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310-232 - Java EE6 Web services Developer(R) Certified Professional Eaxm - BrainDump Information

Vendor Name : SUN
Exam Code : 310-232
Exam Name : Java EE6 Web services Developer(R) Certified Professional Eaxm
Questions and Answers : 120 Q & A
Updated On : October 18, 2018
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310-232 exam Dumps Source : Java EE6 Web services Developer(R) Certified Professional Eaxm

Test Code : 310-232
Test Name : Java EE6 Web services Developer(R) Certified Professional Eaxm
Vendor Name : SUN
Q&A : 120 Real Questions

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SUN SUN Java EE6 Web

the use of Java DB in computer applications | killexams.com Real Questions and Pass4sure dumps

Articles Index

sun Microsystems recently announced that it's distributing and assisting Java DB in keeping with the 100 percent Java expertise, open-supply Apache Derby database. Derby became previously available under its previous name, Cloudscape, from its former owners: Cloudscape, Informix, and IBM. IBM donated the Derby product supply code to the Apache foundation as an open-supply challenge. sun, IBM, other groups, and people have been actively worried in construction of the relational database as part of the Apache Derby group. sun distributes Java DB in lots of of its products, together with the solar Java commercial enterprise device and the sun Java device utility Server. The NetBeans integrated construction atmosphere (IDE) 5.0 also supports Java DB.

Java DB is light-weight at 2 megabytes and embeddable within laptop Java technology purposes. desktop purposes can now entry powerful database storage with triggers, saved tactics, and assist for SQL, Java DataBase Connectivity (JDBC) utility, and Java Platform, business version (Java EE, formerly called J2EE), all embedded within the identical Java digital machine (JVM). *

this article describes the way to down load, deploy, combine, and deploy Java DB inside computing device Java know-how applications. A demo software known as tackle booklet demonstrates a way to work with Java DB as an embedded database.

Contents

   growing the address publication Demo

The address ebook demo makes use of Java DB to store tackle suggestions. This demo retailers names, telephone numbers, email addresses, and postal addresses. It means that you can create new handle entries and to retailer, edit, and delete them. The software creates its database in the consumer's domestic listing within an .addressbook subdirectory. The database is embedded with the utility, so there is not any deserve to set up or control a separate server or equipment. To deploy this embedded database application, we need simplest the software JAR file and the database library JAR file. determine 1 suggests the demo's user interface (UI).

determine 1: handle ebook uses Java DB as an embedded database.

address e-book's leading body window is an AddressFrame type that extends a Java basis classes/Swing (JFC/Swing) JFrame. The AddressFrame is a container for different graphical add-ons and additionally acts as a controller by using managing various hobbies generated via the child components. The baby components are JPanel subclasses, each with a different accountability:

  • AddressPanel represents an handle record. It also gives the UI for modifying current records and growing new facts. It consists of text fields for the entire predominant homes of an tackle object.
  • AddressActionPanel provides buttons for all the major use situations that the utility helps. This panel generates routine that AddressFrame must address. as an instance, when the person clicks keep, this panel generates an adventure. AddressFrame listens to and handles all important events from this panel.
  • AddressListPanel gives a scrollable checklist of names on the some distance left of the AddressFrame. The record holds ListEntry objects. A ListEntry retailers a database record's exciting identifier. The listing identifier (identification) permits the application to retrieve a complete list's contents into the AddressPanel.
  • The application uses an information access Object (DAO) to isolate the database-certain code. The DAO encapsulates database connections and statements. A DAO is a useful design sample that makes it possible for unfastened coupling between an application and the underlying persistence-storage mechanism. The software's AddressDao category is an illustration of a DAO. When the AddressFrame edits, saves, or deletes an address object, it all the time uses an instance of the AddressDao class. despite the fact the handle booklet software uses Java DB, you might alternate it to use a completely different database simply with the aid of modifying this one category.

    The easiest way to get Java DB is to download a replica from the solar Developer community's Java DB website. The binary distribution offers every little thing you need to start working with embedded database applications. After you down load the binary distribution, you will find a Java DB listing structure that contains the following subdirectories:

  • The demo subdirectory has two demonstration programs. One example indicates a way to create a trivial embedded utility. The different suggests a way to use Java DB in a client-server atmosphere.
  • The frameworks subdirectory includes utilities for establishing ambiance variables and for creating and working with databases. This directory is not helpful for our demo because our utility will be utterly self-contained. No outdoor utilities should be used for the tackle publication utility.
  • The javadoc subdirectory includes API documentation. This directory is primarily constructive if you configure your IDE to factor to it because the Java DB API Javadoc.
  • The doctors subdirectory incorporates documentation about the Java DB product itself: setup, administrator, and reference guides.
  • ultimately, the lib subdirectory incorporates the Java DB libraries packaged as JAR data. read the Java DB documentation to discover about the a number of libraries. For an embedded database utility, we can use only the derby.jar library file.
  • installation Java DB for construction requires handiest that you simply make the derby.jar file part of your application classpath. it be that standard. you can set the CLASSPATH variable of your Solaris, Linux, home windows, or other host ambiance to consist of the JAR file, otherwise you can include the file as part of your command-line alternatives when compiling or operating. The tackle ebook demo's ANT script suggests you a way to encompass this JAR file all through challenge building if you use ANT. however, some IDEs, together with NetBeans IDE 5.0, can help you configure classpaths.

    Integrating Java DB With NetBeans IDE 5.0

    Most IDEs give a means so as to add libraries to the development classpath. observe these guidance to add the Java DB libraries to NetBeans IDE 5.0:

  • From the equipment menu, choose Library manager, as proven in figure 2.

    determine 2: The library supervisor allows you to add third-celebration libraries to your mission.

  • within the Library supervisor window, create a new library named JavaDBEmbedded, as proven in determine three. click on ok.

    figure three: identify the set of libraries that your assignment will need.

  • to add the derby.jar file to the JavaDBEmbedded library, click on Add JAR/Folder... in the Library manager window. Navigate the file chooser to the derby.jar file and select it as proven in determine four.

    figure 4: Add the derby.jar file to the JavaDBEmbedded library.

  • within the equal Library supervisor window for the JavaDBEmbedded library, select the Javadoc tab. Add the javadoc subdirectory out of your Java DB installing. Now, the Java DB API Javadoc is attainable if you happen to use the JavaDBEmbedded library on your NetBeans IDE tasks.
  • that you can now add the JavaDBEmbedded library to your NetBeans IDE 5.0 assignment through the use of the task's property settings. if you bring together, debug, and run the application inside the IDE, the IDE should be able to find the necessary derby.jar file.

    To accommodate clients of other IDEs, I actually have positioned the derby.jar library in the lib subdirectory of the downloadable handle booklet assignment. The accompanying ANT script can construct and run the demo without any dependencies on the NetBeans IDE.

    Loading the Database Driver

    Loading the JDBC know-how driver begins the database management system. Java DB's drivers come with the derby.jar file, so you don't need to download anything. Load the JDBC driver with the aid of referencing it the usage of the type.forName components. The embedded driver identify is org.apache.derby.jdbc.EmbeddedDriver, and you may load it as you might other JDBC drivers.

    class.forName("org.apache.derby.jdbc.EmbeddedDriver");  

    The tackle publication demo reads the driver name from a configuration property file and passes the identify to a loadDriver method. moreover, as outlined earlier, address booklet encapsulates all database performance into a data entry Object (DAO), a core Java EE design pattern used to access facts from a whole lot of sources. The DAO pattern works equally well for Java SE purposes like tackle ebook. right here code snippet shows how AddressDao reads the driver identify and loads the motive force:

    deepest properties bProperties = null; public AddressDao(String addressBookName) this.dbName = addressBookName; setDBSystemDir(); dbProperties = loadDBProperties(); String driverName = dbProperties.getProperty("derby.driver"); loadDatabaseDriver(driverName); ... deepest properties loadDBProperties() InputStream dbPropInputStream = null; dbPropInputStream = AddressDao.class.getResourceAsStream("Configuration.properties"); dbProperties = new houses(); try dbProperties.load(dbPropInputStream); seize (IOException ex) ex.printStackTrace(); return dbProperties; deepest void loadDatabaseDriver(String driverName) // Load the Java DB driver. are attempting type.forName(driverName); seize (ClassNotFoundException ex) ex.printStackTrace();   Connecting to the Java DB Database

    A JDBC expertise connection identifies a selected database and lets you perform administrative projects. tasks consist of starting, stopping, copying, and even deleting a database. the driver supervisor offers all database connections.

