Component Description
Phthalates are a group of chemicals used to make plastics more flexible and
harder to break. They are often called plasticizers. Some phthalates are used as
solvents (dissolving agents) for other materials. They are used in many
products, such as vinyl flooring, adhesives, detergents, lubricating oils,
automotive plastics, plastic clothes (raincoats), and personal-care products
(soaps, shampoos, hair sprays, and nail polishes). Phthalates are used widely in
polyvinyl chloride plastics, which are used to make products such as plastic
packaging film and sheets, garden hoses, inflatable toys, blood-storage
containers, and medical tubing. 1,2-cyclohexane dicarboxylic acid, diisononyl
ester (DINCH) is used as a replacement of some high molecular weight phthalates.
Once phthalates or DINCH enter a person's body, they are converted into
breakdown products (metabolites) that pass out quickly in urine. Biomeasures of
phthalates and phthalates alternatives in humans are necessary to evaluate
potential human health effects from exposure to these chemicals.
Eligible Sample
All examined participants aged 3 to 5 years were eligible and participants
aged 6 years and older from a one-third subsample were eligible.
Description of Laboratory
Methodology
The test principle utilizes high performance liquid
chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS)
for the quantitative detection in urine of the following metabolites: monoethyl
phthalate (MEP), monobutyl phthalate (MBP), mono-isobutyl phthalate (MiBP),
mono(3-carboxypropyl) phthalate (MCPP), mono(2-ethylhexyl) phthalate (MEHP),
monobenzyl phthalate (MBzP), monoisononyl phthalate (MNP),
mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), mono(2-ethyl-5-hydroxyhexyl)
phthalate (MEHHP), mono(2-ethyl-5-carboxypentyl) phthalate (MECPP),
monocarboxyoctyl phthalate (MCOP), monocarboxynonyl phthalate (MCNP), and
cyclohexane-1,2-dicarboxylic acid-mono (hydroxy-isononyl) ester (MHNCH) (Silva,
et al., 2007). Urine samples are processed using enzymatic deconjugation of the
glucuronidated metabolites followed by on-line solid phase extraction (SPE)
coupled with reversed phase HPLC-ESI-MS/MS. Assay precision is improved by
incorporating isotopically labeled internal standards of the phthalate
metabolites and MHNCH. In addition, 4-methyl umbelliferyl glucuronide is used to
monitor deconjugation efficiency. This selective method allows for rapid
detection of metabolites of commonly used phthalates and DINCH in human urine
with limits of detection in the low ng/mL range.
Refer to the Laboratory Method Files section for a detailed description of
the laboratory methods used.
There were no changes to the lab method, lab equipment, or lab site for this
component in the NHANES 2015-2016 cycle.
Laboratory Method Files
Phthalates and Phthalate Alternative Metabolites
(October 2018)
Laboratory Quality
Assurance and Monitoring
Urine specimens are processed, stored, and shipped to the Division of
Laboratory Sciences, National Center for Environmental Health, Centers for
Disease Control and Prevention for analysis.
Detailed specimen collection and processing instructions are discussed in the
NHANES Laboratory
Procedures Manual (LPM). Specimen vials are stored under appropriate frozen
(–20°C) conditions until they are shipped to the National Center for
Environmental Health for testing.
The NHANES quality assurance and quality control (QA/QC) protocols meet the
1988 Clinical Laboratory Improvement Act mandates. Detailed QA/QC instructions
are discussed in the NHANES LPM.
Mobile Examination Centers (MECs)
Laboratory team performance is monitored using several techniques. NCHS and
contract consultants use a structured competency assessment evaluation during
visits to evaluate both the quality of the laboratory work and the
quality-control procedures. Each laboratory staff member is observed for
equipment operation, specimen collection and preparation; testing procedures and
constructive feedback are given to each staff member. Formal retraining sessions
are conducted annually to ensure that required skill levels are maintained.
Analytical Laboratories
NHANES uses several methods to monitor the quality of the analyses performed
by the contract laboratories. In the MEC, these methods include performing blind
split samples collected on “dry run” sessions. In addition, contract
laboratories randomly perform repeat testing on 2% of all specimens.
NCHS developed and distributed a quality control protocol for all CDC and
contract laboratories, which outlined the use of Westgard rules (Westgard, et
al., 1981) when running NHANES specimens. Progress reports containing any
problems encountered during shipping or receipt of specimens, summary statistics
for each control pool, QC graphs, instrument calibration, reagents, and any
special considerations are submitted to NCHS quarterly. The reports are reviewed
by NCHS for trends or shifts in the data. The laboratories are required to
explain any identified areas of concern.
All QC procedures recommended by the manufacturers were followed. Reported
results for all assays meet the Division of Laboratory Sciences’ quality control
and quality assurance performance criteria for accuracy and precision, similar
to the Westgard rules (Caudill, et al., 2008).
Data Processing and Editing
The data were reviewed. Incomplete data or improbable values were sent to the
performing laboratory for confirmation.
Analytic Notes
Refer to the 2015-2016
Laboratory Data Overview for general information on NHANES laboratory
data.
Subsample Weights
Urinary metals were measured in a full sample of
participants ages 3-5 and a one third subsample of persons 6 years and
older. Special sample weights are required to analyze these data properly.
Specific sample weights for this subsample are included in this data file and
should be used when analyzing these data.
Demographic and Other Related Variables
The analysis of NHANES laboratory data must be conducted
using the appropriate survey design and demographic variables. The NHANES 2015-2016
Demographic Data File contains demographic and sample design variables. The
recommended procedure for variance estimation requires use of stratum and PSU
variables (SDMVSTRA and SDMVPSU, respectively) in the demographic data file.
Starting in the 2015-2016 NHANES cycle, the variable URXUCR (urine
creatinine) will not be reported in this file. URXUCR can be found in the data
file titled Albumin & Creatinine - Urine.
This laboratory data file can be linked to the other NHANES data files using
the unique survey participant identifier SEQN.
Starting in the 2015-2016 NHANES cycle, the variable URXUCR (urine
creatinine) will not be reported in this file. URXUCR can be found in the data
file titled “Albumin & Creatinine – Urine”.
Detection Limits
The detection limits were constant for the analytes in the
data set. Two variables are provided for each of these analytes. The variable
named ending in “LC” (ex., URDCNPLC) indicates whether the result was below the
limit of detection: the value “0” means that the result was at or above the
limit of detection, “1” indicates that the result was below the limit of
detection. For analytes with analytic results below the lower limit of detection
(ex., URDCNPLC=1), an imputed fill value was placed in the analyte results
field. This value is the lower limit of detection divided by the square root of
2 (LLOD/sqrt [2]). The other variable prefixed URX (ex., URXCNP) provides the
analytic result for the analyte.
The
lower limit of detection (LLOD, in ng/mL) for the phthalates and plasticizers
metabolites:
| VARIABLE NAME |
SAS LABEL |
LLOD |
| URXCNP |
Mono(carboxyisononyl) phthalate (ng/mL) |
0.2 |
| URXCOP |
Mono(carboxyisoctyl) phthalate (ng/mL) |
0.3 |
| URXECP |
Mono-2-ethyl-5-carboxypentyl phthalate (ng/mL) |
0.4 |
| URXHIBP |
MHIBP phthalate (ng/mL) |
0.4 |
| URXMBP |
Mono-n-butyl phthalate (ng/mL) |
0.4 |
| URXMC1 |
Mono-(3-carboxypropyl) phthalate (ng/mL) |
0.4 |
| URXMCOH |
MCOCH phthalate (ng/mL) |
0.5 |
| URXMEP |
Mono-ethyl phthalate (ng/mL) |
1.2 |
| URXMHBP |
Mono-3-hydroxy-n-butyl phthalate (ng/mL) |
0.4 |
| URXMHH |
Mono-(2-ethyl-5-hydroxyhexyl) phthalate (ng/mL) |
0.4 |
| URXMHNC |
Cyclohexane 1,2-dicarboxylic acid monohydroxy isononyl ester
(ng/mL) |
0.4 |
| URXMHP |
Mono-(2-ethyl)-hexyl phthalate (ng/mL) |
0.8 |
| URXMIB |
Mono-isobutyl phthalate (ng/mL) |
0.8 |
| URXMNP |
Mono-isononyl phthalate (ng/mL) |
0.9 |
| URXMOH |
Mono-(2-ethyl-5-oxohexyl) phthalate (ng/mL) |
0.2 |
| URXMZP |
Mono-benzyl phthalate (ng/mL) |
0.3 |
Please refer to the NHANES Analytic Guidelines and the on-line NHANES Tutorial for
further details on the use of sample weights and other analytic
issues.


This is an Online Bibliography Managing website. Manage all your citations and references on a single platform
You can :
1)Create/Add a new library
2)Add the list of references to your library with all the details
3)Share the libraries with others you add
and many more
This website is designed as a part of continuos assessment of CS615-Internet Solutions Engineering Module
Authors
1)Gaurav Sharma
2)Punith Thota
3)Jie


This web application shows a WebRtcEndpoint connected to itself (loopback). Take a look to the Media Pipeline.
To run this demo follow these steps:


Object
Net::SSH::Multi::Channel encapsulates a collection of Net::SSH::Connection::Channel instances from multiple different connections. It allows for operations to be performed on all contained channels, simultaneously, using an interface mostly identical to Net::SSH::Connection::Channel itself.
You typically obtain a Net::SSH::Multi::Channel instance via Net::SSH::Multi::Session#open_channel or Net::SSH::Multi::Session#exec, though there is nothing stopping you from instantiating one yourself with a handful of Net::SSH::Connection::Channel objects (though they should be associated with connections managed by a Net::SSH::Multi::Session object for consistent behavior).
channel = session.open_channel do |ch| # ... end channel.wait
Instantiate a new Net::SSH::Multi::Channel instance, controlled by the given connection (a Net::SSH::Multi::Session object) and wrapping the given channels (Net::SSH::Connection::Channel instances).
You will typically never call this directly; rather, you’ll get your multi-channel references via Net::SSH::Multi::Session#open_channel and friends.
# File lib/net/ssh/multi/channel.rb, line 38 def initialize(connection, channels) @connection = connection @channels = channels @properties = {} end
Retrieve the property (see properties) with the given key.
host = channel[:host]
# File lib/net/ssh/multi/channel.rb, line 53 def [](key) @properties[key] end
Set the property (see properties) with the given key to the given value.
channel[:visited] = true
# File lib/net/ssh/multi/channel.rb, line 61 def []=(key, value) @properties[key] = value end
Returns true as long as any of the component channels are active.
connection.loop { channel.active? }
# File lib/net/ssh/multi/channel.rb, line 112 def active? channels.any? { |channel| channel.active? } end
Closes all component channels.
# File lib/net/ssh/multi/channel.rb, line 127 def close channels.each { |channel| channel.close } self end
Iterate over each component channel object, yielding each in order to the associated block.
# File lib/net/ssh/multi/channel.rb, line 46 def each @channels.each { |channel| yield channel } end
Tells the remote process for each component channel not to expect any further data from this end of the channel.
# File lib/net/ssh/multi/channel.rb, line 134 def eof! channels.each { |channel| channel.eof! } self end
Perform an exec command on all component channels. The block, if given, is passed to each component channel, so it will (potentially) be invoked once for every channel in the collection. The block will receive two parameters: the specific channel object being operated on, and a boolean indicating whether the exec succeeded or not.
channel.exec "ls -l" do |ch, success| # ... end
See the documentation in Net::SSH for Net::SSH::Connection::Channel#exec for more information on how to work with the callback.
# File lib/net/ssh/multi/channel.rb, line 77 def exec(command, &block) channels.each { |channel| channel.exec(command, &block) } self end
Registers a callback on all component channels, to be invoked when the remote server terminates the channel. The callback will be invoked with one argument: the specific channel object being closed.
channel.on_close do |ch| # ... end
# File lib/net/ssh/multi/channel.rb, line 185 def on_close(&block) channels.each { |channel| channel.on_close(&block) } self end
Registers a callback on all component channels, to be invoked when the remote process emits data (usually on its stdout stream). The block will be invoked with two arguments: the specific channel object, and the data that was received.
channel.on_data do |ch, data| puts "got data: #{data}" end
# File lib/net/ssh/multi/channel.rb, line 147 def on_data(&block) channels.each { |channel| channel.on_data(&block) } self end
Registers a callback on all component channels, to be invoked when the remote server has no further data to send. The callback will be invoked with one argument: the specific channel object being marked EOF.
channel.on_eof do |ch| # ... end
# File lib/net/ssh/multi/channel.rb, line 197 def on_eof(&block) channels.each { |channel| channel.on_eof(&block) } self end
Registers a callback on all component channels, to be invoked when the remote process emits “extended” data (typically on its stderr stream). The block will be invoked with three arguments: the specific channel object, an integer describing the data type (usually a 1 for stderr) and the data that was received.
channel.on_extended_data do |ch, type, data| puts "got extended data: #{data}" end
# File lib/net/ssh/multi/channel.rb, line 161 def on_extended_data(&block) channels.each { |channel| channel.on_extended_data(&block) } self end
Registers a callback on all component channels, to be invoked when the remote server is unable to open the channel. The callback will be invoked with three arguments: the channel object that couldn’t be opened, a description of the error (as a string), and an integer code representing the error.
channel.on_open_failed do |ch, description, code| # ... end
# File lib/net/ssh/multi/channel.rb, line 211 def on_open_failed(&block) channels.each { |channel| channel.on_open_failed(&block) } self end
Registers a callback on all component channels, to be invoked during the idle portion of the connection event loop. The callback will be invoked with one argument: the specific channel object being processed.
channel.on_process do |ch| # ... end
# File lib/net/ssh/multi/channel.rb, line 173 def on_process(&block) channels.each { |channel| channel.on_process(&block) } self end
Registers a callback on all component channels, to be invoked when the remote server sends a channel request of the given type. The callback will be invoked with two arguments: the specific channel object receiving the request, and a Net::SSH::Buffer instance containing the request-specific data.
channel.on_request("exit-status") do |ch, data| puts "exited with #{data.read_long}" end
# File lib/net/ssh/multi/channel.rb, line 225 def on_request(type, &block) channels.each { |channel| channel.on_request(type, &block) } self end
Perform a request_pty command on all component channels. The block, if given, is passed to each component channel, so it will (potentially) be invoked once for every channel in the collection. The block will receive two parameters: the specific channel object being operated on, and a boolean indicating whether the pty request succeeded or not.
channel.request_pty do |ch, success| # ... end
See the documentation in Net::SSH for Net::SSH::Connection::Channel#request_pty for more information on how to work with the callback.
# File lib/net/ssh/multi/channel.rb, line 95 def request_pty(opts={}, &block) channels.each { |channel| channel.request_pty(opts, &block) } self end
Send the given data to each component channel. It will be sent to the remote process, typically being received on the process’ stdin stream.
channel.send_data "password\n"
# File lib/net/ssh/multi/channel.rb, line 104 def send_data(data) channels.each { |channel| channel.send_data(data) } self end
Runs the connection’s event loop until the channel is no longer active (see active?).
channel.exec "something" channel.wait
# File lib/net/ssh/multi/channel.rb, line 121 def wait connection.loop { active? } self end
Generated with the Darkfish Rdoc Generator 2.


Calendar Manager
|
Displays the calendar store manager window. |
Subscribed Calendars
|
Displays the subscribed calendar view window. |
|
|
Create
|
Creates a new calendar. |
Rename
|
Renames selected calendars. |
Refresh List
|
Refreshes the list of calendars in the selected accounts. |
|
|
Delete Calendar
|
Deletes the selected calendars. |
|
|
Add Web Calendar
|
Adds a web calendar via a URL. |
Refresh Web Calendar
|
Refreshes the content of the selected web calendars. |
Upload Web Calendar
|
Uploads a web calendar to its server. |
|
|
New Event
|
Creates a new event. |
New To Do
|
Creates a new to do. |
Edit Item
|
Edits the selected event or to do. |
Duplicate Item
|
Duplicates the selected event or to do. |
Delete Item
|
Deletes the selected event or to do. |
|
|
Goto Today
|
Switch the current calendar view to show a date range that includes the current day. |
Goto Date
|
Allows a date to be chosen and then switches the current calendar view to show a date range that includes the chosen day. |


South African Airways cancels flights ahead of strike
JOHANNESBURG: South African Airways (SAA) said on Wednesday (Nov 13) it was cancelling all its flights as thousands of workers vowed to press ahead with an indefinite strike the following day, after the troubled national carrier announced a major retrenchment plan.
Around 3,000 workers, including cabin crew, check-in, ticket sales, technical and ground staff, are expected to take part in the open-ended strike starting on Friday, their unions said.
The looming shutdown forced SAA to announce in a late-night statement on Wednesday that it "has cancelled nearly all its domestic, regional and international flights scheduled for Friday, Nov 15 and Saturday, Nov 16".
"The airline's key objective is to minimise the impact of disruptions for its customers," it said.
Unions earlier Wednesday vowed their members would forge ahead with the strike, which the state-owned airline warned could collapse the embattled carrier.
"We are embarking on the mother of all strikes," Zazi Nsibanyoni-Mugambi, president of the South African Cabin Crew Association (SACCA) told a news conference in Johannesburg.
"We are grounding that airline on Friday," said Irvin Jim, general secretary of the National Union of Metalworkers of South Africa (NUMSA).
The unions are pressing for a three-year guarantee of job security and an eight percent across-the-board wage hike.
'MOTHER OF ALL STRIKES'
Pilots - who are not taking part in the strike - have accepted a 5.9 per cent increase, they said.
The airline had announced on Monday a restructuring process that could affect 944 employees and "lead to job losses".
The airline, which employs more than 5,000 workers, is one of the biggest in Africa, with a fleet of more than 50 aircraft providing dozens of domestic, regional and European flights each day.
But the company is deep in debt, despite several government bailouts, and has not recorded a profit since 2011.
The unions blamed the SAA board and executive management for the airline's crisis.
"They have deliberately destroyed what used to be one of the world's best airlines, because of maladministration, rampant looting and corruption," they said in a statement.
SAA Chief Executive Officer Zuks Ramasia warned that the strike would "exacerbate rather than ameliorate our problem" and urged the unions to make affordable demands.
"The unions and all employees should be mindful of the current financial constraints the company is facing," she said in a statement.
She said the unions were aware that the airline's financial woes were "caused by a number of factors, including a severely distressed global airline industry."
This, she argued, had resulted in "numerous airlines retrenching staff, embarking on cost-reduction programmes, implementing wage freezes, reducing operations, or even closing down."
The airline has been surviving off government bailouts. Finance Minister Tito Mboweni announced in February that the government would reimburse the company's 9.2-billion-rand (US$620-million) debt over the next three years.
South Africa is struggling to get its state-owned companies back on track after nine years of corruption and mismanagement under former president Jacob Zuma.
Analyst Daniel Silke warned in a tweet that the planned strike "may kill an airline already on its knees affecting the jobs of thousands more."


Glazed Doughnuts
These original glazed donuts are light and chewy and a good way to get anyone out of bed in the morning.
Add to basket
These original glazed donuts are light and chewy and a good way to get anyone out of bed in the morning.
So in come donuts. Because no one will shame you for eating donuts rolled in gobs of cinnamon sugar!
 Cakey fried Chocolate Old Fashioned Doughnuts with sour cream, a thick luscious glaze, and no yeast!
These fluffy doughnuts are fried to golden perfection before getting tossed in salt-and-pepper sugar and filled with roasted strawberry jam.


Whats new in the latest release of Project Sakura? Check out..
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august security patch, oplauncher and much more..
Everyone likes thicc *ahem*, Yes the thicc gesture handle and a new shining tiny security patch..
our updates are getting hotter just like the weather, so we should cool down now..
Want to make a official sakura build for your device? check this post..
See whats new and good this may..
XSLTEL OU is a european company that have decided to sponsor Project Sakura with servers..


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Nite Mode takes your pdf files, and simply turns the lights off.
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| teardown {testthat} | R Documentation |
We no longer recommend using setup()
and teardown()
; instead
we think it's better practice to use a test fixture as described in
vignette("test-fixtures")
.
Code in a setup()
block is run immediately in a clean environment.
Code in a teardown()
block is run upon completion of a test file,
even if it exits with an error. Multiple calls to teardown()
will be
executed in the order they were created.
teardown(code, env = parent.frame()) setup(code, env = parent.frame())
code |
Code to evaluate |
env |
Environment in which code will be evaluated. For expert use only. |
## Not run: # Old approach tmp <- tempfile() setup(writeLines("some test data", tmp)) teardown(unlink(tmp)) ## End(Not run) # Now recommended: local_test_data <- function(env = parent.frame()) { tmp <- tempfile() writeLines("some test data", tmp) withr::defer(unlink(tmp), env) tmp } # Then call local_test_data() in your tests


