JUNE 24 The Nativity of Saint John the Baptist
Born
six months before our Lord,
Vigil Mass (During the Day)
ENTRANCE ANTIPHON
He will be great in the sight of the Lord and will be filled with the Holy Spirit, even from his mother’s womb; and many will rejoice at his birth.
The GLORIA is said. (English, Latin)
COLLECT
Grant, we
pray, almighty God, that your family may walk in the way of salvation and,
attentive to what
The
PROFESSION OF FAITH (Creed) is
said. (English, Latin)
PRAYER OVER THE OFFERINGS
Look with favour, O Lord, upon the offerings made by your people on the Solemnity of Saint John the Baptist, and grant that what we celebrate in mystery we may follow with deeds of devoted service. Through Christ our Lord.
COMMUNION ANTIPHON
Blessed be the Lord, the God of Israel! He has visited his people and redeemed them.
PRAYER AFTER COMMUNION
May the marvellous prayer of Saint John the Baptist accompany us who have eaten our fill at this sacrificial feast, O Lord, and, since Saint John proclaimed your Son to be the Lamb who would take away our sins, may he implore now for us your favour. Through Christ our Lord.
ENTRANCE ANTIPHON
A man was sent from God, whose name was John. He came to testify to the light, to prepare a people fit for the Lord.
The GLORIA is said. (English, Latin)
COLLECT
O God, who raised up Saint John the Baptist to make ready a nation fit for Christ the Lord, give your people, we pray, the grace of spiritual joys and direct the hearts of all the faithful into the way of salvation and peace. Through our Lord Jesus Christ, your Son, who lives and reigns with you in the unity of the Holy Spirit, one God, for ever and ever.
The
PROFESSION OF FAITH (Creed) is
said. (English, Latin)
PRAYER OVER THE OFFERINGS
We place these offerings on your altar, O Lord, to celebrate with fitting honour the nativity of him, who both foretold the coming of the world’s Saviour and pointed him out when he came. Who lives and reigns for ever and ever.
COMMUNION ANTIPHON
Through the tender mercy of our God, the Dawn from on high will visit us.
PRAYER AFTER COMMUNION
Having
feasted at the banquet of the heavenly Lamb, we pray, O Lord, that, finding joy
in the nativity of Saint John the Baptist, your Church may know as the author
of her rebirth the Christ whose coming John foretold. Who lives and reigns for
ever and ever.


Frequently Asked Questions
How does this work?
Here's a GIF!
Is this an ICO?
No, but our aim is to showcase all ICO's out there on our web page.
What do I get?
You get to buy a piece of blockchain history! At 0.001 ETH per pixel you can immortalize you ICO forever on the Ethereum blockchain.
How long will this be up?
Your ad will be on the Ethereum blockchain forever!
What happens when I buy an ad?
You’ll be able to publish a URL for an image and a URL that your ad links to. You can change them anytime you want as well! You can even transfer your ad to someone else.
Can we pay with currency we are about to ICO?
Sure you can, reach us at<EMAIL_ADDRESS>You can pay us in your ICO currency equivalent to Ethers to post your ad.
Where are the images hosted?
The ad images are served to the user over HTTP, Swarm, or IPFS. You're responsibe for uploading them to a server or the Swarm or the IPFS network and providing the URL. You can also use a "data://" url, but this will cost you more gas to store. We do provide a periodic static cache of the (non-NSFW) images).
What will you do with the money?
Spend a lot of it and donate the rest. We'd like to fund cool projects if we can. Do you have any ideas? Email<EMAIL_ADDRESS>What if I want to advertise my socially-questionable business?
It’s the wild west out here so we can’t really stop you, but we do ask that you set the NSFW flag if the content you’re putting up is not appropriate for minors or illegal in western society. NSFW ads are not displayed by default.
We can never take your ad away, but one right that we do reserve is to mark your ad as NSFW if we feel that it’s inappropriate.
If you mark your ad as NSFW, then you can always change it later. If we have to go in and override your ad to NSFW, then only we can change it to not-NSFW later. We don’t want to do it, but we don’t want to be liable for promoting questionable content.
What’s cool about your smart contract?
So glad you asked! Obviously, all the ad unit management happens on the blockchain. If you buy the ad slot, you own it forever (or until you transfer it to someone else).
But to us, the the coolest thing is: It’s simple and easy to audit. It doesn’t do anything weird with complex pricing structures or use sneaky modifiers.
The second best part? Simple means the fees are low. The most complicated thing you’ll do will only cost a dollar or two in fees, and you’re mostly paying for what you’re buying (storing your ad data on the blockchain) rather than complex middleware logic.
I'm convinced, how do I buy?
Head over to the homepage and click 'Buy Pixels'. You'll need an Ethereum-enabled browser like Mist, Parity, or install the Metamask Chrome Extension.
When you buy an ad unit, you're buying it directly from the smart contract on the Ethereum blockchain. It feels like magic every time.


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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object | +--org.apache.ecs.examples.HtmlTree
This JavaBean returns the hierarchical structure described in a javax.swing.tree.DefaultMutableTreeNode as valid XHTML. This class is a very simple counterpart of the javax.swing.JTree with the exception that the external Controller is integrated into this View. If you want your tree elements (nodes and leafs) to be marked with an anchor, you'll have to make sure that the name of your node contains such an anchor.
A specific node in a tree can be identified by a path, described as colon separated integer values (e.g. "0:1"). Unlike Swing's JTree component, this JavaBean expands only the requested node, leaving all its parent nodes collapsed. An expanded node is one displays its children. A collapsed node is one which hides them.
You can use this class in a JavaServerPage like this:
Although this class is just an example of using parts of the Element Construction Set, you can use it quite well in real web applications....template text...
<%-- Get the bean from the request scope or create a new one if none exists --%>
<jsp:useBean id="tree" scope="request" class="org.apache.ecs.examples.HtmlTree"></jsp:useBean><%-- Read the path from the request --%>
<jsp:setProperty name="tree" property="path" param="path" /><%-- Set UI properties --%>
<jsp:setProperty name="tree" property="rootVisible" value="true" />
<jsp:setProperty name="tree" property="indentation" value="2" />
<jsp:setProperty name="tree" property="openIcon" value="open.gif" />
<jsp:setProperty name="tree" property="closedIcon" value="closed.gif" />
<jsp:setProperty name="tree" property="leafIcon" value="leaf.gif" /><%-- Workaround for Netscape and Opera browsers --%>
<jsp:setProperty name="tree" property="action" value="tree.jsp" />...template text...