    Retrieve a connection from the motive force supervisor through presenting a URL string that identifies the database and a group of houses that have an effect on the connection's interplay with the database. a extremely regular use of residences is to affiliate a consumer name and password with a connection.

    All connection URLs have the following kind:

    jdbc:derby:<dbName>[propertyList]  

    The dbName portion of the URL identifies a selected database. A database can also be in a single of many areas: in the present working directory, on the classpath, in a JAR file, in a particular Java DB database home listing, or in an absolute area in your file system. The easiest way to control your database location in an embedded environment is to set the derby.system.domestic equipment property. This property tells Java DB the default domestic location of all databases. via setting this property, the tackle e-book demo ensures that Java DB always finds the relevant utility database. The application database is named DefaultAddressBook, and it'll exist within the listing indicated with the aid of the derby.device.domestic property. The connection URL for this database would seem like this:

    jdbc:derby:DefaultAddressBook  

    The non-compulsory propertyList is a set of homes that you should pass to the database system. that you can circulate houses into the Java DB equipment both on the URL itself or as a separate homes object. If homes are part of the URL, a semicolon ( ;) should precede each and every property. the most typical residences are these:

  • create=proper
  • databaseName=nameOfDatabase
  • user=userName
  • password=userPassword
  • shutdown=actual
  • To hook up with the DefaultAddressBook database, the demo must first set the derby.device.domestic system property. The demo makes use of the .addressbook subdirectory of the person's home listing. Use the device type to find out the consumer's home listing. Then use the category again to set the derby.gadget.domestic property:

    inner most void setDBSystemDir() // opt for the db device directory: <userhome>/.addressbook/ String userHomeDir = equipment.getProperty("consumer.home", "."); String systemDir = userHomeDir + "/.addressbook"; // Set the db device listing. gadget.setProperty("derby.system.domestic", systemDir);  

    as soon as the utility has distinctive the place all databases will exist, it may retrieve a database connection. during this instance, be aware that I have appended connection homes to the database URL.

    Connection dbConnection = null; String strUrl = "jdbc:derby:DefaultAddressBook;consumer=dbuser;password=dbuserpwd"; try dbConnection = DriverManager.getConnection(strUrl); capture (SQLException sqle) sqle.printStackTrace();  

    however, that you would be able to put those properties into a houses object. move the houses object as an argument when retrieving a connection:

    Connection dbConnection = null; String strUrl = "jdbc:derby:DefaultAddressBook"; properties props = new houses(); props.put("user", "dbuser"); props.put("password", "dbuserpwd"); are trying dbConnection = DriverManager.getConnection(strUrl, props); catch(SQLException sqle) sqle.printStackTrace();  

    The handle book demo utility doesn't have a competent-made database. as a substitute, the application have to create the database when it begins. one of the crucial greatest benefits of the use of an embedded database is that the application -- now not the person -- takes care of the details of setting up a database. The utility can manage the place the database exists, what tables exist, and how permissions are dealt with.

    tackle booklet creates a database referred to as DefaultAddressBook in a subdirectory of the user's domestic listing, and it does so devoid of asking the consumer for any additional information. you could create a brand new database in Java DB through the use of the create=actual property when retrieving a connection to a database. as a result of our software will use the DefaultAddressBook database, we may still first create this database. Assuming that the utility has already set the derby.gadget.domestic property as mentioned prior, the utility creates the database and connects to it like this:

    Connection dbConnection = null; String strUrl = "jdbc:derby:DefaultAddressBook;create=true"; are attempting dbConnection = DriverManager.getConnection(strUrl); catch (SQLException ex) ex.printStackTrace();  

    because the create=proper property is covered, Java DB will try to create the database files for the primary time. creating the database would not basically create any application tables. however, be sure you now be capable of finding a new subdirectory named .addressbook/DefaultAddressBook for your domestic directory.

    After it has created the database, the software creates the tables. The demo makes use of just one handle desk within the default software APP schema. right here SQL code creates the tackle table:

    CREATE table APP.tackle ( identification INTEGER no longer NULL basic KEY GENERATED at all times AS id (birth WITH 1, INCREMENT by means of 1), LASTNAME VARCHAR(30), FIRSTNAME VARCHAR(30), MIDDLENAME VARCHAR(30), cell VARCHAR(20), electronic mail VARCHAR(30), ADDRESS1 VARCHAR(30), ADDRESS2 VARCHAR(30), metropolis VARCHAR(30), STATE VARCHAR(30), POSTALCODE VARCHAR(20), country VARCHAR(30) )  

    each record has a list identifier or id box. Java DB generates this value for every new record that it adds to the database. The identity box is the simple key for every handle list.

    All remaining handle record fields include varchar facets of a number of lengths. for instance, the LASTNAME container can include a maximum of 30 varchar characters. The varchar classification is such as a UTF-sixteen Java char code unit.

    The Java know-how code that makes use of the above SQL observation to create the address desk feels like the following code. The dbConnection is an identical because the one shown within the outdated code. We effectively move it into createTables, create a brand new statement, and get in touch with the execute method to run the SQL code on the newly shaped database. The strCreateAddressTable instance variable holds the SQL commentary textual content.

    private boolean createTables(Connection dbConnection) boolean bCreatedTables = false; observation commentary = null; are trying observation = dbConnection.createStatement(); observation.execute(strCreateAddressTable); bCreatedTables = real; trap (SQLException ex) ex.printStackTrace(); return bCreatedTables;  

    At this point, the database and the address desk exist in a subdirectory named .addressbook/DefaultAddressBook to your domestic listing. however which you could browse this subdirectory, prevent modifying any info. in case you edit or delete any of these database files without delay, that you can smash the integrity of your database.

    as soon as the database and its tables have been created, your utility can create new connections and statements so as to add, edit, delete, or retrieve data. In handle publication, these movements are controlled through buttons within the AddressActionPanel. determine 5 shows the available alternate options:

  • New. Create a brand new address listing.
  • Delete. Delete the displayed tackle list.
  • Edit. Edit the displayed handle checklist.
  • save. store the brand new or edited address checklist that's displayed.
  • Cancel. Cancel any edits or any try to create a new list.
  • figure 5: tackle publication has several alternatives for interacting with data.

    The main window of the application is AddressFrame, which acts as a controller and as a view on the same time. It registers itself with the AddressActionPanel to acquire notification when a person clicks anywhere on the action bar.

    the new command clears the address entry panel and permits the person to edit all fields. No SQL commands are issued at this factor, however the UI may still allow you to enter a new tackle.