Event Horizon (1997)
Starring Laurence Fishburne, Sam Neill, Joely Richardson, Kathleen Quinlan.
Review by Laurence Mixson<EMAIL_ADDRESS>***1/2 out of ****
Event Horizon really fools you for a while. For about the first third of the movie, I thought it was going to be your average, run-of-the-mill late-summer release, basically a straight-to-video release put into the theaters. I was wrong. The movie sets up the plot rather quickly: seven years ago(it takes place in the year 2040-something)a spaceship named the Event Horizon, designed to explore the vast caverns of space, dissapeared. Now, in the present(although, technically, it's the future, but let's not get into that), the ship suddenly reappers with no apparent crew on board. A rescue ship crew, headed up by Captain Miller(Fishburne)to explore the event horizon. Accompanying he and his several man crew is Dr. Weir(Neill), who designed the Event Horizon. He lets them in on a secret rather early into the picture: the Event Horizon was not actually a space exploration ship, but a top-secret time-travel ship, designed to create a black hole and use it to travel from one end of the universe to the other. Miller's crew boards the Event Horizon and discovers that none of the Event's crew is present, all though lots of neat gore and blood splatter is everywhere. Eventually, they begin having hallucinations, and things start to get more violent, and Weir is cracking up... Event Horizon owes more than a little to both The Shining and Hellraiser which it gracefully rips off; and even ups them, in some cases. The special effects, while not cutting edge, are very good; they're the best when showing off graphic displays of gore. Like a man being cut open, having his insides dumped out, and hung from hooks on the ceiling. Did I mention this is a well-rounded, family movie as well? All though it's probably too late now to see it at the theaters, Event Horizon is a definite must-see video. Which, I suppose, makes it a good direct-to-video picture. Except it's better than that. Except that's not the point. Or is it? Aww, forget it...
The review above was posted to the
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Letter to BS: Judicial system needs procedural overhaul, attitudinal change
It is high time the higher judiciary admits that the main cause is not simply the vacancies of judges
by Business StandardThis refers to “CJI: Justice loses its character when it becomes revenge” (December 8) which quotes the Chief Justice of India S A Bobde as saying that there cannot be instant justice. Justice Bobde made that comment in the backdrop of the rape case in Telengana and subsequent killing of the alleged rapists. He is absolutely right. The CJI also appropriately mentioned long delays in dispensing justice. What he did not mention is how bails are granted in even cases of violent rape and gang rape.
The example of the case in Unnao in UP is a case in view right now. I am of the view that the Supreme Court can give direction, may be administratively but surely judicially that where there is violence mixed with rape and in cases of gang rape, bail should not be given. And the general impression of people is that once the criminal case goes to courts, the case will drag on and rich people with a lot of cash can appoint top lawyers to delay justice and can even derail justice. Quick disposal of such cases must be achieved.
Generalising the issue of inordinate delay in courts, it is high time the higher judiciary admits that the main cause is not simply the vacancies of judges. I feel that increasing the number of judges is only half the solution. What is needed is a thorough procedural overhaul and attitudinal change in the judicial system. The conclusion is that the massive backlog in courts can be reduced not by a quantitative approach but a qualitative change in the approach on the part of all stakeholders.
Sukumar Mukhopadhyay
Letters can be mailed, faxed or e-mailed to:
The Editor, Business Standard
Nehru House, 4 Bahadur Shah Zafar Marg
New Delhi 110 002
Fax:<PHONE_NUMBER>1 E-mail<EMAIL_ADDRESS>All letters must have a postal address and telephone number


Various filters
Download Windows executable (.zip) |
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Fast Harris-SIFT features / kd-tree matching / RANSACImplementation of: P. Azad, T. Asfour, R. Dillmann, "Combining Harris Interest Points and the SIFT Descriptor for Fast Scale-Invariant Object Recognition", IROS 2009.
Download Windows executable (.zip) |
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Line detection with Hough transform
Download Windows executable (.zip) |
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Circle detection with Hough transform
Download Windows executable (.zip) |
|
KLT feature tracking (Kanade-Lucas-Tomasi)
Download Windows executable (.zip) |
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Stereo depth map
Download Windows executable (.zip) |
|
Binarization
Download Windows executable (.zip) |
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HSV color segmentation
Download Windows executable (.zip) |
|
Particle filter (simple 2D tracking example)
Download Windows executable (.zip) |
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Undistortion / rectification
Download Windows executable (.zip) |
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Canny Edge detector
Download Windows executable (.zip) |
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Harris corner detector
Download Windows executable (.zip) |
|
Video capture
Download Windows executable (.zip) (using VfW under Windows) |


Sweden sells off Australian, Canadian bonds over carbon footprint
by BNNSweden’s central bank has stated it had given up bonds in the mining regions of Australia and Canada as Riksbank said the greenhouse gas emissions of both countries were large.
British news portal The Guardian reports Riksbank deputy governor, Martin Floden, said earlier this wek the bank would no longer invest in assets from issuers with a large climate footprint, even if the yields were high.
«Australia and Canada are countries that are not known for good climate work. Greenhouse gas emissions per capita are among the highest in the world,»
he said in a speech at Orebro University in Sweden.
Alberta’s government on Wednesday insisted that the province «has the highest environmental standards in the world,» and said the energy industry is continually improving its environmental footprint.
Floden, who did not give details, stated Sweden had invested around 8% of its foreign exchange reserves in various kinds of Australian and Canadian bonds, The Guardian reports.


The PDredesign Data & Tech Inventory is a free tool for school districts to get the big picture of their data and technology systems that support professional learning. Central office staff from across departments collaboratively add information about their current data systems and technology products to better understand how these systems impact different areas of teacher professional growth. The Inventory can be used on its own, but is most effective when completed after a Readiness Assessment. To take your Inventory to the next level, follow it with a Data & Tech Analysis to complete a Data & Tech Landscape for your district.
The Inventory may be used to:
Setup
Learn about the Data & Tech Landscape process and get started. As the facilitator, you’ll add tool learning questions and lead your team through the process.
Collect
Work collaboratively to build an inventory of existing products and data systems that impact teaching and professional learning. Your team may include a PD lead, tech lead, finance lead, and others.