<%-- Get the XHTML output from the bean --%>
<jsp:getProperty name="tree" property="html" />
| Field Summary | |
protected java.lang.String |
action
Context path of a web resource for the workaround of a strange behaviour of Netscape and Opera browsers. |
protected java.lang.String |
closedIcon
Icon for displaying closed nodes. |
protected javax.swing.tree.DefaultMutableTreeNode |
displayNode
Stores the requested node to be displayed. |
protected int |
indentation
Number of blank spaces around each hierarchical level will be indented in relation to its parent node. |
protected java.lang.String |
leafIcon
Icon for displaying leafs. |
protected java.lang.String |
openIcon
Icon for displaying open nodes. |
protected java.lang.String |
parameterName
Parameter name used to build the query string for the anchor that acts as the Controller for opening and closing nodes. |
protected java.lang.String |
path
Path to the requested node to be displayed, described as colon separated integer values. |
private int[] |
pathArray
Array that stores the index of each node read from the path property. |
protected javax.swing.tree.DefaultMutableTreeNode |
root
The node that defines the tree displayed by this object. |
protected boolean |
rootVisible
True if the root node is displayed, false if its children are the highest visible nodes. |
| Constructor Summary | |
HtmlTree()
Returns a HtmlTree with a sample model. |
|
HtmlTree(javax.swing.tree.DefaultMutableTreeNode root)
Returns a HtmlTree with the specified DefaultMutableTreeNode as its root. |
|
HtmlTree(javax.swing.tree.DefaultTreeModel model)
Returns a HtmlTree with the specified DefaultTreeModel as its model. |
| Method Summary | |
static java.lang.String |
encodeToHtml(java.lang.String in)
Returns the specified string encoded into a format suitable for HTML. |
protected static javax.swing.tree.DefaultMutableTreeNode |
getDefaultTreeModel()
Creates and returns a sample tree model. |
java.lang.String |
getHtml()
Returns the hierarchical structure described in the specified root node as valid XHTML. |
java.lang.String |
getImg(java.lang.String icon)
Constructs a valid XHTML <img>-tag from the specified icon. |
int |
getIndentation()
Returns the number of blank spaces around each hierarchical level is indented in relation to its parent node. |
javax.swing.tree.DefaultTreeModel |
getModel()
Returns the model that defines the tree displayed by this object. |
java.lang.String |
getNodeName()
Returns the name of the currently expanded node. |
java.lang.String |
getParameterName()
Returns the name of the parameter used to build the query string for the anchor that acts as the Controller for opening and closing nodes. |
java.lang.String |
getPath()
Returns the path to the requested node. |
java.lang.String |
getPath(int level)
Returns a path constructed from the path array for the specified level. |
static int[] |
getPathResolved(java.lang.String path)
Returns an array that stores the index of each node read from the specified path. |
javax.swing.tree.DefaultMutableTreeNode |
getRequestedNode()
Expands the tree by following the specified path and returns the requested node to be displayed. |
javax.swing.tree.DefaultMutableTreeNode |
getRoot()
Returns the node that defines the tree displayed by this object. |
int |
getRowCount()
Returns the number of rows that are currently being displayed. |
boolean |
isRootVisible()
Returns true if the root node of the tree is displayed. |
void |
setAction(java.lang.String action)
Workaround of a strange behaviour of Netscape and Opera browsers. |
void |
setClosedIcon(java.lang.String closedIcon)
Sets the icon for displaying closed nodes. |
void |
setIndentation(int indentation)
Sets the number of blank spaces around each hierarchical level will be indented in relation to its parent node. |
void |
setLeafIcon(java.lang.String leafIcon)
Sets the icon for displaying leafs. |
void |
setModel(javax.swing.tree.DefaultTreeModel model)
Sets the tree model that will provide the data. |
void |
setOpenIcon(java.lang.String openIcon)
Sets the icon for displaying open nodes. |
void |
setParameterName(java.lang.String parameterName)
Sets the name of the parameter used to build the query string for the anchor that acts as the Controller for opening and closing nodes. |
void |
setPath(java.lang.String path)
Sets the path - described as colon separated integer values - to the requested node to be displayed. |
void |
setRoot(javax.swing.tree.DefaultMutableTreeNode root)
Sets the root node that will provide the data. |
void |
setRootVisible(boolean rootVisible)
Determines whether or not the root node is visible. |
java.lang.String |
toString()
Overrides toString() to print something meaningful. |
| Methods inherited from class java.lang.Object |
|
| Field Detail |
protected int indentation
protected java.lang.String path
protected java.lang.String parameterName
private int[] pathArray
protected java.lang.String leafIcon
protected java.lang.String openIcon
protected java.lang.String closedIcon
protected java.lang.String action
protected boolean rootVisible
protected javax.swing.tree.DefaultMutableTreeNode root
protected javax.swing.tree.DefaultMutableTreeNode displayNode
| Constructor Detail |
public HtmlTree()
public HtmlTree(javax.swing.tree.DefaultMutableTreeNode root)
root
- a DefaultMutableTreeNode objectpublic HtmlTree(javax.swing.tree.DefaultTreeModel model)
model
- a DefaultTreeModel object| Method Detail |
public void setRoot(javax.swing.tree.DefaultMutableTreeNode root)
root
- a DefaultMutableTreeNode objectpublic javax.swing.tree.DefaultMutableTreeNode getRoot()
public void setModel(javax.swing.tree.DefaultTreeModel model)
model
- a DefaultTreeModel objectpublic javax.swing.tree.DefaultTreeModel getModel()
public void setPath(java.lang.String path)
path
- the path to the requested node to be displayedpublic java.lang.String getPath()
public java.lang.String getPath(int level)
level
- the distance from the node to the root node.public static int[] getPathResolved(java.lang.String path) throws java.lang.NumberFormatException
java.lang.NumberFormatException
- if the specified
path does not consist of colon separated integer valuespublic void setParameterName(java.lang.String parameterName)
parameterName
- the name of the parameter used
to build the query stringpublic java.lang.String getParameterName()
public void setRootVisible(boolean rootVisible)
rootVisible
- true if the root node of the tree
is to be displayedpublic boolean isRootVisible()
public void setIndentation(int indentation)
indentation
- the number of blank spacespublic int getIndentation()
public java.lang.String getNodeName()
public int getRowCount()
public void setOpenIcon(java.lang.String openIcon)
openIcon
- the URI of an image filepublic void setClosedIcon(java.lang.String closedIcon)
closedIcon
- the URI of an image filepublic void setLeafIcon(java.lang.String leafIcon)
leafIcon
- the URI of an image filepublic void setAction(java.lang.String action)
action
- the context path of a web resource (e.g. a JSP-File)public java.lang.String getImg(java.lang.String icon)
icon
- the URI of an image filepublic javax.swing.tree.DefaultMutableTreeNode getRequestedNode()
public java.lang.String getHtml()
A sample Style Sheet would look like this:
a { color: black; text-decoration: none; }
a:hover { color: black; text-decoration: underline; }
img { margin-right: 5px; vertical-align: middle; border: none; }
table { font: normal 8pt Arial,Helvetica,sans-serif; }
td.parent { font-weight: bold; }
public java.lang.String toString()
toString
in class java.lang.Object
getHtml()
protected static javax.swing.tree.DefaultMutableTreeNode getDefaultTreeModel()
public static java.lang.String encodeToHtml(java.lang.String in)
in
- the String to encode
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Color is definded as:
Green
is public parking usage
Blue
is traffic jams
Black
is the amount of air pollution
Red
is the noise level
Severity is defines as:
The higher the ballon is in the air, the higher the level is
Double click anywhere on map to remove this information box
LOADING
Please choose a city
Aarhus
Copenhagen
Odense
Ry
Ringe
Public Parking
Traffic Jam
Pollution
Noise
Building Energy


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| LoggerTreeItem (Logger::Log &log, LoggerTreeItem *parent) |
| |
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const QString & | getName () const |
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const QString & | getFullName () const |
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QVariant | getData (int column, int role) const |
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int | getIndex () const |
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bool | canExpand () const |
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int | getInsertionPoint (const QString &name) const |
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void | insertChild (int row, LoggerTreeItem *child) |
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LoggerTreeItem * | getChild (int row) const |
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int | getNumChildren () const |
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LoggerTreeItem * | getParent () const |
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ChildrenIterator | getChildrenIterator () const |
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Logger::Log & | getLog () const |
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void | dump (std::ostream &os, int indent) const |
| |
Definition at line 12 of file LoggerTreeItem.h.