    The Delete command makes an attempt to delete the at present selected tackle list. AddressFrame retrieves the at the moment selected address identifier from the AddressPanel and uses AddressDao to delete the checklist. The panel calls its own deleteAddress system, which calls the DAO's deleteRecord components with the relevant id. After deleting the checklist from the database, the application have to delete the ListEntry from the AddressListPanel too.

    private void deleteAddress() int identity = addressPanel.getId(); if (identity != -1) db.deleteRecord(identification); int selectedIndex = addressListPanel.deleteSelectedEntry(); ... ...  

    in the AddressDao, the deleteRecord formula handles the precise deletion of the list from the database. The AddressDao creates a PreparedStatement when it first connects to the database.

    stmtDeleteAddress = dbConnection.prepareStatement( "DELETE FROM APP.handle " + "the place id = ?");  

    The PreparedStatement will also be used dissimilar times, and this one uses a parameter to check which checklist to delete. The deleteRecord components executes the update after setting the identity parameter:

    public boolean deleteRecord(int id) boolean bDeleted = false; are trying stmtDeleteAddress.clearParameters(); stmtDeleteAddress.setInt(1, id); stmtDeleteAddress.executeUpdate(); bDeleted = authentic; catch (SQLException sqle) sqle.printStackTrace(); return bDeleted;  

    The Edit command makes it possible for the person to edit the currently selected address checklist within the AddressPanel. as an example, that you would be able to exchange the name, metropolis, or mobilephone number of a saved checklist.

    The save command retrieves either the newly created or edited handle from the AddressPanel and makes an attempt to both replace the present record or create a new listing. If the consumer has been enhancing a list, retailer will update that checklist with the brand new counsel. If the user has created a new checklist, keep will insert a brand new listing within the database. New facts have not yet been saved. At this element, their identity field remains set to the default -1 cost. This value adjustments to an autogenerated, unique record identifier once you shop the checklist.

    here code in AddressFrame will retailer edited and newly created handle statistics through calling the DAO's editRecord or saveRecord components, respectively. Of course, for those who create a new checklist, the utility must also replace the AddressListPanel.

    inner most void saveAddress() if (addressPanel.isEditable()) handle handle = addressPanel.getAddress(); int id = tackle.getId(); if (identification == -1) identification = db.saveRecord(address); address.setId(identity); String lname = handle.getLastName(); String fname = tackle.getFirstName(); String mname = address.getMiddleName(); ListEntry entry = new ListEntry(lname, fname, mname, identity); addressListPanel.addListEntry(entry); else db.editRecord(address); addressPanel.setEditable(false);  

    The DAO's editRecord method have to update the fields that alternate within the address checklist. since the demo software would not distinguish between edited and unedited fields, it conveniently updates all fields within the checklist. Following are the PreparedStatement object and the editRecord formulation:

    stmtUpdateExistingRecord = dbConnection.prepareStatement( "update APP.address " + "SET LASTNAME = ?, " + " FIRSTNAME = ?, " + " MIDDLENAME = ?, " + " cell = ?, " + " e-mail = ?, " + " ADDRESS1 = ?, " + " ADDRESS2 = ?, " + " metropolis = ?, " + " STATE = ?, " + " POSTALCODE = ?, " + " country = ? " + "where identification = ?"); ... public boolean editRecord(address listing) boolean bEdited = false; try stmtUpdateExistingRecord.clearParameters(); stmtUpdateExistingRecord.setString(1, checklist.getLastName()); stmtUpdateExistingRecord.setString(2, record.getFirstName()); stmtUpdateExistingRecord.setString(3, list.getMiddleName()); stmtUpdateExistingRecord.setString(4, list.getPhone()); stmtUpdateExistingRecord.setString(5, list.getEmail()); stmtUpdateExistingRecord.setString(6, list.getAddress1()); stmtUpdateExistingRecord.setString(7, checklist.getAddress2()); stmtUpdateExistingRecord.setString(8, checklist.getCity()); stmtUpdateExistingRecord.setString(9, listing.getState()); stmtUpdateExistingRecord.setString(10, checklist.getPostalCode()); stmtUpdateExistingRecord.setString(11, list.getCountry()); stmtUpdateExistingRecord.setInt(12, checklist.getId()); stmtUpdateExistingRecord.executeUpdate(); bEdited = real; capture(SQLException sqle) sqle.printStackTrace(); return bEdited;  

    Saving a new handle creates a new database listing, and that new checklist has a new fundamental key or record identifier. when we create the PreparedStatement, we will tell the database that we need to understand the generated keys. notice how we will retrieve a ResultSet of generated keys after inserting the record. The saveRecord method returns the basic key of the newly created checklist.

    stmtSaveNewRecord = dbConnection.prepareStatement( "INSERT INTO APP.handle " + " (LASTNAME, FIRSTNAME, MIDDLENAME, " + " telephone, e mail, ADDRESS1, ADDRESS2, " + " city, STATE, POSTALCODE, country) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", statement.RETURN_GENERATED_KEYS); ... public int saveRecord(tackle listing) int identification = -1; are trying stmtSaveNewRecord.clearParameters(); stmtSaveNewRecord.setString(1, checklist.getLastName()); stmtSaveNewRecord.setString(2, checklist.getFirstName()); stmtSaveNewRecord.setString(3, listing.getMiddleName()); stmtSaveNewRecord.setString(four, list.getPhone()); stmtSaveNewRecord.setString(5, checklist.getEmail()); stmtSaveNewRecord.setString(6, listing.getAddress1()); stmtSaveNewRecord.setString(7, record.getAddress2()); stmtSaveNewRecord.setString(8, record.getCity()); stmtSaveNewRecord.setString(9, list.getState()); stmtSaveNewRecord.setString(10, list.getPostalCode()); stmtSaveNewRecord.setString(eleven, checklist.getCountry()); int rowCount = stmtSaveNewRecord.executeUpdate(); ResultSet outcomes = stmtSaveNewRecord.getGeneratedKeys(); if (effects.next()) id = outcomes.getInt(1); seize(SQLException sqle) sqle.printStackTrace(); return identity;   Deploying Your utility

    Now that you've written the utility, you must installation it to clients. Java technology applications can use numerous deployment options, including Java net birth software, applets, and stand-alone JAR information. I distribute the handle ebook software as a stand-on my own software with JAR files.

    The ANT build file, construct.xml, makes use of a dist goal to create AddressBook.jar. It also areas the database JAR file within the lib subdirectory directly below the AddressBook.jar place. The final distribution structure for the application looks like this:

    AddressBook.jar lib/derby.jar  

    in many circumstances, applications that use third-celebration libraries equivalent to derby.jar require an external script to run. The script continually places the third-celebration JAR files on the classpath and then executes the application from its JAR. This components is difficult, despite the fact, because it requires diverse scripts, usually one for each and every supported host working gadget. as an instance, to guide distribution on windows, Solaris, and Linux systems, i might create a run.bat batch file for the home windows platform and a run.csh script for a Solaris or Linux platform. as a substitute, we will avoid these clumsy execution scripts.

    If our construct method comprises classpath suggestions in the AddressBook.jar occur file, that you can run the utility by way of comfortably passing the AddressBook.jar file on the execution command line. On most systems, which you could additionally simply double-click on the JAR file name in a graphical window. On a command line, you can use this fundamental execution command:

    java -jar AddressBook.jar  

    This basic deployment and execution situation will also be accomplished by means of creating a manifest.mf file that turns into part of the AddressBook.jar file. which you could include suggestions in the appear that tells the Java programming language interpreter which class carries the main system and what different JAR data may still develop into a part of the classpath. here occur does each, and we will include it when building AddressBook.jar.

    occur-version: 1.0 main-class: com.solar.demo.addressbook.AddressFrame classification-direction: lib/derby.jar  

    once your build process generates the application distribution structure proven prior to now, which you could simply distribute this structure as a zipper file. users can with no trouble unzip the file into any area and run the AddressBook.jar file. The AddressBook.jar file will contain the manifest file outlined past and should tell your runtime atmosphere what JAR info may still even be on the classpath. Of route, because Java DB is embedded into this software, it must be able to find lib/derby.jar with a view to run accurately.