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LangLR1 generates a parsing state machine for an LR1 language.
LangLR1.exe <lang-name> <file.lang> <CCore-Root>
The first command line argument is the language name.
The second is the file with the language definition.
The third is the path to the CCore root directory. If this path is relative, it starts from the subdirectory test of the working directory.
Program generates two files in the working directory: Result.txt and State.ddl. It also generates the test directory test. You can build and run a test project there.
tools/LangLR1-tools/AMP is a sample directory with AMP language.
Consider a non-empty finite set Atom. Elements of this set are called atoms. Finite sequences of atoms are called words. They comprise another (infinite) set — Atom*. From the abstract algebra perspective, Atom* is a free monoid, generated by the Atom. This set has a natural binary operation — word concatenation. Subsets of Atom* are often called languages. There is a class of languages, known as context-free languages. These languages are defined by context-free grammars.
Consider an example — AMP language. This language is defined by the following grammar:
/* AMP.lang */ A : { M : A1 A + M : A2 } M { P : M1 P * M : M2 } P { x : P1 ( A ) : P2 }
This grammar introduces three non-terminals: A, M and P. Each non-terminal has a set of associated production rules. For example, A has two rules: A1 and A2. Each rule is a sequence of atoms and non-terminals. For example, the rule A2 is a length 3 sequence: ( A , + , M ). The atom set is { + , * , x , ( , ) } and it is defined implicitly by the grammar.
Non-terminal and rule names must be C-names. Atom name may be any string, to include one of the character { } : ` prefix it with ` character. You may not use " character.
Non-terminal A is marked with : character. The language of this non-terminal is included in the resulting language. You may mark several non-terminals, but usually only a single non-terminal is marked as resulting.
Grammar produces a language per each non-terminal by the following inductive process:
{A}0 = ∅ {M}0 = ∅ {P}0 = ∅
{A}n+1 = union of { {M}n {A}n + {M}n } {M}n+1 = union of { {P}n {P}n * {M}n } {P}n+1 = union of { x ( {A}n ) }
{A} = union of all {A}n {M} = union of all {M}n {P} = union of all {P}n
The following relations are satisfied:
{A} = union of { {M} {A} + {M} } {M} = union of { {P} {P} * {M} } {P} = union of { x ( {A} ) }
Informally speaking, decoding a language according a grammar is a reconstruction of the way how the given word is produced by grammar rules. If this way is unique, the grammar is called uniquely decoded. There is a convenient formal definition of a "uniquely decoded grammar".
Lets extend the initial grammar by the following way:
A { M [A1] : A1 A + M [A2] : A2 } M { P [M1] : M1 P * M [M2] : M2 } P { x [P1] : P1 ( A ) [P2] : P2 }
I.e. we append each rule by a special atom — one atom per each rule. Lets denote new languages as {A[]} etc... Then if we take a word from the {A[]} and cleanup all extended atoms, then we receive a word from the {A}. So we get a map clean: {A[]} → {A} and other such maps. They are surjective and grammar is uniquely decoded if they are bijective. I.e. if we know the word from the {A}, we can uniquely up this word to the word from the {A[]}. And doing so we receive the production formula in the postfix form:
( x [P1] * x [P1] [M1] [M2] [A1] ) [P2] [M1] [A1] + x [P1] [M1] [A2] ( x * x ) + x
If the grammar is uniquely decoded, it may be used to evaluate words. For evaluation some entities must be given. First, a set of types, one per each non-terminal and per each atom. Second, a set of functions, one per each rule. Atom may carry a value of the correspondent type.
type typeA; type typeM; type typeP; type type_add; // + type type_mul; // * type type_obr; // ( type type_cbr; // ) type type_x; // x typeA funcA1(typeM); typeA funcA2(typeA,type_add,typeM); typeM funcM1(typeP); typeM funcM2(typeP,type_mul,typeM); typeP funcP1(type_x); typeP funcP2(type_obr,typeA,type_cbr);
The evaluation process goes using a polymorphic stack. Each time an extended atom puts on the stack, the corespondent function is applied at the top of the stack (arguments are removed from the stack) and the result is put to the stack.
( x [P1] ( funcP1(x) * x [P1] ( funcP1(x) * funcP1(x) [M1] ( funcP1(x) * funcM1(funcP1(x)) [M2] ( funcM2(funcP1(x),*,funcM1(funcP1(x))) [A1] ( funcA1(funcM2(funcP1(x),*,funcM1(funcP1(x)))) ) [P2] funcP2((,funcA1(funcM2(funcP1(x),*,funcM1(funcP1(x)))),)) [M1] ....
LR1 decoder is an efficient language decoder. It is working with the evaluation stack. It looks ahead on one atom and makes a choice: put this atom to the stack or apply a rule.
( funcP1(x) * ← x // stack shift operation
( funcP1(x) * x [P1] )
To make a decision decoder uses a state machine. States of this state machines are stores in the stack:
( funcP1(x) * ← x s0 s1 s2 stop
Empty stack has the initial state. The next stack entry generates the next state according the state machine transition rule:
s1 = transition(s0,type_obr) ; s2 = transition(s1,typeP) ; ....
A decision is made based on the top state and the next atom. LR1 is a class of such languages, that only a single decision can be done.
LangLR1 generates two main output files: State.ddl and Result.txt. State.ddl is a DDL description of the LR1 decoder for a further processing. It looks like:
/* State.ddl */ RIndex RIndexLim = 7 ; TIndex TIndexLim = 6 ; NIndex NIndexLim = 3 ; NTIndex NTIndexLim = 9 ; // T , N StateIndex StateIndexLim = 22 ; FinalIndex FinalIndexLim = 15 ; Final[15] FinalTable= { { 0 , { { 0 , RuleTable+0 }, { 4 , RuleTable+0 } } }, .... }; State[22] StateTable= { { 0 , { { 1 , StateTable+1 }, { 5 , StateTable+2 }, { 6 , StateTable+3 }, { 7 , StateTable+4 }, { 8 , StateTable+16 } }, FinalTable+7 }, .... }; text[6] TNames= { "(End)", "T(\"(\")", "T(\")\")", "T(\"*\")", "T(\"+\")", "T(\"x\")" }; Rule[7] RuleTable= { /* shift, special */ { 0 , "<-" }, { 1 , "A1" , 0 , { 7 } }, { 2 , "A2" , 0 , { 6, 4, 7 } }, { 3 , "M1" , 1 , { 8 } }, { 4 , "M2" , 1 , { 8, 3, 7 } }, { 5 , "P1" , 2 , { 5 } }, { 6 , "P2" , 2 , { 1, 6, 2 } } }; NonTerminal[3] NonTerminalTable= { { 0 , "A" , { RuleTable+1, RuleTable+2 } }, { 1 , "M" , { RuleTable+3, RuleTable+4 } }, { 2 , "P" , { RuleTable+5, RuleTable+6 } } };
The following type definitions are used (it can be found in CCORE/tools/LangLR1-tools/StateTypes.ddl):
/* StateTypes.ddl */ type RIndex = uint ; type TIndex = uint ; type NIndex = uint ; type NTIndex = uint ; type StateIndex = uint ; type FinalIndex = uint ; struct Final { FinalIndex final; struct Action { TIndex t; Rule *rule; } [] actions; }; struct State { StateIndex state; struct Transition { NTIndex ntt; State *state; } [] transitions; Final *final; }; struct Rule { RIndex rule; text name; NIndex result; NTIndex[] str; }; struct NonTerminal { NIndex nt; text name; Rule * [] rules; };
The type RIndex is used to index rules, TIndex — to index atoms (aka terminals), NIndex — to index non-terminals, NTIndex — to index non-terminals and atoms as the one set, where atoms go first, StateIndex — to index states, FinalIndex — to index finals.
RIndexLim is the number of rules, the one special rule (shift) is added with index 0. This rule denotes the stack shift operation.
TIndexLim is the number of atoms, the one special atom is added with index 0. This atom denotes the end of input.
NIndexLim is the number of non-terminals.
NTIndexLim = TIndexLim + NIndexLim
StateIndexLim is the number of states. Initial state has index 0.
FinalIndexLim is the number of "finals". Each state has a correspondent final, which maps atoms to rules.
There are four types and five tables.
Atoms are described by TNames table. It is an atom name table. The name is build like T("name"), where name is the atom name from the lang file: T("("), T(")") etc...
NonTerminaTable describes non-terminals. It has a NonTerminal structure per each non-terminal.
struct NonTerminal { NIndex nt; text name; Rule * [] rules; };
nt is the non-terminal index.
name is the non-terminal name.
rules is the rule table for this non-terminal.
RuleTable describes rules. It has a Rule structure per each rule.
struct Rule { RIndex rule; text name; NIndex result; NTIndex[] str; };
rule is the rule index.
name is the rule name.
result is the result of the rule — the non-terminal index.
str is the rule itself, it is a sequence of atoms and non-terminals given by indexes.
The next table is the StateTable. It has a State structure per each state.
struct State { StateIndex state; struct Transition { NTIndex ntt; State *state; } [] transitions; Final *final; };
state is the state index.
transitions is the transition table. It consists of pairs: (NTIndex index, next state). If some index is missed here, the correspondent transition does not exist and will not appear during a decoding process.
finals points to the final.
The last table is the FinalTable. It is a list of all finals.
struct Final { FinalIndex final; struct Action { TIndex t; Rule *rule; } [] actions; };
final is the final index.
actions is a list of pair: (atom index, rule). It is used to make a decision. If some index is missed here, the correspondent atom cannot appear in a correct input sequence.
The file Result.txt is a more human readable description of the state machine. Each state here has a path, leading to this state. If the language is not LR1, you will find non LR1 states at the end of the file. It helps figure out what is wrong in the language definition and correct it.
/* Result.txt */ /* Finals */ Final 0 { (End) -> ( <- ) T("+") -> ( <- ) } Final 1 { (End) -> ( A1 ) T("+") -> ( A1 ) } Final 2 { (End) -> ( A2 ) T("+") -> ( A2 ) } Final 3 { (End) -> ( M1 ) T("*") -> ( <- ) T("+") -> ( M1 ) } Final 4 { (End) -> ( M2 ) T("+") -> ( M2 ) } Final 5 { (End) -> ( P1 ) T("*") -> ( P1 ) T("+") -> ( P1 ) } Final 6 { (End) -> ( P2 ) T("*") -> ( P2 ) T("+") -> ( P2 ) } Final 7 { T("(") -> ( <- ) T("x") -> ( <- ) } Final 8 { T(")") -> ( <- ) T("+") -> ( <- ) } Final 9 { T(")") -> ( A1 ) T("+") -> ( A1 ) } Final 10 { T(")") -> ( A2 ) T("+") -> ( A2 ) } Final 11 { T(")") -> ( M1 ) T("*") -> ( <- ) T("+") -> ( M1 ) } Final 12 { T(")") -> ( M2 ) T("+") -> ( M2 ) } Final 13 { T(")") -> ( P1 ) T("*") -> ( P1 ) T("+") -> ( P1 ) } Final 14 { T(")") -> ( P2 ) T("*") -> ( P2 ) T("+") -> ( P2 ) } /* Compressed States */ State 0 : ( ) { T("(") -> State 1 T("x") -> State 2 A -> State 3 M -> State 4 P -> State 16 --------------------- T("(") -> ( <- ) T("x") -> ( <- ) } State 1 : ( T("(") ) { T("(") -> State 5 T("x") -> State 6 A -> State 7 M -> State 8 P -> State 19 --------------------- T("(") -> ( <- ) T("x") -> ( <- ) } State 2 : ( T("x") ) { --------------------- (End) -> ( P1 ) T("*") -> ( P1 ) T("+") -> ( P1 ) } State 3 : ( A ) { T("+") -> State 9 --------------------- (End) -> ( <- ) T("+") -> ( <- ) } State 4 : ( M ) { --------------------- (End) -> ( A1 ) T("+") -> ( A1 ) } State 5 : ( T("(") , T("(") ) { T("(") -> State 5 T("x") -> State 6 A -> State 10 M -> State 8 P -> State 19 --------------------- T("(") -> ( <- ) T("x") -> ( <- ) } State 6 : ( T("(") , T("x") ) { --------------------- T(")") -> ( P1 ) T("*") -> ( P1 ) T("+") -> ( P1 ) } State 7 : ( T("(") , A ) { T(")") -> State 11 T("+") -> State 12 --------------------- T(")") -> ( <- ) T("+") -> ( <- ) } State 8 : ( T("(") , M ) { --------------------- T(")") -> ( A1 ) T("+") -> ( A1 ) } State 9 : ( A , T("+") ) { T("(") -> State 1 T("x") -> State 2 M -> State 14 P -> State 16 --------------------- T("(") -> ( <- ) T("x") -> ( <- ) } State 10 : ( T("(") , T("(") , A ) { T(")") -> State 17 T("+") -> State 12 --------------------- T(")") -> ( <- ) T("+") -> ( <- ) } State 11 : ( T("(") , A , T(")") ) { --------------------- (End) -> ( P2 ) T("*") -> ( P2 ) T("+") -> ( P2 ) } State 12 : ( T("(") , A , T("+") ) { T("(") -> State 5 T("x") -> State 6 M -> State 18 P -> State 19 --------------------- T("(") -> ( <- ) T("x") -> ( <- ) } State 13 : ( T("(") , P , T("*") ) { T("(") -> State 5 T("x") -> State 6 M -> State 20 P -> State 19 --------------------- T("(") -> ( <- ) T("x") -> ( <- ) } State 14 : ( A , T("+") , M ) { --------------------- (End) -> ( A2 ) T("+") -> ( A2 ) } State 15 : ( P , T("*") , M ) { --------------------- (End) -> ( M2 ) T("+") -> ( M2 ) } State 16 : ( P , T("*") , P ) { T("*") -> State 21 --------------------- (End) -> ( M1 ) T("*") -> ( <- ) T("+") -> ( M1 ) } State 17 : ( T("(") , T("(") , A , T(")") ) { --------------------- T(")") -> ( P2 ) T("*") -> ( P2 ) T("+") -> ( P2 ) } State 18 : ( T("(") , A , T("+") , M ) { --------------------- T(")") -> ( A2 ) T("+") -> ( A2 ) } State 19 : ( T("(") , A , T("+") , P ) { T("*") -> State 13 --------------------- T(")") -> ( M1 ) T("*") -> ( <- ) T("+") -> ( M1 ) } State 20 : ( T("(") , P , T("*") , M ) { --------------------- T(")") -> ( M2 ) T("+") -> ( M2 ) } State 21 : ( P , T("*") , P , T("*") ) { T("(") -> State 1 T("x") -> State 2 M -> State 15 P -> State 16 --------------------- T("(") -> ( <- ) T("x") -> ( <- ) }