The documentation for this class was generated from the following files:


#include <basic3d.h>
Inheritance diagram for Basic3D:
Public Member Functions | |
| virtual void | Draw () |
| Sends object to draw queue. | |
| virtual void | UpdateTransform () |
| Update object result transform matrix. | |
| virtual Matrix4fx | GetResultTransform () |
| Returns object result transform matrix. | |
| virtual Int | GetClassID () |
| Returns Basic3D class id. | |
| DWord | GetFlags () |
| Returns control Basic3D object flags. | |
| void | SetFlags (DWord flags_) |
| Sets control Basic3D object flags. | |
| Bool | CheckFlag (Int f) |
| Checks one specified control Basic3D object flag. | |
| void | SetFlag (Int f) |
| Set one specified control Basic3D object flag. | |
| void | ClearFlag (Int f) |
| Clears one specified control Basic3D object flag. | |
| Bool | IsVisible () |
| Check is object visible. Not visible object will be not rendered. | |
| void | SetVisible (Bool visible) |
| Set object visible flag. | |
| const string & | GetName () |
| Returns ASCII name for this object. | |
| ObjRef< Basic3D > | GetParent () |
| Returns pointer to parent object. | |
| Int | GetNodeID () |
| Returns object node id. | |
| void | SetName (const Char *name_) |
| Sets ASCII name for this object. | |
| void | SetParent (ObjRef< Basic3D > parent_) |
| Sets pointer to parent object. | |
| void | SetNodeID (Int node_id_) |
| Set object node id. | |
| virtual void | SetResultTransform (const Matrix4fx &rm) |
| Sets object's result transform matrix. | |
| Render3D * | GetRender () |
| Returns pointer to Render3D object. | |
Static Public Member Functions | |
| static ObjRef< Basic3D > | New () |
| Creates new Basic3D. | |
| static ObjRef< Basic3D > | New (Render3D *render_) |
| Creates new Basic3D. | |
Public Attributes | |
| CustomData * | cd |
| Store custom data exported from 3D Max. | |
Protected Member Functions | |
| Basic3D () | |
| Default constructor. | |
| Basic3D (Render3D *render_) | |
| Constructor with given render pointer value. | |
| virtual | ~Basic3D () |
| Destructor. | |
Protected Attributes | |
| DWord | flags |
| Control Basic3D object flags. | |
| string | name |
| Object's name. | |
| ObjRef< Basic3D > | parent |
| Parent object. | |
| Int | node_id |
| Node ID exported from 3D Max. | |
| Render3D * | render |
| Pointer to the Render3D class object. |
|
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Constructor with given render pointer value.
|
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Checks one specified control Basic3D object flag.
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|
Clears one specified control Basic3D object flag.
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Returns Basic3D class id.
Reimplemented in Actor3D, Actor3DNode, DummyBox, DummySphere, Joint3D, Joint3DNode, Light, Object3D, Robot3D, and Sprite3D. |
|
|
Returns control Basic3D object flags. By default only Visible flag is set.
|
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|
Returns ASCII name for this object.
|
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|
Returns object node id.
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|
Returns pointer to parent object.
|
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|
Returns pointer to Render3D object.
|
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|
Returns object result transform matrix.
Reimplemented in Actor3D, Actor3DNode, DummyBox, DummySphere, Joint3D, Light, Object3D, and Robot3D. |
|
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Check is object visible. Not visible object will be not rendered.
|
|
|
Creates new Basic3D. Call this function instead constructor calling.
Reimplemented in Actor3D, Actor3DNode, Joint3D, Joint3DNode, Light, Object3D, Robot3D, and Sprite3D. |
|
|
Creates new Basic3D. Call this function instead constructor calling.
Reimplemented in Actor3D, Actor3DNode, DummyBox, DummySphere, Joint3DNode, Light, Object3D, Robot3D, and Sprite3D. |
|
|
Set one specified control Basic3D object flag.
|
|
|
Sets control Basic3D object flags. Warning! Sixteen low bits reserved for system.
|
|
|
Sets ASCII name for this object.
|
|
|
Set object node id.
|
|
|
Sets pointer to parent object.
|
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|
Sets object's result transform matrix.
|
|
|
Set object visible flag.
|
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|
Update object result transform matrix. Need to call after object matrix transform update. Reimplemented in Actor3D, Actor3DNode, Joint3D, Light, Object3D, and Robot3D. |


If checked, Jenkins will not automatically archive all artifacts
of a maven site (target/site, generated by
mvn site). If you wish to archive the results of this build
within Jenkins, you will need to use the "Archive the artifacts"
post-build action below.


REMEMBER THE TITANS (2000) / ***
Directed by Boaz Yakin. Screenplay by Gregory Allen Howard. Starring Denzel Washington, Will Patton, Hayden Panettiere. Running time: 121 minutes. Rated PG by the MFCB. Reviewed on November 11th, 2000.
By SHANNON PATRICK SULLIVAN
"Remember The Titans" is neither a great sports movie, nor a great movie about race relations. Like Denzel Washington's last based-on-reality movie, "The Hurricane", "Remember The Titans" is a simplified Hollywoodisation of true-life events, prettied up for the big screen. But unlike "The Hurricane", "Titans" manages to maintain its integrity throughout the proceedings, resulting in a superior overall product which, while not the Oscar contender some have touted, is certainly worth the price of admission.