    Working with Java DB is convenient and enjoyable. Java DB makes it feasible to create and distribute an embedded database with minimal effort. just bear in mind a number of suggestions to make your work with Java DB a success:

  • region the derby.jar file to your construction ambiance's classpath in order that your Java expertise compiler and runtime atmosphere can locate the libraries to compile and run the application.
  • Set the derby.gadget.home equipment property to tell Java DB the place to locate your database. which you could set this property programmatically or on the command line.
  • Create a build system that areas the derby.jar file in a lib subdirectory immediately under your utility's personal directory.
  • Add derby.jar to the application classpath through including a class-course property on your application JAR's show up file.
  • Footnote

    The phrases "Java digital machine" and "JVM" mean a virtual machine for the Java platform."

    For greater tips


    Oracle To purchase solar For about $7.4 Billion – grasp On To Your Hats | killexams.com Real Questions and Pass4sure dumps

    Oracle organization is to buy solar Microsystems for $9.50 a share in a deal valued at about $7.4 billion, simply just a few weeks after a deal by using IBM to purchase solar fell apart. It seems like Oracle will pay a top rate of $2.eighty one a share, or 42%, over sun Micro’s closing cost of $6.sixty nine a share on Friday.

    Oracle observed the deal is valued at $5.6 billion except for money and debt. Oracle is looking solar’s Java “the most crucial software” it has ever obtained. The deal, which is anticipated to close in the summer and was unanimously accepted by means of sun’s board of administrators, has big implications for the longer term openness of Java and MySQL.

    because the ny times elements out, Oracle and solar are two heavyweights that have been companions for greater than 20 years, although Oracle has been distancing itself a bit of from sun’s server line in choose of rivals like HP and Dell currently as a result of solar’s enterprise decline. on account of this deal, Oracle will now turn into a behemoth in each the application and the hardware market, and the implications this acquisition will have on the its closest rivals and the market in familiar will be noticable for years to come.

    The legitimate free up, with emphasis ours:

    Oracle to buy solar

    SANTA CLARA, Calif. April 20, 2009 solar Microsystems (NASDAQ: JAVA) and Oracle service provider (NASDAQ: ORCL) introduced these days they have got entered into a definitive contract below which Oracle will acquire solar commonplace inventory for $9.50 per share in cash. The transaction is valued at approximately $7.4 billion, or $5.6 billion web of solar’s cash and debt.

    “We expect this acquisition to be accretive to Oracle’s earnings with the aid of at least 15 cents on a non-GAAP foundation within the first full 12 months after closing. We estimate that the obtained enterprise will make contributions over $1.5 billion to Oracle’s non-GAAP operating profit within the first 12 months, increasing to over $2 billion within the second year. this may make the sun acquisition extra profitable in per share contribution within the first year than we had planned for the acquisitions of BEA, PeopleSoft and Siebel mixed,” referred to Oracle President Safra Catz.

    “The acquisition of solar transforms the IT business, combining best-in-class commercial enterprise software and mission-important computing methods,” said Oracle CEO Larry Ellison. “Oracle should be the most effective business that may engineer an built-in equipment – applications to disk – where the entire items healthy and work collectively so shoppers don't have to do it themselves. Our purchasers improvement as their techniques integration expenses go down whereas gadget efficiency, reliability and security go up.”

    There are mammoth long-time period strategic consumer merits to Oracle owning two key solar application belongings: Java and Solaris. Java is among the computer business’s most effective-regularly occurring manufacturers and most widely deployed applied sciences, and it is the most vital application Oracle has ever obtained. Oracle Fusion Middleware, Oracle’s fastest turning out to be company, is developed on accurate of sun’s Java language and utility. Oracle can now make sure continued innovation and investment in Java technology for the improvement of valued clientele and the Java group.

    The solar Solaris working device is the leading platform for the Oracle database, Oracle’s biggest business, and has been for a long time. With the acquisition of sun, Oracle can optimize the Oracle database for one of the vital enjoyable, high-end aspects of Solaris. Oracle is as committed as ever to Linux and different open systems and should continue to support and boost our powerful trade partnerships.

    “Oracle and solar have been business pioneers and close partners for greater than twenty years,” mentioned solar Chairman Scott McNealy. “This combination is a natural evolution of our relationship and may be an business-defining adventure.”

    “here's a lovely day for solar’s clients, builders, partners and employees across the globe, joining forces with the global leader in commercial enterprise software to pressure innovation and value throughout each element of the expertise marketplace,” stated Jonathan Schwartz, sun’s CEO, “From the Java platform touching nearly each enterprise device on earth, powering billions of buyers on cellular handsets and buyer electronics, to the convergence of storage, networking and computing driven by the Solaris operating system and solar’s SPARC and x64 systems. in conjunction with Oracle, we’ll power the innovation pipeline to create compelling cost to our customer base and the market.”

    “sun is a pioneer in business computing, and this aggregate acknowledges the innovation and customer success the company has carried out. Our biggest valued clientele were asking us to step up to a broader position to cut back complexity, risk and cost through offering a highly optimized stack based on requisites,” observed Oracle President Charles Phillips. “This transaction will keep and boost investments made by using our purchasers, whereas we proceed to work with our companions to supply valued clientele with choice.”

    The Board of directors of solar Microsystems has unanimously permitted the transaction. it's anticipated to close this summer time, field to sun stockholder approval, certain regulatory approvals and typical closing conditions.

    There can be a conference call these days to discuss the transaction at 5:30 a.m. Pacific time. buyers can hearken to the conference call by using dialing (719) 234-7870, passcode 923645. A replay will be available for 24 hours after the name ends at (719) 884-8882, passcode: 923645. A are living audio webcast of the name might be made attainable at www.oracle.com/investor and a replay can be obtainable for seven days after the name ends.

    About Oracle

    Oracle (NASDAQ: ORCL) is the realm’s largest enterprise application enterprise. For extra suggestions about Oracle, please visit our web web site at http://www.oracle.com.About sun Microsystems, Inc.

    solar Microsystems develops the applied sciences that vigour the global market. Guided by a unique imaginative and prescient — “The community is the computer” — sun drives community participation via shared innovation, neighborhood construction and open supply management. solar can be present in greater than a hundred nations and on the net at http://solar.com.


    Spring unlock models three.0.4 and a pair of.4.3 of Spring internet capabilities | killexams.com Real Questions and Pass4sure dumps

    Spring has released two models of their net capabilities assignment on the equal time: version 3.0.4.unlock because the leading branch of construction, and edition 2.four.3.liberate for upkeep. both versions have been upgraded to run on Spring Framework 5.1.0 and to help Java eleven.