Unlike pediatric intussusception, intestinal intussusception is infrequent in adults and it is often secondary to a pathological condition. The growing use of Multi-Detector Computed Tomography (MDCT) in abdominal imaging has increased the number of radiological diagnoses of intussusception, even in transient and nonobstructing cases. MDCT is well suited to delineate the presence of the disease and provides valuable information about several features, such as the site of intussusception, the intestinal segments involved, and the extent of the intussuscepted bowel. Moreover, MDCT can demonstrate the complications of intussusceptions, represented by bowel wall ischemia and perforation, which are mandatory to promptly refer for surgery. However, not all intussusceptions need an operative treatment. In this paper, we review the current role of MDCT in the diagnosis and management of intussusception in adults, focusing on features, as the presence of a leading point, that may guide an accurate selection of patients for surgery.
Intestinal intussusception in adults is considered uncommon, accounting for an estimated 5% of all intussusceptions and representing only 1% of intestinal obstructions [ 1 , 2 ]. Unlike pediatric intussusception, which is usually idiopathic, adult intussusception is most often secondary to an identifiable cause [ 3 ]. Many pathological conditions [ 4 , 5 ], such as malignant or benign neoplasms, polyps, Meckel's diverticulum, and postoperative adhesions, may act as leading points by altering bowel peristalsis. Association with malignant tumors is more common in large bowel intussusception (65–70% of cases), while small bowel intussusceptions are secondary to a malignancy in 30–35% of cases only.
Before the widespread use of Multi-Detector Computed Tomography (MDCT), the diagnosis was based on surgical findings in patients with obstructive symptoms [ 4 ]. The significant advancements in CT technology, along with the progressive use of MDCT in the diagnosis of abdominal emergencies, have determined an increment in the detection of intestinal intussusceptions [ 6 , 7 ]. The typical bowel-within-bowel appearance [ 1 , 8 – 10 ] is often found in asymptomatic patients, with transient intussusception and no underlying disease [ 11 – 13 ]. Although surgical intervention is considered necessary in symptomatic patients with leading point intussusception [ 14 ], not every patient with CT evidence of intestinal intussusception may require surgery [ 15 , 16 ]. The distinction between lead point and non–lead point intussusception, as well as the detection of obstructive complications on MDCT, is important in determining the appropriate treatment, avoiding unnecessary surgery. In this paper, we review the current role of MDCT in the diagnosis and management of intussusception, focusing on features that may guide an accurate selection of adult patients for surgery, both in small bowel and large bowel intussusceptions.
Intussusception results from altered intestinal motility, determining the telescoping of one bowel segment ( intussusceptum) into the lumen of the contiguous intestinal tract ( intussuscipiens) [ 1 , 8 ]. Although this invagination can occur anywhere along the gastrointestinal tract, most intussusceptions occur in the junctions between mobile and retroperitoneal fixed intestinal segments.
Intussusceptions can be classified into three types based on the location:
Enteroenteric, when confined to the small bowel.
Colocolonic, when involving the large bowel.
Enterocolonic, which can be ileocaecal or ileocaecocolonic.
Intussusception in an adult can be further classified on the basis of whether a lead point is present. Intussusceptions without a lead point tend to be transient, self-limiting, and nonobstructing. Patients present with nonspecific symptoms if any, like vague abdominal pain. In asymptomatic patients, the diagnosis of intussusception is often an incidental finding on MDCT performed for other reasons [ 17 ]. In these cases, most of the time, the small bowel intussusception is self-limited; the length of intussusception is the most reliable predictive indicator of the outcome. Intussusception shorter than 3.5 cm rarely requires surgery [ 16 ].
Clinical diagnosis can be difficult even in symptomatic patients with a leading point intussusception, because of the variety of clinical findings at presentation (crampy abdominal pain, nausea, vomiting, and bloody mucoid stools), depending on the underlying cause. Complicated intussusceptions, with bowel wall engorgement due to impaired mesenteric circulation and signs of parietal ischaemia, are associated with a higher risk of perforation and peritonitis [ 3 , 5 ].
Abdominal MDCT has been shown to be the imaging modality of choice for the detection and assessment of adult bowel intussusception, with a reported accuracy of 58–100% [ 1 , 18 ]. MDCT is well suited to delineate the presence of the disease and provides valuable information about several features, such as the site of intussusception, the intestinal segments involved, and the extent of the intussuscepted bowel [ 19 ]. MDCT has the ability to differentiate between presence and lack of a leading point.
Moreover, MDCT can demonstrate the complications of intussusceptions, represented by bowel wall ischemia and perforation, which are mandatory to promptly refer for surgery.
Merine et al. [ 15 ] in 1987 described three CT patterns of intussusception as corresponding to different stages of the disease: the target-like pattern, the reniform pattern, and the sausage-shaped pattern. The first appearance, the target-like pattern, described as a round mass with intraluminal soft-tissue and eccentric fat density, was thought to correspond to an early intussusception with no or minimal obstruction and without signs of ischemia [ 9 ]. The second appearance, the reniform pattern, appearing as a bilobed mass with central low attenuation and peripheral higher density, was thought to result from ischemic thickening of the intussusceptum's bowel wall. The latter appearance, the sausage-shaped pattern, was thought to result from alternating areas of low and high attenuation related to the bowel wall, mesenteric fat and fluid, intraluminal fluid, contrast material, or air.
Actually, intussusception often appears as a complex soft-tissue mass on MDCT images. It is composed of a central intussusceptum and outer intussuscipiens, separated by mesenteric fat, which appears as a low-attenuation layer. Enhanced vessels are often seen within the mesenteric fat ( Figure 1). The image pattern varies according to location, axis of section, bowel wall thickness, and lumen patency. The appearance of an intussusception on CT images is similar to that of a “target” mass when the CT beam is perpendicular to the longitudinal axis of the intussusception and to that of a “sausage” mass when the CT beam is parallel or oblique to the longitudinal axis [ 10 ] ( Figure 2). The presence of a lead point, the configuration of the lead mass, the degree of bowel wall edema, and the amount of invaginated mesenteric fat all affect the radiological aspect of an intussusception. Intussusception with a lead point usually appears as an abnormal target-like mass with a cross-sectional diameter greater than that of the normal bowel and may be associated with proximal bowel obstruction ( Figure 3). It is often not easy to distinguish the distinct anatomic features of the lead mass, because of poor recognisability of the edematous intestinal wall and the lead mass. If there is bowel wall edema due to impaired circulation of the mesenteric vessels, it is difficult to differentiate a lead mass from inflammation because the former may appear amorphous ( Figure 4). Even when a leading mass is seen, it is not always possible to reliably distinguish a malignant from benign neoplasm.
Adult enteroenteric intussusceptions are thought to be relatively rare. They can be classified as duodenojejunal, jejunojejunal, or jejunoileal. Duodenojejunal intussusception is rare, because of anatomic fixation of a large portion of the duodenum. Retrograde jejunal intussusceptions, in which retrograde peristalsis determines the telescoping of a distal bowel segment into the adjacent proximal segment, are reported as postoperative complications of Roux-en-Y anastomoses [ 20 ].
Most cases of small bowel intussusceptions are secondary to benign intra- or extraluminal lesions, such as inflammatory lesions, Meckel's diverticulum [ 21 ], postoperative adhesions, lipoma ( Figure 5), and adenomatous polyps [ 3 ], but they can also be iatrogenic (placement of intestinal tube, gastrojejunostomy) or caused by abdominal trauma [ 22 ] ( Figure 6). Malignant pathologies, accounting for 15% of cases, include adenocarcinoma, malignant GIST, metastasis from various primary sites (lung or breast, malignant melanoma, osteosarcoma, and lymphoma), and primary lymphoma [ 3 ] ( Figure 7).
Enteroenteric intussusceptions without a lead point tend to be nonobstructing and are usually smaller in transverse diameter and shorter in length than intussusception with a lead point ( Figure 8). In some cases, the enteroenteric intussusception is due to increased peristalsis of the intestinal loops caused by distal obstruction, such as a stenosis caused by a neoplasm of the colon ( Figure 9). Obstructing enteroenteric intussusceptions, often caused by a lead point, may present at CT with thickening and alterated enhancement of the bowel wall and engorgement of mesenteric vessels ( Figure 10).
Unlike small-bowel intussusception, more than half of large bowel intussusceptions are associated with malignant lesions. Adenocarcinoma is the most common malignant neoplasm associated with colocolonic intussusception, followed by lymphoma and metastatic disease [ 1 ]. Among about 30% of large bowel intussusceptions caused by benign lesions, lipomas are the most common cause, followed by GISTs, adenomatous polyps ( Figure 11), and other benign conditions like endometriosis and a previous anastomosis [ 8 ]. Idiopathic intussusception occurs less often than those of the small bowel, accounting for approximately 10% of intussusceptions [ 5 ].
Sigmoid-rectal intussusception ( Figure 12) is a very rare condition in which concentric invagination of distal sigma progresses towards rectal ampulla but does not protrude through the anus [ 23 ].
In enterocolonic intussusception, the lead point can be located in the small bowel, in the large bowel, often in the caecum ( Figure 13), or in the appendix. Enterocolonic intussusception can be further classified as ileocaecal, in which the ileocaecal valve is in site, or ileocaecocolonic ( Figure 14), in which the ileocaecal valve is displaced. Appendiceal intussusception is rare and difficult to diagnose radiologically [ 24 ].
Because of significant advancements in MDCT scanners along with increasing use of MDCT in abdominal emergency imaging, the detection of enteroenteric intussusceptions by CT has increased. Intussusceptions are now being detected incidentally on MDCT in patients being scanned for unrelated reasons [ 8 ] or in asymptomatic patients, often with transient intussusceptions and without an identifiable lead point. The radiologist can readily make a correct diagnosis, detecting specific MDCT findings such as the bowel-within-bowel appearance. Some findings on CT may be helpful in guiding management and reducing the prevalence of unnecessary surgery. The radiologist's aim is not only to recognize intussusception, but also to define its location, enteroenteric, colocolonic, or enterocolonic, to evaluate underlying pathology, and to identify complicated intussusceptions, associated with obstruction or ischemia, which represent indications for surgical exploration.
The authors declare that there is no conflict of interests regarding the publication of this paper.
Enteroenteric intussusception. Axial CT images (a, b) demonstrate a round mass with “target” pattern and central hypodense area of mesenteric fat in which vessels are seen as linear enhanced structures (arrow). Oblique CT reformatted images (c, d) oriented parallel to the longitudinal axis of the intussusception depict intussusception as a large “sausage-shaped” mass, showing length of involvement. Gas-fluid levels in the dilated proximal loops are signs of small bowel obstruction.
Enterocolonic (ileocaecocolonic) intussusception. Intussusception may present as a target (a), reniform bilobed (b, c), and a sausage-shaped (d) mass depending on the different axial CT scans or reformatted planes. Mesenteric vessels appear as enhanced linear structures between hypodense mesenteric fats (arrow). Dilated proximal bowel loops are opacified with oral contrast.
Enterocolonic (ileocaecocolonic) intussusception caused by intestinal lymphoma, with proximal bowel obstruction. Plain abdominal radiography (a) shows gas-fluid levels within distended small bowel loops. Intussusception is well depicted on axial CT scans (b, c) and coronal reformatting (d).
Enterocolic (ileocaecocolonic) intussusception due to a caecal carcinoma. CT images on axial scans (a, b) and oblique reformatting (c, d) show lymph nodes and vascular engorgement in the intussuscepted mesentery and fluid distention of the intussuscipiens. Extraparietal air indicates local perforation (arrow).
Colocolonic (sigmoid) intussusception caused by a lipoma. Ultrasound scan (a) shows a pelvic layered ovoid mass. CT images on axial (b) and oblique reformatting (c, d) demonstrate an intraluminal lesion with fat attenuation (arrow) that serves as the intussusception lead point.
Enteroenteric (ileoileal) transient intussusception in a traumatized patient. Axial CT images oriented perpendicular to the longitudinal plane of the intussusception demonstrate the typical multilayered appearance of small bowel intussusception. Heterogeneous “target” mass with the intussuscipiens, intussusceptum, and vessels within the invaginated mesenteric fat. No signs of significative obstruction, only mild stasis in the small bowel.
Enteroenteric (ileoileal) intussusception caused by lymphoma. Axial CT images show the typical appearance of small bowel intussusception (a, b, and c). Marked circumferential thickening of the wall of a distal ileum loop (d) is due to lymphoma, which is responsible for intussusception.
Enteroenteric (ileoileal) intussusception. Target-like mass on axial CT images. Transient intussusception with no signs of intestinal obstruction or intestinal ischemia.
Enteroenteric (ileoileal) intussusception secondary to colic obstruction caused by a sigmoid cancer. Intussusception appears as a small target mass (a, b) in a condition of intestinal obstruction with massive small and large bowel dilatation, due to stenosing sigmoid cancer (d). Coronal (e) and sagittal (f) reformatting better depict the site and the extent of intussusception.
Enteroenteric (ileoileal) intussusception. Bowel wall of intussusception is thickened and enhanced. Signs of small bowel obstruction are seen on axial scans (a, b) and coronal (c) and sagittal (d) reformatting.
Colocolonic intussusception caused by a sigmoid lipoma. The intussusception is well depicted on axial CT scan (a). Both axial scan (b) and coronal reformatting (c) show the hypodense polypoid mass that acts as the lead point.
Colocolonic (sigmoid-rectal) intussusception (a) caused by sigmoid adenocarcinoma. The enhanced neoplastic mass, which acts as the lead point, is located in the rectum (arrow), at the tip of intussusceptum (b, c, d, and e).
Enterocolic (ileocaecocolonic) intussusception caused by a caecal carcinoma. Ultrasound scan (a) shows a heterogeneous target-like mass located in right flank. CT images on axial scans (b, c) and coronal and oblique reformatting (d, e) demonstrate lead point intussusception with invaginated mesenteric fat, vessels, and lymph nodes.
Enterocolonic (ileocaecocolonic) intussusception caused by a polyp. Axial CT images (a, b) demonstrate a soft-tissue density round mass with thin eccentric hypodensity (arrow). Oblique CT reformatting (c, d) clearly shows the enhancement of the lead mass, which facilitates its identification.