The setting is Alexandria, Virginia, in 1971. Due to the racial integration of the formerly all-white high school where he coaches football, Bill Yoast (Will Patton) has been demoted to serve under a new black coach, Herman Boone (Denzel Washington). He stays on only to ensure that his white players do not boycott the season, thereby ruining their college eligibility. Boone, meanwhile, has been installed purely as a public relations move, with the school board ready to yank him out of his job as soon as his team -- the Titans -- loses so much as one game.
The result is a series of fairly predictable set pieces. There are the two leaders of their respective group of players -- Gerry Bertier (Ryan Hurst) amongst the white students and Julius Campbell (Wood Harris) amongst the black -- who are first enemies, then grudging allies. There is the new white player who does not subscribe to Alexandria's racial turmoil, and the veteran Titan who refuses to accept the integrated team. There is the inevitable growth of respect between Boone and Yoast, building up to a crucial moment in the team's season. And, of course, there is the football aspect of the film which, unsurprisingly, all hinges on a final climactic game.
There is little new in "Remember The Titans" -- familiar buttons are pushed both in terms of the race plotline and the football story -- but it is at least handled well by screenwriter Gregory Allen Howard. Howard keeps the tone of the story light without sugarcoating the racial tension on which it hinges. There is irrefutable unpleasantness here, and not all of it is resolved comfortably; consider a scene in which one of the white players new to Alexandria convinces two of his black teammates to come into a restaurant which refuses to serve African-Americans. But the indisputable thrust of the film is toward racial harmony, and the final outcome is sure to leave audiences smiling as they exit the theatre. "Remember The Titans" is a feel-good movie in every respect, and serious scenes such as the preceding quickly give way to more upbeat fare.
Fortunately, even this material is well-scripted and amusing, such as an increasingly hilarious game of one-upsmanship in the Titans' locker room. Howard also mixes in some genuinely intriguing and moving subplots, including one in which Bertier must choose between his football team and his girlfriend (Kate Bosworth), who cannot bring herself to accept her black schoolmates. Howard and director Boaz Yakin are fortunately able to tone down the unavoidable schmaltz factor, keeping such scenes as the resolution to the Bosworth subplot pointed and underplayed.
Yakin handles the directorial reins capably, and the material on the football field is executed with particular skill, neither belabored nor difficult to follow. He demonstrates wisdom in not exaggerating scenes unnecessarily; a sequence at Gettysburg, for example, could have been milked for all the patriotic, egalitarian fervour it was worth, but instead Yakin keeps it subdued, almost subtle.
He is aided in this endeavour by both his lead actors, who concentrate on their characters as people rather than representatives of societal ideologies. There is an excellent scene toward the start of the movie where Boone refuses to take Yoast's job away from him, because that sort of thing was done to him at his old school. But when a throng of African-Americans shows up at his doorstep to cheer him on in his new position, Boone is forced to change his mind and coach the team despite his personal preferences. Boone is not a noble campaigner for black rights, but rather a black man who finds such a role thrust upon him. Washington delivers a very even performance, accepting both Boone's defects and his virtues.
Similarly, Patton plays Yoast as neither a stubborn redneck nor a bleeding-heart liberal. His transition from being passively against the integration of his high school to learning to respect African-Americans is handled smoothly and without show. For example, Yoast's decision to let his daughter Sheryl (Hayden Panettiere) stay at the Boones' house is not treated as an earthshattering event, but is instead relayed almost off-handedly, in a conversation with another coach. Indeed, Patton's performance may eclipse even Washington's. Although Washington handles his part with unsurprising skill, Patton has to convey a far greater range, as Yoast makes the gradual transition from (broadly speaking) villain to hero.
Yakin coaxes workmanlike portrayals from the rest of his cast, who are mostly stuck in typical, formulaic roles: the jovial fat guy, the jock, the religious guy, and so forth. "Remember The Titans" is sadly unambitious on this front. Very little true conflict is uncorked between the characters, and is instead left to fester mostly with briefly-appearing cameo parts -- a corrupt referee, a local Hall of Fame president, the aforementioned restaurant owner. When there is disagreement amongst the Titans, it is, in general, easily dealt with.
This leaves the impression that bringing the team together wasn't much work at all. This was likely not Yakin and Howard's intent, as it somewhat undermines the tension of the film's opening hour. Indeed, viewers are left wondering just why it took the rest of Alexandria so long to follow suit. And while, in principle, this is a very valid question, the sad truth is that reality is rarely so accommodating, and I expect the real Titans did not come together quite as smoothly as this depiction would suggest. It only works that way in the movies.
Copyright © 2000 Shannon Patrick Sullivan. Archived at The Popcorn Gallery, http://www.physics.mun.ca/~sps/movies/RememberTheTitans.html
_______________________________________________________________________ / Shannon Patrick Sullivan | "We are all in the gutter, but some of us \ |<EMAIL_ADDRESS>| are looking at the stars." - Oscar Wilde | \___________________________|__________________________________________/ | Popcorn Gallery Movie Reviews www.physics.mun.ca/~sps/movies.html | | Doctor Who: A Brief History of Time (Travel) /drwho.html |
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Welcome to the second issue of SteemSTEM Distilled, a curation effort by the members of the @steemstem team.
SteemSTEM is a community driven project which seeks to promote well-written and informative Science, Technology, Engineering and Mathematics posts on Steemit. The project involves curating STEM-related posts through upvoting, resteeming, offering constructive feedback, supporting scientific contests, and other related activities.
For more details click here.
Tuesday - Misconception 8: Caesarian C-Section Is Named After Julius Caesar
Wednesday - Salads Under Siege, Episode 3: The Pathogen Running Game
Thursday - Does Aluminum as an adjuvant in vaccines cause Autism? New article provides solid evidence it does! (Spoiler: No, it doesn’t)
Friday - Egyptology NEWS: This is huge - and the 3D pharaoh puzzle goes on!
Saturday - Sweat & Antiperspirants - The Chemistry
Sunday - Scotch Tape, Scientists and Dermatoscopy
For the sake of transparency we feel we need to clarify how our nomination process works; each issue is compiled by certain members of the SteemSTEM community. This week’s editorial team was comprised of @abigail-dantes, @ruth-girl, @trumpman and @zest. A special mention goes to @locikll for helping with the markdown coding.
The editorial team chooses their favorite posts for the week and each issue is the result of their joined effort. In order to keep the process fair, posts written by the editorial team are excluded from the nomination process.
Don’t be discouraged if you didn’t make it on this issue. The competition is harsh and there is always a next time :) In the meantime, you may wish to check these guidelines for extra tips on how your writing can be more effective.
In the same spirit of being transparent about our actions that are now supported by @curie, we will release every week a list of authors who have caught the attention of the @steemstem curation team.
If you want to discuss this with us, please do not hesitate to join the steemSTEM channel on the Steemit chat or to follow our curation trail on Streemian. If you are not on Streemian, we have a plan B through a homemade voting bot. You can contact us on the chat in order to be added to its trail.