    Spring web capabilities (Spring-WS) is a made from the Spring community focused on creating doc-pushed net services. It goals to facilitate contract-first soap provider development, allowing for the advent of flexible net features the usage of one of the many ways to manipulate XML payloads.

    essentially the most first-rate development in the new Spring net capabilities models is assist for Java eleven. considering Java 9, a couple of key Java EE applications similar to core XML and soap-based mostly applications, had their visibility decreased, and in Java eleven, these packages were eliminated altogether. for that reason, to make use of Spring internet features on Java 9+, developers can now not depend on the JDK featuring key XML and cleaning soap-based libraries.

    a new Java eleven profile, which incorporates the added dependencies that developers must add to their personal construct file, has been delivered to the Spring internet capabilities construct file. builders should add the dependencies to their construct file when the use of Java 11. here is a snippet of the construct.xml with the dependencies obligatory for Java 11 on version 3.0.four.free up:

    <dependencies> <dependency> <groupId>com.solar.xml.ws</groupId> <artifactId>jaxws-ri</artifactId> <version>2.three.0</edition> <category>pom</class> </dependency> <dependency> <groupId>org.jvnet.staxex</groupId> <artifactId>stax-ex</artifactId> <version>1.7.8</edition> </dependency> <dependency> <groupId>com.sun.xml.messaging.saaj</groupId> <artifactId>saaj-impl</artifactId> <version>1.3.28</edition> </dependency> <dependency> <groupId>javax.xml.soap</groupId> <artifactId>javax.xml.cleaning soap-api</artifactId> <version>1.four.0</version> </dependency> <dependency> <groupId>javax.xml.ws</groupId> <artifactId>jaxws-api</artifactId> <version>2.three.0</version> </dependency>

    builders who still use Spring web functions 2.four.three.unencumber will not have complications the usage of Java 11. This edition simply uses a a bit of older version of the soap API (1.three.8). right here is a snippet of the construct.xml with the dependencies crucial for Java eleven on edition 2.4.3.liberate:

    <dependencies> <dependency> <groupId>com.sun.xml.ws</groupId> <artifactId>jaxws-ri</artifactId> <version>2.3.0</version> <classification>pom</type> </dependency> <dependency> <groupId>org.jvnet.staxex</groupId> <artifactId>stax-ex</artifactId> <version>1.7.8</edition> </dependency> <dependency> <groupId>com.sun.xml.messaging.saaj</groupId> <artifactId>saaj-impl</artifactId> <edition>1.3.28</version> </dependency> <dependency> <groupId>javax.xml.soap</groupId> <artifactId>javax.xml.soap-api</artifactId> <edition>1.three.8</version> </dependency> <dependency> <groupId>javax.xml.ws</groupId> <artifactId>jaxws-api</artifactId> <edition>2.3.0</edition> </dependency>

    both Spring net services types deliver a collection of malicious program fixes and improvements beyond the general Java 11 help, akin to:

  • SaajSoapMessage created with default (empty) SoapEnvelope (SWS-1018)
  • SimpleXsdSchema no longer initialized property produces NullPointerException (SWS-1036)
  • unravel version conflicts of third birthday party libraries (SWS-1030)
  • Ehcache - OWASP Dependency verify considerations (SWS-1033)
  • elements certain to Spring internet services 3.0.4.free up are:

  • Make SimpleXsdSchema supply a greater productive error message (SWS-1037)
  • Polish documentation, a group of small corrections on the documentation, reminiscent of a incorrect reference to Gradle Wrapper as an alternative of Maven Wrapper, broken hyperlinks, and so forth. (SWS-1038)
  • upgrade to newest version of Spring (SWS-1039)
  • Spring net features artifacts are staged on maven important and http://repo.spring.io/. there's additionally an example on Spring GitHub repo demonstrating how to use Spring internet capabilities with Spring Boot.




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    So there is a website out there, Educational Realist (via Steve Sailer), which made me aware of some statistics from ETS on the intellectual aptitudes of those who passed a teaching certification. This is relevant because those who major in education at university are notoriously rather weak students. The implication here is that teachers are substandard as a whole, a narrative long favored on the American Right, but now spreading in some parts of the Left.

    Below are the verbal and mathematical scores by licensing domain. The solid line represents the average SAT score of a college graduate.

    First, as a whole it seems the teaching profession is a high verbal and low math area, with math & science teachers being exceptions. But it really looks to me like there’s a sharp discontinuity between two groups of teachers here. Physical and special education instructors, as well as elementary school teachers, are less intelligent than the average college graduate. The other fields far less so, and in their domain of specialization they seem to be superior to the average college graduate.

    Another table also caught my eye. You see see in the table to the left that teachers are far whiter than their students. It is clear that the “Other” category is mostly Asian from later tables. Observe that both Asians and Hispanics are underrepresented in the teaching corps by a factor of 7 in relation to the number of Asian and Hispanic students. I suppose someone might start wondering as to whether this is a problem, but last I checked Asian students are not having difficulties despite not having Asian ethnicity teachers. And in any case, no offense to the teaching profession, but if there is outreach to Asian Americans to encourage their children to become teachers there may be violent repercussions. There’s enough ostracism already when children decide to go graduate school, instead of medical or law school. Trying to change the professional priorities of the community is going to be viewed as insensitive.

    To be more explicit at what I’m getting at, below are mean SAT test scores comparing individuals of various races between those who are college bound (not necessarily graduates!) and those who pass teaching certification.

    College bound seniors, 2010 Passed teacher certification, 2002-2005 Asian White Black Asian White Black SAT Math 591 536 429 521 524 459 SAT Verbal 519 528 428 510 534 482

    As you can see, Asian teachers, who presumably have a college degree, actually score lower than college bound Asians! This means they’re almost certainly drawn from a below average set of college graduates. For whites there is not so much discrepancy. And interestingly for blacks teachers seem to be drawn from the higher end of the distribution.


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    If you haven’t heard, universities around the world are offering their courses online for free (or at least partially free). These courses are collectively called MOOCs or Massive Open Online Courses.

    In the past six years or so, close to 800 universities have created more than 8,000 of these MOOCs. And I’ve been keeping track of these MOOCs the entire time over at Class Central, ever since they rose to prominence.

    In the past three months alone, over 200 universities have announced 600 such free online courses. I’ve compiled a list of them and categorized them according to the following subjects: Computer Science, Mathematics, Programming, Data Science, Humanities, Social Sciences, Education & Teaching, Health & Medicine, Business, Personal Development, Engineering, Art & Design, and finally Science.

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    COMPUTER SCIENCE

    Neural Networks and Deep Learning from deeplearning.ai (taught by Stanford prof. Andrew Ng)

    Algorithms: Design and Analysis from Stanford University (old Coursera course, but hosted without any paywalls directly by Stanford)

    Algorithms: Design and Analysis, Part 2 from Stanford University

    The Unix Workbench from Johns Hopkins University

    Hacking and Patching from University of Colorado System

    Machine Learning from Georgia Institute of Technology

    Linux Server Management and Security from University of Colorado System

    Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization from deeplearning.ai 

    Structuring Machine Learning Projects from deeplearning.ai

    Sequence Models from deeplearning.ai

    Convolutional Neural Networks from deeplearning.ai

    Accessible Gamification for Business from Georgia Institute of Technology

    Accounting Analysis II: Measurement and Disclosure of Liabilities from University of Illinois at Urbana-Champaign

    Bayesian Methods for Machine Learning from Higher School of Economics

    Natural Language Processing from Higher School of Economics

    Introduction to Deep Learning from Higher School of Economics

    Deep Learning in Computer Vision from Higher School of Economics

    Addressing Large Hadron Collider Challenges by Machine Learning from Higher School of Economics

    Introduction to Reinforcement Learning from Higher School of Economics

    Introduction to TCP/IP from Yonsei University

    Deep Learning for Business from Yonsei University

    TV Whitespaces for Museums and Archives from San Jose State University

    Capstone: Autonomous Runway Detection for IoT from EIT Digital

    Homeland Security & Cybersecurity Connection – It’s Not About the Terrorists from University of Colorado System

    Packet Switching Networks and Algorithms from University of Colorado System

    Proactive Computer Security from University of Colorado System

    Introduction to Cybersecurity for Business from University of Colorado System

    Cyber Threats and Attack Vectors from University of Colorado System

    Detecting and Mitigating Cyber Threats and Attacks from University of Colorado System

    Cybersecurity Policy for Aviation and Internet Infrastructures from University of Colorado System