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Applying to Google Season of Docs 2020
May 4, 2020 - Marta Paes (@morsapaes)The Flink community is thrilled to share that the project is applying again to Google Season of Docs (GSoD) this year! If you’re unfamiliar with the program, GSoD is a great initiative organized by Google Open Source to pair technical writers with mentors to work on documentation for open source projects. The first edition supported over 40 projects, including some other cool Apache Software Foundation (ASF) members like Apache Airflow and Apache Cassandra.
Why Apply? #
As one of the most active projects in the ASF, Flink is experiencing a boom in contributions and some major changes to its codebase. And, while the project has also seen a significant increase in activity when it comes to writing, reviewing and translating documentation, it’s hard to keep up with the pace.
Since last year, the community has been working on FLIP-42 to improve the documentation experience and bring a more accessible information architecture to Flink. After some discussion, we agreed that GSoD would be a valuable opportunity to double down on this effort and collaborate with someone who is passionate about technical writing…and Flink!
How can you contribute? #
If working shoulder to shoulder with the Flink community on documentation sounds exciting, we’d love to hear from you! You can read more about our idea for this year’s project below and, depending on whether it is accepted, apply as a technical writer. If you have any questions or just want to know more about the project idea, ping us at<EMAIL_ADDRESS>Project: Improve the Table API & SQL Documentation #
Apache Flink is a stateful stream processor supporting a broad set of use cases and featuring APIs at different levels of abstraction that allow users to trade off expressiveness and usability, as well as work with their language of choice (Java/Scala, SQL or Python). The Table API & SQL are Flink’s high-level relational abstractions and focus on data analytics use cases. A core principle is that either API can be used to process static (batch) and continuous (streaming) data with the same syntax and yielding the same results.
As the Flink community works on extending the scope of the Table API & SQL, a lot of new features are being added and some underlying structures are also being refactored. At the same time, the documentation for these APIs is growing onto a somewhat unruly structure and has potential for improvement in some areas.
The project has two main workstreams: restructuring and extending the Table API & SQL documentation. These can be worked on by one person as a bigger effort or assigned to different technical writers.
1) Restructure the Table API & SQL Documentation
Reworking the current documentation structure would allow to:
- Lower the entry barrier to Flink for non-programmatic (i.e. SQL) users.
- Make the available features more easily discoverable.
- Improve the flow and logical correlation of topics.
FLIP-60 contains a detailed proposal on how to reorganize the existing documentation, which can be used as a starting point.
2) Extend the Table API & SQL Documentation
Some areas of the documentation have insufficient detail or are not accessible for new Flink users. Examples of topics and sections that require attention are: planners, built-in functions, connectors, overview and concepts sections. There is a lot of work to be done and the technical writer could choose what areas to focus on — these improvements could then be added to the documentation rework umbrella issue (FLINK-12639).
Project Mentors #
- Aljoscha Krettek (Apache Flink and Apache Beam PMC Member)
- Seth Wiesman (Apache Flink Committer)
Related Resources #
-
FLIP-60: https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=127405685
-
Table API & SQL Documentation: //nightlies.apache.org/flink/flink-docs-release-1.10/dev/table/
-
How to Contribute Documentation: https://flink.apache.org/contributing/contribute-documentation.html
-
Documentation Style Guide: https://flink.apache.org/contributing/docs-style.html
We look forward to receiving feedback on this GSoD application and also to continue improving the documentation experience for Flink users. Join us!


{% extends "dashboard_main_template/dashboard_main.html" %} {% load staticfiles %} {%block style%} {% endblock %} {% block content %}
Please be advised that Metamarker stores your datasetto its own database for temporary purpose however it does not share or reuse your dataset with anyone else
Are you sure you want to proceed? Note that all your data and result will be permanently removed from our databae.


Fila Hotels & Resorts |
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Surfing
What the expert saysDarren Burrett is the founder of Surf South West, the first surf school in the UK to be awarded level four status by the British Surfing Association (BSA). He has 26 years' experience and has surfed in more than 20 countries.Keep in trimEvery board has a sweet spot, where it is stable and balanced and will move 'in trim' with the wave. If you're too far forward, the nose will sink, too far back and it will stall. When you are centred, the nose of the board will sit just above the surface of the water.Catch a waveStart paddling for a wave way in advance, to build up enough momentum for it to take you. When you feel it pick you up, the natural instinct is to grip the board and attempt to get up, but this is when you should put in a final two to three powerful arm strokes. Spend plenty of time practising this without trying to stand up.Master the pop-upThe holy grail of surfing is to get from a prone position to standing in one clean motion. You need to jump to your feet and turn them simultaneously so that they are at a right angle to the board.Perfect your paddlingPaddling - whether to get out through the waves or to catch a wave - is a key skill. The action is similar to front crawl but you need to keep your body as still as possible, just using the shoulders and arms to pull rather than twisting the body or head. Keep your feet slightly apart to aid stability.Night FishingIn typical daylight fishing, anglers are accustomed to seeing what they’re doing and watching the line or the lure, but this is seldom possible at night. You can use black lights that make it possible to watch fluorescent lines very well, but although this was a popular monofilament line several decades ago, few anglers use this type of line, today. For the most part, intuition and a feel for your tackle become more important at night than in the daylight. This makes it advantageous to use a sensitive rod and line, and not to try fishing with ultralight tackle. Obviously, your vision is better on nights with moonlight than on dark or overcast nights, and there can be much debate over whether bright nights are better for fishing than dark nights. Keeping the use of external-source lights to a minimum is a good idea for some types of fishing, though it is unnecessary for others. It is also well known that locations prone to receiving light (docks, piers, bridges, etc.) may attract small fish and thus larger predators, although this is more likely the case in saltwater than in freshwater. Even if you have great intuition and a natural feel for your tackle, a small headlamp is a proper accessory for night fishing, since it frees both hands and issues only a small amount of light. Better ones are those with red and/or green color options, which are not as alarming to fish if you happen to turn toward the water. |
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| © 2018 Fila Hotels & Resorts |


ChIA-PET Utilities
from Reads to Interactions
ChIA-PET Data Processing
-
01. Linker Processing
Identify linker to generate the tags -
02. Tag Mapping
Align tags to reference genome -
03. Interaction Calling
Aggregate mapped paired tags
ChIA-PET Data Processing Options
-
01. Linker Processing
Identify linker to generate the tags -
02. Tag Mapping
Align tags to reference genome -
03. Interaction Calling
Aggregate mapped paired tags
- Cutadapt
- BWA
Bowtie
many others - ChiaSig
MC_DIST
MICC
SPROUT
ChIA-PET Data Processing Options
-
01. Linker Processing
Identify linker to generate the tags -
02. Tag Mapping
Align tags to reference genome -
03. Interaction Calling
Aggregate mapped paired tags
- Cutadapt
- BWA
Bowtie
many others - ChiaSig
MC_DIST
MICC
SPROUT
- ChIA-PET Tools
Mango
ChIA-PET Utilities
ChIA-PET Read Construct
- Step 1 : Fixing
- Step 2 : Fragmentation
- Step 3 : Linker ligation
ChIA-PET Read Construct
- Step 4 : Proximity ligation
- Step 5 : Tagmentation
- Step 6 : End products
ChIA-PET Read Construct
- Read w.r.t. genome
01. Linker Processing
Cutadapt
--info-file -a <LINKER1> -g <LINKER1> [-a <LINKER2> -g <LINKER2>]
Tab-separated output fields:
col 5: Sequence of the read to the left of the adapter match (can be empty)
col 6: Sequence of the read that was matched to the adapter
col 7: Sequence of the read to the right of the adapter match (can be empty)
col 8: Name of the found adapter
Post process to extract tag sequences into FASTQ
01. Linker Processing
Mango
std::string::find('GTTGGATAAG') std::string::find('GTTGGAATGT')
Support dual-linker and bridge linker
Exact match (on the first 10 bases)
What about the remaining 9 bases?
01. Linker Processing
CPU
cpu stag cpu tag
Support dual-linker and bridge linker
Banded Smith-Waterman alignment of linkers permutation
Multithreading with single instruction multiple data
01.
01.
01. Linker Processing
Some other possibilities
Does the program identify the linker cases on left?
How does it handle the permutations?
An imagination?
Hypothetical case..
02. Tag Mapping
Where from the genome may the tags have come from? Seemingly a simple task...
- Mode: Map R/1 & R/2 separately
- Mapper: 140 available (Nov 2012)
- Tag length: 15 to 151 bases
02. Tag Mapping - 'Best' mapper for 50 bp reads..
02. Tag Mapping - 'Best' mapper for 100 bp reads..
02. Tag Mapping - 'Best' mapper for 200 bp reads..
02. Tag Mapping
Best Mapper?
- Exact: All aligners performed well. BWA mem performs noticeably worst.
- Snp: All aligners comparable if mismatch rate<4% except for SOAP.
- Insertion: BWA mem best. With increasing read length, Bowtie2 catches up to STAR.
- Deletion: Similar observations to insertion.
- Summary: Overall, Bwa mem and Bowtie2. STAR has higher error rate.
Best of both worlds..
Short-to-medium size reads..
BWA aln + BWA memCPU memaln
02. Tag Mapping
Which of the possible alignments is the "Best"?
- MAPQ: MapQ≥q? q=[25, 30, 40]?
- #alignments: Alignments with tag "NM"≤x? Or tag "AS"≤y?
- 2nd Best: Tag "AS" vs "XS"?
- Aligner: Provide these attributes?
Tag "TTGTCAAAATTTAGTAAAATATATTCAAATATAATTAAATATATTAAAAC" (50bp) from Arabidopsis
- Bowtie2: MapQ=30
- BWA: MapQ=9
- Alignments: 1 locus (NM=0), 25 loci (NM=1)
02. Tag Mapping
Non-redundant set.. which pair of tags are duplicated?
- Criteria: Same genomic position of Tag/1 and Tag/2 5' ends
A) Duplicated?
B) Duplicated?
C) Duplicated?
The details are not the details. They make the design.
Charles Eames
03. Interaction Calling
- Interaction Classification
03. Interaction Calling
- Interaction Classification
03. Interaction Calling - Individual Tags
03. Interaction Calling - Paired Tags
03. Interaction Calling - clustering
03. Interaction Calling - clustering
03. Interaction Calling
03. Interaction Calling
03. Interaction Calling
-
*ChIA-PET Tools
Li et al. 2010 -
*ChiaSig
Paulsen et at. 2013 -
*Mango
Phanstiel et al. 2015
- - Hypergeometric model
- random ligation noise only
- PET count>3
- misses weaker interactions - - Non-central hypergeometric model
- random ligation noise +
chromatin random collision
- too conservative (high FNs) - - Binomial model with bias correction
- random ligation noise +
chromatin random collision
- read depth consideration
Acknowledgements
Others
Heng LI
Mengyao ZHAO
* * *
IGV Team
Mark ZIEMANN
WebSlides
Unsplash Community
uvCharts + D3.js
gravit
* * *
Dr Wei's Lab
Chia-Lin WEI
Chew Yee NGAN
Harianto TJONG
Liang GONG
* * *
Funding
Department of Energy
Joint Genome Institute
* * *
Dr Ruan's Lab
Yijun RUAN
Liuyang CAI
Zhonghui TANG
Simon TIAN
Byoungkoo LEE
* * *
Thank you!
Questions & Answers