On week-37, from September 13th to September 17th, we rewarded 53 different authors who have written 90 different articles. The authors who received both a @steemstem and a @curie vote that week are:
@amirul, @anneke, @ariannavassalla, @asbonclz, @bluemist, @braini, @cabbagepatch, @carlos-cabeza, @cjclaro, @cjrc97, @cryptomillion, @dber, @eco-alex, @getonthetrain, @harshallele, @himal, @imjoker, @jakipatryk, @jamhuery, @janusface, @jefpatat, @justyy, @jvalentine, @kerriknox, @kharrazi, @khatisam4, @libert, @lordkingpotato, @mcfarhat, @mcw, @mookie, @mountainwashere, @nedspeaks, @ovij, @ryal, @samminator, @shieha, @spotlight, @stats-n-lats, @steelwings, @sustainablyyours, @sweetpea, @tanyaschutte, @technovedanta, @tradersharpe, @valth, @vlemon
On week-38,from September 17th to September 24th, we rewarded 73 different authors who have written 152 different articles. The authors who received both a @steemstem and a @curie vote that week are:
@alexander.alexis, @alishi, @amyf, @andreajane, @anneke, @asbonclz, @astronomyizfun, @azirgraff, @azman, @bape682, @beesteem, @braini, @brotherbear, @cabbagepatch, @cjrc97, @dana-edwards, @dber, @dean101, @ertwro, @fabulousfungi, @faisalking, @galotta, @gauss01, @ghostgtr, @hansenator, @herpetologyguy, @himal, @homes, @huitemae, @jamhuery, @joha09sohi, @jonathanxvi, @joshpeterson, @justyy, @kerriknox, @khansa, @kharrazi, @khatisam4, @kyriacos, @laylahsophia, @littlekookie, @logic, @masterwu, @mawardi, @mcfarhat, @mcw, @mes, @mostly.nature, @mountainwashere, @n3bul4, @natord, @ovij, @paikan, @reddust, @reneenouveau, @safflongtime, @samminator, @shairanada, @simplifylife, @somethingburger, @sustainablyyours, @sward, @sweetpea, @tanata, @thatsweeneyguy, @tinadahms, @titusfrost, @tking77798, @tradersharpe, @valth, @vlemon, @wanderingmind
All curation rewards earned will be used to fund @steemstem project functioning and activities.
See you all next Monday!
This page is synchronized from the post: SteemSTEM Distilled #2


Manual for the Amberbio app
The Amberbio app allows scientists to analyze and visualize data sets, in particular biological data, on mobile devices. All calculations are performed directly on the device and all data is stored locally. The functionality should be evident from the user interface and it is not necessary to read the manual to use the app. The manual gives a high level overview of the app and explains some important details that are not clear from the user interface such as some mathematical details.
This manual is available in the app and on the web at www.amberbio.com/manual.
Table of contents
Version
Version 4 of the Amberbio app.
Privacy
All data is stored locally on the device. All calculations are performed locally. Amber Biosciences does not have any access to user data.
Projects and data sets
The app handles projects and data sets. A data set is a set of values organized in a table with rows and columns. A project is a collection of one or more data sets. A project is always created with a data set called "The original data set". Other data sets within a project are created by the app using various methods such as normalization or sample removal.
The columns of a data set are called samples and the rows are called molecules. The values in the table represent an intensity, or some other quantity, for a sample-molecule pair. Data sets could represent biological information such as gene expression, protein abundances, microRNA expressions, peptide abundances, or metabolite concentrations. Data sets do not need to be of biological nature even though the app uses terminology from biology.
A project also contains factor, or grouping, information about the samples. A factor has one or more levels. An example of a factor is "Gender" with levels "male" and "female". Naturally, factors are shared for all data sets within a project.
A project can also contain extra information about the molecules. This information is called molecule annotations. An example could be the molecule annotation "Chromosome" where each molecule has a value such as "chromosome 21".
The active data set
Analysis is performed on the active data set. Selection of the active data set is done on the page Data Set Selection. The active project is the project to which the active data set belongs. Editing and adding factors and molecule annotations are done in the active project.
There is almost always an active data set and project. The only exceptions are when the app starts the first time and when the active project is deleted. When a new data set is created, the app automatically makes the new data set active and jumps to the page Data Set Selection
Import of data
Data import is used to create new projects and to add factor and molecule annotation information to projects. Import of data is a two step process. First, a file is imported into the app. Second, the file is read and parsed and the data is imported into a database kept by the app.
Import of data is handled by the page Import data. All imported files can be seen on this page. The app keeps all imported files until they are deleted by the user.
Import of files
The app can import files with extension "txt" or "sqlite". The "txt" files represent tables of data supplied by the user. The "sqlite" is a database file that has been exported from the app at an earlier time. The "sqlite" files are used for backup and transfer of projects from one device to another.
Files can be imported in two ways; either by "opening" the file in another app or by import from a cloud storage.
Importing a file by "opening" it in another app is done by tapping the file in the other app and selecting the Amberbio app. A typical use case is to open an email attachment by tapping the attachment in the Mail app.
Files can be imported from a cloud storage such as iCloud Drive, Dropbox, Box, or Google Drive. The import is done by tapping "Import new file" on the page Import data. The corresponding cloud storage app must itself be installed on the device.
Parsing import files
Imported files are shown on the page Import data. By tapping a file, the file can be parsed and read into the database of the app. Files with extension "sqlite" are read automatically into projects. Files with extension "txt" file can be used to create a new project, to import factors to the active project, or to import molecule annotations to the active project.
File format of txt files
The "txt" files contain tables of values. The rows, or lines, are separated by "\n", "\r", or "\r\n", and the cells are separated by "\t" or ",". The file extension should be "txt" in any case. The app tries to find the separator by first searching for tabs. If there are tabs, the app tries to read the file as a tab separated file. If there are no tabs, the file is read as a comma separated file. Tabs are never allowed within the cells. If there are commas within the cells, the file must be tab separated. The three types of "txt" files are described below.
The first file type contains the measurement values. This file is used to create a new project. It contains samples in the columns and molecules in the rows. The first row contains the sample names. The first column contains the molecule names. The values are written in the cells.
The decimal separator can be either point as in "12.98" or comma as in "12,98". If the decimal separator is comma, the file must be tab separated. Missing values must be written as (the empty string), NA, na, NaN, NAN, or nan. The upper left cell can contain anything. An example value table with two missing values is shown below.
| Anything | Sample 1 | Sample 2 |
| Molecule 1 | 6.7 | |
| Molecule 2 | -0.45 | NaN |
| Molecule 3 | 50 | -12 |
The second file type contains factors for the samples. Factors can be imported into a project at any time and are not part of the initial creation of the project. The first row of the factor file contains sample names. The first column contains factor names. The values are the levels for the factor and sample. The upper left cell can be anything. The factor names must be distinct and the sample names can come in any order. All samples of the project must be present. Extra samples will be ignored. An example factor table is shown below
| Anything | Sample 1 | Sample 2 |
| Gender | Male | Female |
| Disease | Cancer | Alzheimer |
| Age | 55 | 78 |
The third file type contains molecule annotations and it can be imported at any time and is not part of the initial creation of the project. The first row contains the name of the molecule annotation. The first column must contain all the molecule names. The order of the rows is arbitrary and extra molecules are ignored. An example molecule annotation table is shown below
| Anything | Unigene | Chromosome |
| Molecule 7 | Hs.551713 | 18 |
| Molecule 3 | Hs.518267 | 3 |
| Molecule 2 | Hs.300701 | 6 |
| Molecule 1 | Hs.691131 | 10 |
Result files
Result files are created by the app. The result files are figures and tables and have the file extension "txt", "pdf", or "png". The result files are kept on the page Result files. The result files can be sent by email, opened in another app, or exported to a cloud storage.