    Enterprise System Management and Security from University of Colorado System

    TCP/IP and Advanced Topics from University of Colorado System

    Peer-to-Peer Protocols and Local Area Networks from University of Colorado System

    Windows Server Management and Security from University of Colorado System

    Planning, Auditing and Maintaining Enterprise Systems from University of Colorado System

    Homeland Security and Cybersecurity Future from University of Colorado System

    Cybersecurity Policy for Water and Electricity Infrastructures from University of Colorado System

    Fundamentals of Network Communication from University of Colorado System

    Design and Analyze Secure Networked Systems from University of Colorado System

    Secure Networked System with Firewall and IDS from University of Colorado System

    Symmetric Cryptography from University of Colorado System

    Cryptography and Information Theory from University of Colorado System

    Cryptographic Hash and Integrity Protection from University of Colorado System

    Asymmetric Cryptography and Key Management from University of Colorado System

    Basic Cryptography and Programming with Crypto API from University of Colorado System

    Classical Cryptosystems and Core Concepts from University of Colorado System

    Архитектура ЭВМ (Computer Architecture) from Saint Petersburg State University

    Sparse Representations in Signal and Image Processing: Fundamentals from Technion – Israel Institute of Technology

    Sparse Representations in Image Processing: From Theory to Practice from Technion – Israel Institute of Technology

    Cutting Edge Deep Learning For Coders, Part 2 from fast.ai

    Architettura degli elaboratori from University of Urbino

    Liberating Programming: System Development for Everyone from Weizmann Institute of Science

    Банковское дело и финансы from Sberbank Corporate University

    Digital Accessibility as a Business Practice from Ryerson University

    PROGRAMMING

    Advanced MATLAB for Scientific Computing from Stanford University

    Building Web Applications in PHP from University of Michigan

    Building Database Applications in PHP from University of Michigan

    JavaScript, jQuery, and JSON from University of Michigan

    Introduction to Structured Query Language (SQL) from University of Michigan

    Server-side Development with NodeJS, Express and MongoDB from The Hong Kong University of Science and Technology

    Database Systems Concepts and Design from Georgia Institute of Technology

    Programming Fundamentals from Duke University

    Python Programming Essentials from Rice University

    Python Data Representations from Rice University

    Parallel Programming in Java from Rice University

    Distributed Programming in Java from Rice University

    Concurrent Programming in Java from Rice University

    3D Models for Virtual Reality from University of London International Programmes

    Building Interactive 3D Characters and Social VR from University of London International Programmes

    Introduction to Virtual Reality from University of London International Programmes

    Making Your First Virtual Reality Game from University of London International Programmes

    3D Interaction Design in Virtual Reality from University of London International Programmes

    Software Development Processes and Methodologies from University of Minnesota

    Agile Software Development from University of Minnesota ★★★★☆(1)

    Lean Software Development from University of Minnesota

    Engineering Practices for Building Quality Software from University of Minnesota

    Globally Distributed Software Engineering from Delft University of Technology

    Программирование на Python from Moscow Institute of Physics and Technology

    Тонкости верстки from Moscow Institute of Physics and Technology

    Основы HTML и CSS from Moscow Institute of Physics and Technology

    JavaScript, часть 1: основы и функции from Moscow Institute of Physics and Technology

    Основы дизайна мобильных приложений from Moscow Institute of Physics and Technology

    Introducción a la programación orientada a objetos en Java from Universidad de los Andes

    Design Patterns from University of Alberta

    Object-Oriented Design from University of Alberta

    Service-Oriented Architecture from University of Alberta

    Software Architecture from University of Alberta

    Software Design as an Element of the Software Development Lifecycle from University of Colorado System

    Software Design Methods and Tools from University of Colorado System

    Software Design as an Abstraction from University of Colorado System

    Software Design Threats and Mitigations from University of Colorado System

    Video Game Design: Teamwork & Collaboration from Rochester Institute of Technology

    Базы данных (Databases) from Saint Petersburg State University

    Aprende a programar en C desde cero from Universitat Jaume I

    Introducción a la programación en Python I: Aprendiendo a programar con Python from Pontificia Universidad Católica de Chile

    Fare Apps in classe, ora! from University of Urbino

    Creando Apps. Aprende a programar aplicaciones móviles from Universitas Telefónica

    Introducción al diseño de videojuegos from Universitas Telefónica

    Game Design and Development: Video Game Character Design from Abertay University

    Hacking PostgreSQL: Data Access Methods from Ural Federal University

    HUMANITIES

    Modern Masterpieces of World Literature from Harvard University

    Religion, Conflict and Peace from Harvard University

    Ancient Masterpieces of World Literature from Harvard University

    China Humanities: The Individual in Chinese Culture from Harvard University

    Supporting English Learners: Resources for Leaders from Stanford University

    Sports and the University from Stanford University

    Wonders of Ancient Egypt from University of Pennsylvania

    Race and Cultural Diversity in American Life and History from University of Illinois at Urbana-Champaign

    Studying in Italian – Science and Mathematics: a MOVE-ME Project Course from The Open University

    Studying in Italian – Law and Economics: a MOVE-ME Project Course from The Open University

    Faith and Finance from Boston University

    Philosophy, Science and Religion: Philosophy and Religion from University of Edinburgh

    The Birmingham Qur’an: Its Journey from the Islamic Heartlands from University of Birmingham

    Frontières en tous genres from University of Geneva

    Incipit. Curso básico de lengua y cultura latinas from Universitat Politècnica de València

    東亞儒家:人文精神二(East Asian Confucianisms: Humanism 2) from National Taiwan University

    Arab-Islamic History: From Tribes to Empires from Tel Aviv University

    Knowledge Exchange: Using, Protecting and Monetizing Ideas with Third Parties from Leiden University

    El Valle de los Reyes from Universitat Autònoma de Barcelona (Autonomous University of Barcelona)

    ITA101 – Benvenuti in Italia! Orientarsi con l’italiano from Politecnico di Milano

    Re-imaging God in Korean Context from Yonsei University

    Mitos clásicos y mundo actual from Universidad Carlos iii de Madrid

    Philosophy for Children and P.E.A.C.E. from University of Naples Federico II

    L’innovazione sociale per il patrimonio culturale from University of Naples Federico II

    Dante tra poesia e scienza from University of Naples Federico II

    La costruzione dell’Italia from University of Naples Federico II

    La Letteratura latina in età post-augustea from University of Naples Federico II

    Comprendere la filosofia from University of Naples Federico II

    L’Italiano nel mondo from University of Naples Federico II

    Русский как иностранный: B1 (Повторение). Russian as a foreign language B1 (Revision) from Saint Petersburg State University

    Китайский для начинающих (Chinese for beginners) from Saint Petersburg State University

    Ислам: история, культура, практика (Islam: history, culture, praxis) from Saint Petersburg State University

    Business Start–Up: From Idea to Launch from IOC Athlete MOOC

    Safeguarding Athletes from Harassment and Abuse from McMaster University

    Exercise Physiology – The Body In Motion from IOC Athlete MOOC

    Writing your World: Finding yourself in the academic space from University of Cape Town

    Power of the Pen: Identities and Social Issues in Poetry and Plays from University of Iowa

    Power of the Pen: Identities and Social Issues in Fiction and Nonfiction from University of Iowa

    Sports Nutrition: Eat to Compete from IOC Athlete MOOC

    The Art and Science of Coaching from IOC Athlete MOOC

    Russian History: from Lenin to Putin from University of California, Santa Cruz

    Why Research Matters from Deakin University

    The Qur’an Between Judaism and Christianity from The University of Nottingham

    Words Spun Out of Images: Visual and Literary Culture in Nineteenth Century Japan from The University of Tokyo

