |
|
| 38 |
1.1 |
|
| 42 |
1.1 |
|
| 45 |
1.1 |
|
| 52 |
1.1 2.2 |
|
| 54 |
1.1 |
|
| 55 |
1.1 |
|
| 58 |
1.1 |
|
| 67 |
1.1 2.2 |
|
| 69 |
1.1 |
|
| 70 |
1.1 |
|
| 71 |
1.1 |
|
| 75 |
1.1 |
|
| 79 |
1.1 |
|
| 86 |
1.1 |
|
| 87 |
1.1 |
|
| 91 |
1.1 |
|
| 97 |
1.1 |
|
| 98 |
1.1 |
|
| 102 |
1.1 |
|
| 105 |
1.1 |
|
| 112 |
1.1 2.2 |
|
| 113 |
1.1 |
|
| 114 |
1.1 |
|
| 117 |
1.1 |


%COMPONENT_NAME% [%COMPONENT_GROUP%]
%COMPONENT_TITLE%
This page is giving short introduction to the component with summary of the change status (green/yellow/red)
%CUSTOM_HTML%
This page is listing community blogs on this component, follow
this link to learn more.
This page is listing all changes which have been done on this component with corresponding test status (green: ok, red: not ok, yellow: untested), follow
this link to learn more.
This page is listing all example applications with this component, follow
this link to learn more.
This page will bring more "about" details (generated)
VISIBLE PROPERTIES
| Name | Description | Group | Default | Visible |
%VISIBLE_PROPERTY_ENTRY%
VISIBLE EVENTS
| Name | Description | Group | Default | Visible |
%EVENT_ENTRY%
FUNCTIONS LIST
| Name | Parameters | Return |
%FUNCTION_ENTRY_LIST%
HIDDEN (TECHNICAL) PROPERTIES
| Name | Description | Group | Default | Visible |
%HIDDEN_PROPERTY_ENTRY%
FUNCTIONS IN DETAIL
THE END