Backup and sharing of projects
Projects can be exported to a database file on the page Export projects. The database file has the extension "sqlite". The file can be used for backup and transfer of projects to other devices. The file can be sent by email or exported to a cloud storage.
Name and emails
On the page User, a name and a list of emails can be typed. There are no user accounts in the app. The name is only used for comments in the result files and the project notes. The emails are used as suggested emails when a file is sent by email from the app. The actual destination emails can always be changed before sending the email.
Anova
The Anova test is performed on the selected levels of a factor. The selected levels are highlighted, and the anova test is performed by tapping the blue factor name. For each molecule a standard Anova test is performed by removing samples with missing values, calculating the F-statistic as the ratio of between-groups variation divided by the within-groups variation. The p-value is the upper tail of the cumulative F-distribution.
Pairwise test
After selecting a factor, any number of pairwise tests between two levels can be selected in the table. The pairwise test will be performed on all pairs and presented in a table. The t-test is a standard student t-test with equal variance. The p-value from the Wilcoxon test, which is the same as the Mann-Whitney test, is calculated as an exact value for small sample size and by the Normal distribution approximation for large sample sizes. The p-values are two-sided.
Paired test
A pairing factor must be selected. Samples with the same level for the pairing factor can be paired. An example of a pairing factor is "Patient" and the levels are the patient ids or names. For each patient, two or more samples are measured.
The comparison factor is the factor for which the levels will be compared. When two levels of the comparison factor are compared, the pairs of samples are defined as follows. For each pairing factor level, there must be exactly one sample for each of the two comparison levels with that particular pairing factor level. An example of a comparison factor is "Time" with the levels "morning" and "evening". In this case, a pairwise test will be performed between the evening and morning samples for each patient.
The t-test is performed by subtracting the values of the paired samples in the two selected comparison levels. The p-value is two-sided and tests the null hypothesis that the difference between the comparison levels is zero.
Linear regression
For the selected factor, the levels with a numeric interpretation are chosen. A level can be converted to a number if it starts with, or is, a numeric values, such as "5 days" or "12.3". For each molecule, the samples with a numeric level and a non-missing value are used for the linear regression. The intercept and slope are calculated by least square regression.
The p-value is an Anova p-value and tests the null-hypothesis that the slope is zero, i.e. low p-values imply that there is a trend with a non-zero slope. The Anova test is performed by calculating a F-statistic as the ratio where the numerator is the variation between the line and a simple mean value, and the denominator is the residual variation from the fitted line. A large F-statistic, and correspondingly low p-value, implies that the line is a much better fit than a single mean value.
Multiple hypothesis testing
Multiple hypothesis testing is performed using the Benjamini-Hochberg false discovery rate method. The columns named false discovery rate contains the q-values of the molecules.
A histogram of the p-values can be seen by tapping "histogram". High frequencies for the low p-values imply a significant difference between the groups.
Supervised classification
The supervised classifiers separate samples into two or more levels. A classifier is trained on a training set and afterwards tested on a test set.
There are three modes in which the classifiers in the app can be used; a fixed training set, leave-one-out cross validation, and k-fold cross validation.
When a fixed training set is chosen, the classifier is tested on all remaining samples, both those with actual levels matching the levels of the classifier and those with actual levels different from the levels known to the classifier. The latter type of samples can not be used to estimate the predictive power of the classifier. However, often it is still useful to know the predicted levels of these samples. For instance, if the classifier has the levels "sick" and "healthy", it might be useful to classify "borderline" samples into either "healthy" or "sick".
For leave-one-out cross validation, each sample is left out and tested with the remaining samples as the training set. In that way, each sample obtains one classified level which can be compared to the real level. Leave-one-out classification only uses the samples with levels known to the classifier.
For k-fold cross validation, the samples are divided randomly into k subsets of almost equal size; if the number of samples is not divisible by k, some subsets will be one sample larger than other subsets. Each subset is tested on a classifier trained by the remaining samples. In that way, each sample obtains one classified level which can be compared to the real level. Cross validation only uses the samples with levels that are known to the classifier.
In the special case where k is equal to the number of samples, k-fold cross validation is equal to leave-one-out cross validation. Leave-one-out cross validation has its own selection in the app because of it importance.
k nearest neighbor classification
Given a training set and a test sample, the k training samples with the smallest distances to the test sample are found. The levels of these k nearest samples are considered, and if there is a single level that constitutes a majority of the k samples, that level is chosen as the predicted level for the test sample. If no level obtains a majority alone, the test sample is considered unclassified. If the classifier has only two levels and k is odd, there is always one level with a majority. In other cases, samples might be unclassified.
The distance measure is euclidean distance. Only molecules without missing values in the combined training set and test set are included in the distance calculation.
In case of ambiguity in the k nearest neighbors due to ties in the distances, the classifier makes an arbitrary, but non-random, choice. This situation practically never occurs in real biological data.
Support vector machine classification
The support vector machine (SVM) classification uses the software library LIBSVM: "Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011". The LIBSVM software is available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. The Amberbio app uses the LIBSVM library to train an SVM classifier and calculate the decision values and predicted classes for test samples.
Copyright for LIBSVM
Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither name of copyright holders nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

The SVM classifier in the app can compare two or more levels. A standard SVM classifier can directly classify two levels. Comparisons between K levels are performed by the LIBSVM library by performing all K(K - 1)/2 pairwise comparisons and selecting the level that wins most pairwise comparisons.
It is recommended to use logarithmic values for the SVM classifier. No scaling or other pre-processing is performed by the app.
For the binary classifier (two levels), a decision value is calculated. Samples with positive decision values are classified in one level and those with negative decision values in the other level. Using a variable threshold instead of a fixed zero threshold, a whole curve of classifiers is obtained. Plotting the true positive rate versus the false positive rate for variable thresholds leads to the receiver operating characteristic (ROC) curve. The area under the curve is a measure of the success of the classifier. Good classifiers have areas close to 1, whereas bad classifiers have areas close to 0.5.