    Humphry Davy: Laughing Gas, Literature, and the Lamp from Lancaster University

    (K-12) Teaching Math to English Language Learners from Iowa State University

    The Spain of Don Quixote from Universidad Autónoma de Madrid

    Paesaggi di Roma Antica. Archeologia e storia del Palatino. from Sapienza University of Rome

    Logic and Paradoxes from Universidad de Navarra

    New Zealand Landscape as Culture: Islands (Ngā Motu) from Victoria University of Wellington

    A History of Violence: From the Middle Ages to Modern Times from University of Newcastle

    Digital Culture and Writing from University of Burgundy

    Livres enluminés à la cour des Sforza from University of Pavia

    Beer Matters from University of Wisconsin – Whitewater

    ga080: memento mori-死を想え- from Tohoku University

    다문화 사회와 공존의 인문학 from Inha University

    pt015: 食文化の本来と将来 from Kokushikan University

    Visión cristiana de la persona humana from Pontifical Catholic University of Valparaíso

    Comunicación cultural en España from Antonio de Nebrija University

    Spanish Across the Americas: Beginners from Universidad Nacional de Córdoba

    The Bible in Light of the Ancient Near East from Bar-Ilan University

    Fantastic Fiction and Where to Find It from Linnaeus University

    Introduction to Kabbalah from Ben-Gurion University of the Negev

    DATA SCIENCE

    Data Science: R Basics from Harvard University

    Causal Diagrams: Draw Your Assumptions Before Your Conclusions from Harvard University

    Principles, Statistical and Computational Tools for Reproducible Science from Harvard University

    Data Science Ethics from University of Michigan

    Statistical Modeling and Regression Analysis from Georgia Institute of Technology

    Big Data Analytics in Healthcare from Georgia Institute of Technology

    Data Analytics Foundations for Accountancy I from University of Illinois at Urbana-Champaign

    Introduction to Genomic Data Science from University of California, San Diego

    Python Data Visualization from Rice University

    Python Data Analysis from Rice University

    Understanding Your Data: Analytical Tools from University of Virginia

    How to Win a Data Science Competition: Learn from Top Kagglers from Higher School of Economics

    Big Data and the Environment from University of Reading

    Predictive Modeling in Learning Analytics from University of Texas Arlington

    Knowledge Inference and Structure Discovery for Education from University of Texas Arlington

    Multimodal Learning Analytics from University of Texas Arlington

    Social Network Analysis (SNA) from University of Texas Arlington

    Connecting Learning Data to Improve Instructional Design from University of Texas Arlington

    Feature Engineering for Improving Learning Environments from University of Texas Arlington

    Cluster Analysis from University of Texas Arlington

    Learning Analytics Fundamentals from University of Texas Arlington

    Whole genome sequencing of bacterial genomes – tools and applications from Technical University of Denmark (DTU)

    Understanding Data from George Mason University

    Análisis de Datos Experimentales: Utilidades Básicas. from Universidad Politécnica de Madrid

    Advanced Data Mining with Weka from University of Waikato

    Введение в данные from Novosibirsk State University

    Тренды и классификации from Novosibirsk State University

    pt010: 高校生のためのデータサイエンス入門 from Shiga University

    MATHEMATICS

    Calculus Applied! from Harvard University

    Differential Equations: 2×2 Systems from Massachusetts Institute of Technology

    Introduction to Probability: Part 1 – The Fundamentals from Massachusetts Institute of Technology

    Introduction to Graph Theory from University of California, San Diego

    What is a Proof? from University of California, San Diego

    Number Theory and Cryptography from University of California, San Diego

    Delivery Problem from University of California, San Diego

    Геометрия и группы from Moscow Institute of Physics and Technology

    Mathematical Foundations for Cryptography from University of Colorado System

    Математические методы в психологии. Основы применения (Mathematical Methods in Psychology: Basics of Applying) from Saint Petersburg State University

    More Fun with Prime Numbers from Kyoto University

    Precalculus: the Mathematics of Numbers, Functions and Equations from University of Padova

    Advanced Precalculus: Geometry, Trigonometry and Exponentials from University of Padova

    Einführung in die Graphentheorie from ITMO University

    SCIENCE

    The Climate-Energy Challenge from Harvard University

    Hurricanes: What’s Next? from University of Michigan

    Thomas Berry的世界观:地球社区的繁荣 (The Worldview of Thomas Berry: The Flourishing of the Earth Community) from Yale University

    宇宙之旅:展现生命 (Journey of the Universe: The Unfolding of Life) from Yale University

    宇宙之旅:对话 (Journey of the Universe: Weaving Knowledge and Action) from Yale University

    Analyze your Genome! from University of California, San Diego

    Introduction to Mechanics, Part 1 from Rice University

    生物学概念与途径 from Peking University

    Simulation Neuroscience from École Polytechnique Fédérale de Lausanne

    Life on Earth: Biomes, Climates, Ecology, and Evolution from Ohio State University

    Energy and Earth: Fossil Fuels, Alternative, and Renewable Energy from Ohio State University

    Earth’s Environment: Soil, Water, and Air from Ohio State University

    Introducción a la Geotermia from Universidad Nacional Autónoma de México

    A Circular Economy of Metals: Towards a Sustainable Societal Metabolism from Leiden University

    Sustainability of Social-Ecological Systems: the Nexus between Water, Energy and Food from Universitat Autònoma de Barcelona (Autonomous University of Barcelona)

    Laboratorio di Programmazione from University of Naples Federico II

    Science & Religion 101 from University of Alberta

    Nutrition and Health: Human Microbiome from Wageningen University

    Principios básicos de divulgación científica from Universidad de Cantabria

    Квантовые вычисления (Quantum computing) from Saint Petersburg State University

    Неорганическая химия: Введение в химию элементов (Inorganic chemistry: Introduction to chemistry of the elements) from Saint Petersburg State University

    Введение в физиологию (Introduction to physiology) from Saint Petersburg State University

    Общие вопросы патологии и патологической анатомии (General Issues of Pathology and Pathological Physiology from Saint Petersburg State University

    Life in the Universe: Syntheses for Life from Seoul National University

    Wasser in der Schweiz from University of Zurich

    Ecosystems of California from University of California, Santa Cruz

    Physiology: the Science of Life from University of Liverpool

    Discover Best Practice Farming for a Sustainable 2050 from University of Western Australia

    Language Testing during Awake Brain Surgery from University of Groningen

    The Scientific Revolution: Understanding the Roots of Modern Science from University of Groningen

    Water Scarcity: Crisis and Response from Colorado State University Online

    Intro to Acoustics (Part 1) from Korea Advanced Institute of Science and Technology

    Metabolomics in Life Sciences from Osaka University

    Planning for Climate Change in African Cities from Erasmus University Rotterdam

    Autophagy: Research Behind the 2016 Nobel Prize in Physiology or Medicine from Tokyo Institute of Technology

    Introduction to Sustainability and Development from Deakin University

    Sistemas Coloidales en Alimentos. Del Laboratorio a la Cocina from Universidad Complutense de Madrid

    Sex, Science and Society from University of Newcastle

    Plasmonics: From Fundamentals to Modern Applications from ITMO University

    Why Biology Matters: Basic Concepts from Pompeu Fabra University

    L’eredità di Volta: dalla pila al fotovoltaico from University of Pavia

    Batteries, Fuel Cells, and their Role in Modern Society from Ural Federal University