Move your mouse cursor over either table to move the red column. It should stay close to your cursor. If both tables respond fluently on yur hardware, try increasing the table size in App.jsx. Conversely, if both are too slow, try a smaller table size.
The effect is even clearer when you open the developer console.
This example simulates a complex webapp with leaf components that apply logic on state that centrally managed. We developed this for a car configurator (https://configurator.mazda.nl) where many things change based on a single click.
React is built to quickly rerender subtrees, but in our case that approach isn't sufficient because many components derive their state from the centrally managed state. Deriving that state costs CPU cycles. Therefore preventing rerenders in that case will improve user experience.


Before I met Shana I was incomplete. I've always felt that God delivered Shana to me, as my other half, to make me whole. She's my guardian angel and I was over the moon when she agreed to become my wife. I pleaded to love her forever, day and night.
I met Jay when I was 18 years old, in the very first lecture of university - he happened to sit right behind me! From that moment sparks flew and our future was written in the stars, unbeknownst to us. No matter where I went, or what obstacle I faced, Jay has always been there (with his jokes) to love and comfort me.
We invite you to witness the ceremony of our marriage as we pledge our vows. Share the joy with us at the beautiful honour of our love, loyalty and union.
Our wedding celebration party! Join us for an unparalled evening of delight. Together with dinner, dancing and drinks we will toast to our happily ever after as Mr & Mrs Lawrence.


Born, Raised and Brewed in Waco.
It didn’t take long for us to come up with the name of our brewery. If you talk to small, independent
brewery owners around the world about why they wanted to get into this business, you’ll hear one
particular word often: community. And what better community to give back to than the one we hold so
dear.
At Waco Ale Company, we use the highest quality ingredients to create award-winning craft beers for
every pallet. Whether you’re a beginner looking for just a little more flavor, or a connoisseur looking for
the rarest of brews, you will find what you’re looking for right here.
Come see us in beautiful downtown Waco. We love it here and hope you will too.
From our family to yours,
Cheers!


Use the SpotifySearch app to browse new releases and find your favorite songs
All you get is this text and a mostly barebones HTML document.
What’s on Spotify? Music There are millions of songs on Spotify. Play your favorites, discover new tracks, and build the perfect collection. Playlists You’ll find readymade playlists to match your mood, put together by music fans and experts. New Releases Hear this week’s latest singles and albums, and check out what’s hot in the Top 50. Know what you want to listen to? Just search and hit play. Browse Check out the latest charts, brand new releases and great playlists for right now. Discover Enjoy new music every Monday with your own personal playlist.


Guide
Mimir
Official site
More from Dolittle
Bifrost
Yggdrasil
Forseti
Balder
Chirp
Contact
Dolittle AS
Klinestadmoen 9
3241 Sandefjord
Norway


Actual source code: iguess.c
petsc-3.8.0 2017-09-26
1: #include <petsc/private/kspimpl.h>
3: PetscFunctionList KSPGuessList = 0;
4: static PetscBool KSPGuessRegisterAllCalled;
6: /*
7: KSPGuessRegister - Adds a method for initial guess computation in Krylov subspace solver package.
9: Not Collective
11: Input Parameters:
12: + name_solver - name of a new user-defined solver
13: - routine_create - routine to create method context
15: Notes:
16: KSPGuessRegister() may be called multiple times to add several user-defined solvers.
18: Sample usage:
19: .vb
20: KSPGuessRegister("my_initial_guess",MyInitialGuessCreate);
21: .ve
23: Then, it can be chosen with the procedural interface via
24: $ KSPSetGuessType(ksp,"my_initial_guess")
25: or at runtime via the option
26: $ -ksp_guess_type my_initial_guess
28: Level: advanced
30: .keywords: KSP, register, KSPGuess
32: .seealso: KSPGuess, KSPGuessRegisterAll()
34: @*/
35: PetscErrorCode KSPGuessRegister(const char sname[],PetscErrorCode (*function)(KSPGuess))
36: {
40: PetscFunctionListAdd(&KSPGuessList,sname,function);
41: return(0);
42: }
44: /*
45: KSPGuessRegisterAll - Registers all KSPGuess implementations in the KSP package.
47: Not Collective
49: Level: advanced
51: .keywords: KSPGuess, register, all
53: .seealso: KSPRegisterAll(), KSPInitializePackage()
54: */
55: PetscErrorCode KSPGuessRegisterAll(void)
56: {
60: if (KSPGuessRegisterAllCalled) return(0);
61: KSPGuessRegisterAllCalled = PETSC_TRUE;
62: KSPGuessRegister(KSPGUESSFISCHER,KSPGuessCreate_Fischer);
63: KSPGuessRegister(KSPGUESSPOD,KSPGuessCreate_POD);
64: return(0);
65: }
67: /*@
68: KSPGuessSetFromOptions
69: @*/
70: PetscErrorCode KSPGuessSetFromOptions(KSPGuess guess)
71: {
76: if (guess->ops->setfromoptions) { (*guess->ops->setfromoptions)(guess); }
77: return(0);
78: }
80: /*@
81: KSPGuessDestroy - Destroys KSPGuess context.
83: Collective on KSPGuess
85: Input Parameter:
86: . guess - initial guess object
88: Level: beginner
90: .keywords: KSP, destroy
92: .seealso: KSPGuessCreate(), KSPGuess, KSPGuessType
93: @*/
94: PetscErrorCode KSPGuessDestroy(KSPGuess *guess)
95: {
99: if (!*guess) return(0);
101: if (--((PetscObject)(*guess))->refct > 0) {*guess = 0; return(0);}
102: if ((*guess)->ops->destroy) { (*(*guess)->ops->destroy)(*guess); }
103: PetscHeaderDestroy(guess);
104: return(0);
105: }
107: /*@C
108: KSPGuessView - View the KSPGuess object
110: Logically Collective on KSPGuess
112: Input Parameters:
113: + guess - the initial guess object for the Krylov method
114: - viewer - the viewer object
116: Notes:
118: Level: intermediate
120: .seealso: KSP, KSPGuess, KSPGuessType, KSPGuessRegister(), KSPGuessCreate(), PetscViewer
121: @*/
122: PetscErrorCode KSPGuessView(KSPGuess guess, PetscViewer view)
123: {
125: PetscBool ascii;
129: if (!view) {
130: PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)guess),&view);
131: }
134: PetscObjectTypeCompare((PetscObject)view,PETSCVIEWERASCII,&ascii);
135: if (ascii) {
136: PetscObjectPrintClassNamePrefixType((PetscObject)guess,view);
137: if (guess->ops->view) {
138: PetscViewerASCIIPushTab(view);
139: (*guess->ops->view)(guess,view);
140: PetscViewerASCIIPopTab(view);
141: }
142: }
143: return(0);
144: }
146: /*@
147: KSPGuessCreate - Creates the default KSPGuess context.
149: Collective on MPI_Comm
151: Input Parameter:
152: . comm - MPI communicator
154: Output Parameter:
155: . guess - location to put the KSPGuess context
157: Notes:
158: The default KSPGuess type is XXX
160: Level: beginner
162: .keywords: KSP, create, context
164: .seealso: KSPSolve(), KSPGuessDestroy(), KSPGuess, KSPGuessType, KSP
165: @*/
166: PetscErrorCode KSPGuessCreate(MPI_Comm comm,KSPGuess *guess)
167: {
168: KSPGuess tguess;
173: *guess = 0;
174: KSPInitializePackage();
175: PetscHeaderCreate(tguess,KSPGUESS_CLASSID,"KSPGuess","Initial guess for Krylov Method","KSPGuess",comm,KSPGuessDestroy,KSPGuessView);
176: *guess = tguess;
177: return(0);
178: }
180: /*@C
181: KSPGuessSetType - Sets the type of a KSPGuess
183: Logically Collective on KSPGuess
185: Input Parameters:
186: + guess - the initial guess object for the Krylov method
187: - type - a known KSPGuess method
189: Options Database Key:
190: . -ksp_guess_type <method> - Sets the method; use -help for a list
191: of available methods
193: Notes:
195: Level: intermediate
197: .seealso: KSP, KSPGuess, KSPGuessType, KSPGuessRegister(), KSPGuessCreate()
199: @*/
200: PetscErrorCode KSPGuessSetType(KSPGuess guess, KSPGuessType type)
201: {
202: PetscErrorCode ierr,(*r)(KSPGuess);
203: PetscBool match;
209: PetscObjectTypeCompare((PetscObject)guess,type,&match);
210: if (match) return(0);
212: PetscFunctionListFind(KSPGuessList,type,&r);
213: if (!r) SETERRQ1(PetscObjectComm((PetscObject)guess),PETSC_ERR_ARG_UNKNOWN_TYPE,"Unable to find requested KSPGuess type %s",type);
214: if (guess->ops->destroy) {
215: (*guess->ops->destroy)(guess);
216: guess->ops->destroy = NULL;
217: }
218: PetscMemzero(guess->ops,sizeof(struct _KSPGuessOps));
219: PetscObjectChangeTypeName((PetscObject)guess,type);
220: (*r)(guess);
221: return(0);
222: }
224: /*@C
225: KSPGuessGetType - Gets the KSPGuess type as a string from the KSPGuess object.
227: Not Collective
229: Input Parameter:
230: . guess - the initial guess context
232: Output Parameter:
233: . name - name of KSPGuess method
235: Level: intermediate
237: .keywords: KSP, get, method, name
239: .seealso: KSPGuessSetType()
240: @*/
241: PetscErrorCode KSPGuessGetType(KSPGuess guess,KSPGuessType *type)
242: {
246: *type = ((PetscObject)guess)->type_name;
247: return(0);
248: }
250: /*@
251: KSPGuessUpdate - Updates the guess object with the current solution and rhs vector
253: Collective on KSPGuess
255: Input Parameter:
256: + guess - the initial guess context
257: . rhs - the corresponding rhs
258: - sol - the computed solution
260: Level: intermediate
262: .keywords: KSP, get, method, name
264: .seealso: KSPGuessCreate(), KSPGuess
265: @*/
266: PetscErrorCode KSPGuessUpdate(KSPGuess guess, Vec rhs, Vec sol)
267: {
274: if (guess->ops->update) { (*guess->ops->update)(guess,rhs,sol); }
275: return(0);
276: }
278: /*@
279: KSPGuessFormGuess - Form the initial guess
281: Collective on KSPGuess
283: Input Parameter:
284: + guess - the initial guess context
285: . rhs - the current rhs vector
286: - sol - the initial guess vector
288: Level: intermediate
290: .keywords: KSP, get, method, name
292: .seealso: KSPGuessCreate(), KSPGuess
293: @*/
294: PetscErrorCode KSPGuessFormGuess(KSPGuess guess, Vec rhs, Vec sol)
295: {
302: if (guess->ops->formguess) { (*guess->ops->formguess)(guess,rhs,sol); }
303: return(0);
304: }
306: /*@
307: KSPGuessSetUp - Setup the initial guess object
309: Collective on KSPGuess
311: Input Parameter:
312: - guess - the initial guess context
314: Level: intermediate
316: .keywords: KSP, get, method, name
318: .seealso: KSPGuessCreate(), KSPGuess
319: @*/
320: PetscErrorCode KSPGuessSetUp(KSPGuess guess)
321: {
322: PetscErrorCode ierr;
323: PetscObjectState omatstate = -1, matstate;
324: PetscInt oM = 0, oN = 0, M, N;
328: if (guess->A) {
329: PetscObjectStateGet((PetscObject)guess->A,&omatstate);
330: MatGetSize(guess->A,&oM,&oN);
331: }
332: KSPGetOperators(guess->ksp,&guess->A,NULL);
333: MatGetSize(guess->A,&M,&N);
334: PetscObjectStateGet((PetscObject)guess->A,&matstate);
335: if (omatstate != matstate || M != oM || N != oN) {
336: PetscInfo6(guess,"Resetting KSPGuess since mat state or sizes have changed (%D != %D, %D != %D, %D != %D)\n",omatstate,matstate,oM,M,oN,N);
337: if (guess->ops->reset) { (*guess->ops->reset)(guess); }
338: } else {
339: PetscInfo(guess,"KSPGuess status unchanged\n");
340: }
341: if (guess->ops->setup) { (*guess->ops->setup)(guess); }
342: return(0);
343: }