The ROC curve only applies to the binary classifier. Call the two levels A and B. The true positive rate is defined as the fraction of samples with an actual level of A that are predicted to have level A. The false positive rate is defined as the fraction of samples of level B that are classified as level A. The true positive rate is also called the sensitivity. The false positive rate is equal to 1 - specificity.
The kernels are the linear kernel and the radial basis function (RBF) kernel of Gaussian functions. The parameter C is the coefficient of the error term in the optimizing function. The parameter gamma is the scale parameter in the exponent of the Gaussian function. A proper explanation can be found in any article or book about support vector machines. The app uses the same terminology as LIBSVM.
The linear kernel with default parameter is usually a good choice for biological data with many molecules and relatively few samples.
Only molecules without missing values in the training and test sets are used for the SVM.
k means clustering
K means clustering is an unsupervised clustering algorithm that divides the samples into k groups. The number of clusters, k, is user determined. The algorithm employed by the app is probabilistic and might give different results for several runs on the same data set. However, the results will be similar, and the differences are explained by movement of samples whose group membership is ambiguous. Since there is no unique biologically correct clustering in any case, the probabilistic nature of the algorithm is acceptable.
The algorithm is almost identical to the standard Lloyd algorithm. The steps are described below.
- Iterate over the entire cluster algorithm below, and take the clustering with the smallest sum of square deviation as the final result. The number of iterations is dynamic and depends on the duration for one iteration. The app should never become unresponsive by being stuck in a long computation.
-
- Assign the samples to random clusters.
- Resolve empty clusters by moving samples from the largest cluster to empty clusters.
- Iterate the following steps until the clusters are constant or a maximum limit is reached. In practice, the maximum limit is almost never needed.
-
- Calculate centroids as the average point in each cluster.
- Reassign samples to clusters. A sample belongs to the cluster whose centroid is closest.
- Resolve empty clusters by moving samples from the largest cluster to the empty clusters.
- Calculate the sum of square deviation of samples from their cluster centroid.
Sammon map
The Sammon map is a projection of the high dimensional samples to a low dimensional space. The map was invented by Sammon in 1969. (J. W. Sammon jr. A Nonlinear Mapping for Data Structure Analysis. IEEE Transactions on Computers, vol. C-18, no. 5, pp 401-409, May 1969). In this app the low dimensional space is two or three dimensional such that the samples can be visualized. The Sammon map attempts to preserve the pairwise distances between samples as much as possible.
The distances in the high dimensional space are Euclidean distances using only the molecules without missing values for the selected samples. The algorithm is described by by Kohonen ( T. Kohonen. Self Organizing Maps. Springer, 3 edition, 2001) and is iterative. The number of iterations depend on the time of one iteration; the app will never go into a long computation.
At each iteration, the algorithm loops over all pairs of samples and updates the two samples. The update moves the two points directly away from each other if their distance is too small, and moves them towards each other if it is to large. The size of the movement depends on the current distances and a variable multiplier. The multiplier starts at 0.4 and ends at a small value. The multiplier is reduced at each iteration.
The purpose of the Sammon map is to obtain a visualization of the samples and hopefully gain some biological insight. The Sammon map is an alternative to the PCA map. The Sammon map has the property that samples close to each other remain close after the projection which is not guaranteed by the PCA map.


| Percent | Status | TotalTests | Passed | Failed | Inconclusive | TimeTaken |
|---|---|---|---|---|---|---|
| 8 | 1 | 6 | 0 |
| TestClasses Summary | Percent | Status | TestsPassed | TestsFailed | TestsIgnored | Duration |
|---|---|---|---|---|---|---|
| SageframeTest.DeleteRole | ||||||
| SageframeTest.UserSettings | ||||||
| SageframeTest.UserSearch | ||||||
| SageframeTest.DeleteUser | ||||||
| SageframeTest.AddMessageTemplate | ||||||
| SageframeTest.AddRoles | ||||||
| SageframeTest.UserEdit | ||||||
| SageframeTest.AddUsers |
| SageframeTest.DeleteRole | |||
|---|---|---|---|
| Can_DeleteRole |
n |
Test method SageframeTest.DeleteRole.Can_DeleteRole threw exception: OpenQA.Selenium.NoSuchElementException: Could not find element by: By.Id: ctl16_lblUdpSageMesageCustom |
00:00:04.2279939 |
| SageframeTest.UserSettings | |||
|---|---|---|---|
| Can_saveUserSettings |
n |
Test method SageframeTest.UserSettings.Can_saveUserSettings threw exception: OpenQA.Selenium.WebDriverException: Unexpected error. System.Net.WebException: Unable to connect to the remote server ---> System.Net.Sockets.SocketException: No connection could be made because the target machine actively refused it <IP_ADDRESS>:7055 at System.Net.Sockets.Socket.DoConnect(EndPoint endPointSnapshot, SocketAddress socketAddress) at System.Net.ServicePoint.ConnectSocketInternal(Boolean connectFailure, Socket s4, Socket s6, Socket& socket, IPAddress& address, ConnectSocketState state, IAsyncResult asyncResult, Exception& exception) --- End of inner exception stack trace --- at System.Net.HttpWebRequest.GetRequestStream(TransportContext& context) at System.Net.HttpWebRequest.GetRequestStream() at OpenQA.Selenium.Remote.HttpCommandExecutor.Execute(Command commandToExecute) at OpenQA.Selenium.Firefox.FirefoxDriverCommandExecutor.Execute(Command commandToExecute) at OpenQA.Selenium.Remote.RemoteWebDriver.Execute(String driverCommandToExecute, Dictionary`2 parameters) |
00:00:04.0170917 |
| SageframeTest.UserSearch | |||
|---|---|---|---|
| Can_SearchUser |
n |
Test method SageframeTest.UserSearch.Can_SearchUser threw exception: OpenQA.Selenium.WebDriverException: Unexpected error. System.Net.WebException: Unable to connect to the remote server ---> System.Net.Sockets.SocketException: No connection could be made because the target machine actively refused it <IP_ADDRESS>:7055 at System.Net.Sockets.Socket.DoConnect(EndPoint endPointSnapshot, SocketAddress socketAddress) at System.Net.ServicePoint.ConnectSocketInternal(Boolean connectFailure, Socket s4, Socket s6, Socket& socket, IPAddress& address, ConnectSocketState state, IAsyncResult asyncResult, Exception& exception) --- End of inner exception stack trace --- at System.Net.HttpWebRequest.GetRequestStream(TransportContext& context) at System.Net.HttpWebRequest.GetRequestStream() at OpenQA.Selenium.Remote.HttpCommandExecutor.Execute(Command commandToExecute) at OpenQA.Selenium.Firefox.FirefoxDriverCommandExecutor.Execute(Command commandToExecute) at OpenQA.Selenium.Remote.RemoteWebDriver.Execute(String driverCommandToExecute, Dictionary`2 parameters) |