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DAMAGE_RECOVER
- Static variable in class eu.irreality.age.
Mobile
data
- Variable in class micromod.
Instrument
DataReader
- Class in
micromod
The DataReader contains methods to convert data on an implementation of DataInput into a more useful format.
DataReader(DataInput)
- Constructor for class micromod.
DataReader
Constructor
dccChatRequest(String, InetAddress, short)
- Method in class eu.irreality.age.
IrcAgeBot
dccChatRequest(String, InetAddress, short)
- Method in class eu.irreality.age.
IrcBot
dccChatRequest(String, InetAddress, short)
- Method in interface eu.irreality.age.
IrcListener
dccConnection(String)
- Method in class eu.irreality.age.
IrcBot
dccConnection(String)
- Method in class eu.irreality.age.
IrcDccChatSynchronousHandler
dccConnection(String)
- Method in interface eu.irreality.age.
IrcDccListener
dccDisconnection(String)
- Method in class eu.irreality.age.
IrcBot
dccDisconnection(String)
- Method in class eu.irreality.age.
IrcDccChatSynchronousHandler
dccDisconnection(String)
- Method in interface eu.irreality.age.
IrcDccListener
dccMsg(String, String)
- Method in class eu.irreality.age.
IrcBot
dccMsg(String, String)
- Method in class eu.irreality.age.
IrcDccChatSynchronousHandler
dccMsg(String, String)
- Method in interface eu.irreality.age.
IrcDccListener
DEAD
- Static variable in class eu.irreality.age.
Mobile
dealDamage(Mobile, Mobile, boolean)
- Method in class eu.irreality.age.
Weapon
dealDamage(Mobile, Mobile, boolean, Item)
- Method in class eu.irreality.age.
Weapon
dealDamageDefended(Mobile, Mobile, boolean)
- Method in class eu.irreality.age.
Weapon
dealDamageDefended(Mobile, Mobile, boolean, Item)
- Method in class eu.irreality.age.
Weapon
decreaseHP(int)
- Method in class eu.irreality.age.
Mobile
decreaseTime()
- Method in class eu.irreality.age.
PropertyEntry
defaultBPM
- Variable in class micromod.
Sequence
defaultTempo
- Variable in class micromod.
Sequence
Descriptible
- Interface in
eu.irreality.age
Description
- Class in
eu.irreality.age
Descripción con comparando y máscara, devuelve un texto u otro según el estado.
Description(String, long, long)
- Constructor for class eu.irreality.age.
Description
Description(World, Node)
- Constructor for class eu.irreality.age.
Description
desvestir(Item)
- Method in class eu.irreality.age.
Mobile
die()
- Method in class eu.irreality.age.
Mobile
die()
- Method in class eu.irreality.age.
Player
directionName(int)
- Static method in class eu.irreality.age.
Path
disable(Entity)
- Method in class eu.irreality.age.
Effect
DISABLED
- Static variable in class eu.irreality.age.
Mobile
disconnect()
- Method in class eu.irreality.age.
Player
DO
- Static variable in interface eu.irreality.age.
TelnetConstants
dodge(Mobile)
- Method in class eu.irreality.age.
Mobile
DODGE_RECOVER
- Static variable in class eu.irreality.age.
Mobile
DODGING
- Static variable in class eu.irreality.age.
Mobile
DONT
- Static variable in interface eu.irreality.age.
TelnetConstants
doPlayback()
- Method in class micromod.
MicroMod
Call this method in an infinite loop to render a "tick" of audio at a time.
doRealTimePlayback()
- Method in class micromod.
MicroMod
DYING
- Static variable in class eu.irreality.age.
Mobile
Overview
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--- layout: default title: React Redux GraphQL bootcamp in Lisbon description: React Redux GraphQL bootcamp in Lisbon. Small groups and project-based learning. menu: next-courses alias: [/react-redux-graphql-bootcamp-lisbon, /react-redux-training-london/] url: /react-redux-training-london img: https://reactjs.academy/assets/img/space-torres-vedras6.jpg ---
ReactJS Academy is Europes longest running dedicated React and Redux training. Established in May 2016, we've trained over 1000 developers across the EU in our private bootcamps and public workshops.
You can read reviews of our workshops in the following Meetup groups which we organise:
{% include meetup-list.html %}We are committed to making the tech industry more accessible and diverse, please contact us directly to find out about discounts and scholarships.


| Name: mast PUJITH | Reg No: 13-12SMHC18 |
|---|---|
| Age / Sex:13/M | Contact No:9900159039 |
| Marital Status:NA | Date:29/12/18 |
| Occupation:8TH STD | Dr. PJFP |
| Address:NATIVE OF BENGALURU. | DIAGNOSIS- |
GREYING OF HAIR FRM 1 AND HALF YEAR.

IF GETS PHYSICALLY STRESSED WILL GET FEVER AND HEADACHE.
PICKY EATER. FRM CHILDHOOD ALO H WAS VERY THIN SKINNY. LIKES ONLY RAGI MALT AND CHAPATHI. BUT LESS QUANTITY. WATER ALSO LESS. CANT TOLERATE HEAT AND COLD. LIKE SUMMER ONLY.
PAST H/O:
WHEEZING – AYURVEDIC RX NOW FRM 2 YEARS OK.
WT: 42.4KG.
RX:
LYCO 1M – 4-4-4-4 1 DRAM. 4 DAYS.
TUBERCULIUM / ALFALFA/AVENA 1M – 4-4-4-4 1 DRAM 4 DAYS
ALFALFA+AVENA+GINSENG Q – 10-10-10 DROPS A/FOOD WATER.
RUB 1DOSE – NIGHT B/FOOD.
CALC PHOS 12X -4-4-4-4.
19/1/19: app improved.
TUBERCULIUM / ALFALFA/AVENA 1M – 4-4-4-4 1 DRAM 4 DAYS
Five phos 4-4-4-4.
RUB 1DOSE – NIGHT B/FOOD. 15
CALC PHOS 12X -4-4-4-4.
2/3/19: feeling weak. Exhausted in the evening. Sleepy more. Exam time erratic sleep timings. Food has improved. Wt: 40kg.
Rx:
TUBERCULIUM / ALFALFA/AVENA 1M – 4-4-4-4 1 DRAM 4 DAYS
Five phos 4-0-4.
RUB 1DOSE – NIGHT B/FOOD. 30
Lycopodium 1m/vinca minor 1m/thyroidinum1m 4-4-4 alternate weeks fr 4 days.
Acid phosQ – 20 ml mix in 100 ml oil and apply.
1 month medicines - 1350
13/4/19: food improved. Cough getting severe these days. Grey hair same. Dry scalp. Weakness better. Headaches not there.before use to get cough for curd now he feels ok with curd. Milk still aggravates his cough or gets cough if he drinks milk. Cycling also gives cough.morning walks also cough inc.>hot water if he drinks.
Rx:
TUBERCULIUM / ALFALFA/AVENA 1M – 4-4-4-4 1 DRAM 4 DAYS
Five phos 4-0-4.
RUB 1DOSE – NIGHT B/FOOD. 30
Thyroidinum 6x 4-4-4
Acid phos/lyco 200 – 4-4-4-4. 1st week and 3nd week.
4/5/19: cough not getting controlled . getting tears in eyes.
Rx:
Syphilinum 1m – 1 dose morning b/food.
Bryonia / antim tart 200 – 4 pills sos cough.
Rub 60 – 1 mng and night.
Nat sulph 6x 4-0-4.
Five phos 4-0-4.


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Hello! Thanks for checking this splash page.
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Getting started is very easy. Like many other projects we
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npm install page.js -- save
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//index.html
"<"vue-router">" "<"/vue-router">"
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This is sample paragraph text that will show off how awesome
this theme is. It was totally inspired by minimal gh theme,
but I thought it would be even better if we made it tottally
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it even supports some basic syntax highlighting.
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this theme is. It was totally inspired by minimal gh theme,
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Mongo DB
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//../../app.js
function name (one, two)
{
var ans = one * two;
return ans;
console.log('Answer: ' + ans);
};
//../../app.js
mongo.db.insert({name: 'matt eads', age: '21'})
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Connecting to a SQL server is just as easy as any other
db. Go under Microsoft Developer center and download
the appropriate driver. Then follow the directions below.
//../../app.js
var mssql = require('sql-server');
mssql = server();
mssql.connect({
usnm: 'Your Username',
pswd: 'Your Password',
port: 8080,
api-key: 'AS127ahas9090123lk087523'
});
mssql.listen(8080);
This is sample paragraph text that will show off how awesome
this theme is. It was totally inspired by minimal gh theme,
but I thought it would be even better if we made it tottally
in scss and let people customize its awesomeness. Also it comes
with a totally dope terminal, where you customize the code inside
it even supports some basic syntax highlighting.
I would like to give a huge shoutout to the original creator
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