00:00:07.0265082 |
| SageframeTest.DeleteUser | |||
|---|---|---|---|
| Can_DeleteUser |
n |
00:00:08.1634173 |
| SageframeTest.AddMessageTemplate | |||
|---|---|---|---|
| Can_AddMessageTemplate |
n |
Test method SageframeTest.AddMessageTemplate.Can_AddMessageTemplate threw exception: OpenQA.Selenium.NoSuchElementException: Unable to locate element: {"method":"id","selector":"ctl19_pnlMessageTemplateList"} |
00:00:16.9524963 |
| SageframeTest.AddRoles | |||
|---|---|---|---|
| Can_AddRole |
n |
Assert.IsTrue failed. |
00:00:13.5891662 |
| SageframeTest.UserEdit | |||
|---|---|---|---|
| Can_EditUserProfile |
n |
Test method SageframeTest.UserEdit.Can_EditUserProfile threw exception: OpenQA.Selenium.WebDriverException: Unexpected error. System.Net.WebException: Unable to connect to the remote server ---> System.Net.Sockets.SocketException: No connection could be made because the target machine actively refused it <IP_ADDRESS>:7055 at System.Net.Sockets.Socket.DoConnect(EndPoint endPointSnapshot, SocketAddress socketAddress) at System.Net.ServicePoint.ConnectSocketInternal(Boolean connectFailure, Socket s4, Socket s6, Socket& socket, IPAddress& address, ConnectSocketState state, IAsyncResult asyncResult, Exception& exception) --- End of inner exception stack trace --- at System.Net.HttpWebRequest.GetRequestStream(TransportContext& context) at System.Net.HttpWebRequest.GetRequestStream() at OpenQA.Selenium.Remote.HttpCommandExecutor.Execute(Command commandToExecute) at OpenQA.Selenium.Firefox.FirefoxDriverCommandExecutor.Execute(Command commandToExecute) at OpenQA.Selenium.Remote.RemoteWebDriver.Execute(String driverCommandToExecute, Dictionary`2 parameters) |
00:00:06.0432252 |
| SageframeTest.AddUsers | |||
|---|---|---|---|
| Can_AddUsers |
n |
UTA007: Method Can_AddUsers defined in class SageframeTest.AddUsers does not have correct signature. Test method marked with the [TestMethod] attribute must be non-static, public, does not return a value and should not take any parameter. for example: public void Test.Class1.Test(). Additionally, return-type must be Task if you are running async unit tests. Example: public async Task Test.Class1.Test2(). |
| TestRun Environment Information | |
|---|---|
| TestCodebase | C:/Users/RajKumar-PC/Documents/Visual Studio 2013/Projects/QASageframe/SageframeTest/bin/Debug/SageframeTest.dll |
| AssemblyUnderTest | |
| MachineName | RAJKUMARTHAPA |
| UserName | RajkumarThapa\RajKumar-PC |
| Original TRXFile | RajKumar-PC@RAJKUMARTHAPA 2015-04-03 15:59:49 |


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Simplified odds ratio calculation of GAM(M)s & GLM(M)s. Provides structured output (data frame) of all predictors and their corresponding odds ratios and confident intervals for further analyses. It helps to avoid false references of predictors and increments by specifying these parameters in a list instead of using 'exp(coef(model))' (standard approach of odds ratio calculation for GLMs) which just returns a plain numeric output. For GAM(M)s, odds ratio calculation is highly simplified with this package since it takes care of the multiple 'predict()' calls of the chosen predictor while holding other predictors constant. Also, this package allows odds ratio calculation of percentage steps across the whole predictor distribution range for GAM(M)s. In both cases, confident intervals are returned additionally. Calculated odds ratio of GAM(M)s can be inserted into the smooth function plot.
| Version: | 1.0.3 |
| Depends: | R (≥ 3.0.0) |
| Imports: | mgcv, MASS, stats, ggplot2 (≥ 3.0.0), cowplot |
| Suggests: | knitr, rmarkdown, grid, gtable, scales, testthat, pacman, gam |
| Published: | 2018-07-25 |
| Author: | Patrick Schratz [aut, cre] |
| Maintainer: | Patrick Schratz <patrick.schratz at gmail.com> |
| BugReports: | https://github.com/pat-s/oddsratio/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/pat-s/oddsratio |
| NeedsCompilation: | no |
| Citation: | oddsratio citation info |
| Materials: | NEWS |
| CRAN checks: | oddsratio results |
| Reference manual: | oddsratio.pdf |
| Vignettes: |
function-tutorial |
| Package source: | oddsratio_1.0.3.tar.gz |
| Windows binaries: | r-devel: oddsratio_1.0.3.zip, r-release: oddsratio_1.0.3.zip, r-oldrel: oddsratio_1.0.3.zip |
| OS X binaries: | r-release: oddsratio_1.0.3.tgz, r-oldrel: oddsratio_1.0.3.tgz |
| Old sources: | oddsratio archive |
Please use the canonical form https://CRAN.R-project.org/package=oddsratio to link to this page.


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each domain above and add your default service account to the list of Verified
Owners: {{ serviceAccount }}
The path /.well-known/acme-challenge/*
is not correctly mapped to
this app.
Make sure your dispatch.yaml
is correct.
| Display name | Domains | Cert ID | Issue | Expiry | Issuer | |
|---|---|---|---|---|---|---|
| {{ cert.DisplayName }} | {{ cert.DomainNames|join:", " }} | {{ cert.ID }} | {{ cert.Issue|date:"2 January 2006" }} | {{ cert.Expiry|date:"2 January 2006" }} | {{ cert.Issuer }} |


{% extends "_layouts/main.html" -%} {% block js %} {% endblock js %} {% block content -%}
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With peak fossil fuel consumption on the horizon, electric cars have been put on the spotlight as a necessary transition to slow this disaster down. Almost 15 percent of all carbon emissions come from cars, so making this number smaller is key in the battle against global warming. The motivation behind each car Volta manufactures is to fuse the comfort expected in a car with a sleek, eco-friendly design. The majority of gasoline - based cars releases around 4.6 metric tons of CO2 whereas Volta’s electric automobiles produce only half as much. With an optimized battery and engine designed to get the most amount out of every resource used, Volta’s cars are the future of transportation.
Volta was the brainchild of four close high school friends - Megan, Brandon, Emily, Subhash - who first met at their school’s TSA chapter. They realized the growing problem of pollution present in the society and decided to collaborate to create Volta. Through the expansion of this company, they hope to spread awareness of environmental concerns and to promote the use of electric cars. The four of them started to brainstorm ideas, meeting up with local car dealerships to discuss the current number of electric cars. With the determination to bring attention to the newer vehicles, they worked hard to develop their own electric models. Today, Volta’s cars can be found across the world, with consumers in more than 100 countries. Together, Volta’s consumers can help clean the environment, one electric car at a time.
Not yet convinced? Browse our selection of world-class,
top-rated vehicles and decide for yourself.