    Scientific Methods and Research from Ural Federal University

    ga079: 解明:オーロラの謎 from Tohoku University

    Metodología Avanzada en Fisiología Celular from University of Extremadura

    Introducción a las ondas gravitacionales from Universidad Nacional de Córdoba

    Ecosistema y Cultura from Universidad Autónoma de Occidente

    Brewing Microbiology from Oklahoma State University

    SOCIAL SCIENCES

    Essentials of Program Strategy and Evaluation from Stanford University

    Love as a Force for Social Justice from Stanford University

    The Threat of Nuclear Terrorism from Stanford University

    Democracy and Development: Perspectives from Africa from Massachusetts Institute of Technology

    Network Dynamics of Social Behavior from University of Pennsylvania

    The Internet and You from University of Michigan

    Fake News, Facts, and Alternative Facts from University of Michigan

    Privacy, Reputation, and Identity in a Digital Age from University of Michigan

    Public Library Marketing and Public Relations from University of Michigan

    Infrastructure Management for Public Libraries from University of Michigan

    Grant Writing and Crowdfunding for Public Libraries from University of Michigan

    Strategic Planning for Public Libraries from University of Michigan

    Introduction to Psychology as a Science 2 – Fundamentals of the Mind and Behavior from Georgia Institute of Technology

    Introduction to Psychology as a Science 3 – Normal and Abnormal Behavior from Georgia Institute of Technology

    Introduction to Psychology as a Science 1 – Methodological and Biological Foundations from Georgia Institute of Technology

    Everyday Parenting: The ABCs of Child Rearing from Yale University

    Risk and Return and the Weighted Average Cost of Capital from Columbia University

    Federal Taxation I: Individuals, Employees, and Sole Proprietors from University of Illinois at Urbana-Champaign

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    Industrial Biotechnology from University of Manchester

    Environmental Management & Ethics from Technical University of Denmark (DTU)

    Introduction to solar cells from Technical University of Denmark (DTU)

    Software-Defined Radio 101 with RTL-SDR from Universidad Politécnica de Madrid

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    Use of machine learning to shorten observation-based screening and diagnosis of autism | killexams.com real questions and Pass4sure dumps

    Because the AGRE data contained only 11 controls for Module 1, we included all other Module 1 individuals with a classification of non-spectrum from the Boston AC in the analysis to bring the total number of controls to 15. We found that this improved the accuracy of our classifier when compared with the accuracy of only using the 11 controls alone. We then tested the performance of 16 different machine-learning algorithms on the 29 items in Module 1 (Table 2). We chose the best algorithm by comparing the sensitivity, specificity and accuracy (Figure 1). Two algorithms, the alternating decision tree (ADTree)16 and the functional tree,17 operated with perfect sensitivity, specificity and accuracy, resulting in classifiers with 8 and 9 questions, respectively. Because it was our goal to shorten the exam without appreciable loss of accuracy, we selected the ADTree as the optimum algorithm for further analysis and validation. The ADTree classifier correctly classified all 612 individuals from the AGRE who previously received a diagnosis of autism by the ADOS Module 1 algorithm, as well as all 15 individuals from the AGRE and AC who were given a classification of non-spectrum. The ADTree classifier consisted of 8 items out of the 29 used in the analysis and included A2, B1, B2, B5, B9, B10, C1 and C2 (Table 3).

    Figure 1 Figure 1

    Receiver operating characteristic curves mapping sensitivity versus specificity for the 16 different machine-learning algorithms tested on the ADOS Module 1 training data. We identified the best classifiers as those closest to the point (1, 0) on the graph indicating perfect sensitivity (true positive rate) and one specificity (false positive rate). The best performing models were the ADTree and functional tree (FT). The ADTree was chosen over the FT because it used less items. See Table 2 for a summary of the 16 machine-learning algorithms used in our analysis.

    Table 3: The eight items found in the ADTree classifier

    These eight items segregated into two of the three main functional domains associated with autism, language/communication and social interactions, both important indicators of autism. Item A2 (Frequency of Vocalization Directed to Others) corresponded to the language and communication domain. Items B1 (Unusual Eye Contact), B2 (Responsive Social Smile), B5 (Shared Enjoyment in Interaction), B9 (Showing) and B10 (Spontaneous Initiation of Joint Attention) corresponded to the domain of social interaction. Items C1 (Functional Play with Objects) and C2 (Imagination/Creativity) were designed to assess how the subject plays with objects. The eight items formed the elements of a decision tree that enabled classification of either autism or non-spectrum (Figure 2). Two items appeared more than once in the tree (B9 and B10), indicating the possibility that these items have a relatively more important role in classification of autism and that the domain of social interaction may have more utility in observation-based screening and diagnosis of autism. Each item in the tree either increased or decreased a running total statistic known as the ADTree score. A negative score indicated a classification of autism, whereas a positive score yielded the classification of non-spectrum. Importantly, the amplitude of the score provided a measure of confidence in the classification outcome, with larger absolute values indicating higher confidence overall, as previously indicated in Freund and Mason.16 In our study, the vast majority of the scores were away from the borderline for both the case and control classes (Figure 3), demonstrating that a majority of the predictions made by the classifier were robust and unambiguous.

    Figure 2 Figure 2

    Diagrammatic representation of the classifier generated by the ADTree algorithm. The ADTree was found to perform best out of the 16 different machine-learning approaches (Figure 1, Table 2). The resulting tree enables one to follow each path originating from the top node and increment (+) or decrement (−) prediction variables accordingly. In our case, variables with a negative sign yielded the classification of autism, whereas those with a positive sign resulted in a classification of non-spectrum. The magnitude of the score corresponded to confidence in the class prediction.

    Figure 3 Figure 3

    The ADTree scores of individuals in the AGRE, Boston AC and SSC data sets plotted against age in years (range from 13 months to 49 years). A majority of the ADTree scores were large, indicating confidence in the class predictions, and uncorrelated with the ages of the individuals.

    For independent validation of our eight-question classifier, we collated score sheets for Module 1 from the Boston AC and SSC. Here the objective was to determine if the classifier could correctly recapitulate the classification, i.e., autism versus non-spectrum, provided by the ADOS assessments of the individuals recruited to these two independent studies. The classifier correctly classified all 110 individuals previously meeting cutoffs for autism in AC. The classifier also performed with high accuracy on the SSC dataset misclassifying only 2 of the 336 individuals given a classification of autism in the original SSC (99.7% accuracy). Upon further examination of the two misclassified individuals from SSC, we learned that their ADTree scores were approximately zero, at 0.1 and 0.039. The low scores, corresponding to low statistical confidence in the classifications, suggested inadequate classifier power and the potential presence of non-spectrum behaviors in the misclassified subjects themselves.

    Because of the limited number of controls who received any ADOS Module, we elected to simulate 1000 controls by randomly sampling from the group of observed answers in the 15 individuals classified as non-spectrum. This procedure enabled us to construct a series of artificial score sheets for the ADOS Module 1 that were within the bounds of answers likely to be provided by prospectively recruited individuals who would not receive a diagnosis of autism following an ADOS exam. The classifier correctly classified 944 out of the 1000 simulated controls (94.4% accuracy). Upon closer inspection of the 56 simulated individuals misclassified with autism, we found that all but 6 had ADTree scores less than one unit away from the classification of non-spectrum (Figure 3).

    Because of the small number of controls and the imbalance between the numbers of cases and controls, we elected to perform a machine-learning procedure called upsampling to assess and rule out biases in the original classifier. Upsampling balances the numbers of cases and controls by progressive sampling from the population of observed data. We constructed a classifier using the ADTree algorithm with the 612 individuals with a classification of autism from the AGRE and 612 individuals with a classification of non-spectrum, of which 11 were from the AGRE, 4 were from the AC and the remaining 597 were from the simulated controls. The resulting classifier correctly classified 609 out of the 612 individuals with autism and all 612 individuals with a classification of non-spectrum (99.8% accuracy). The resulting ADTree consisted of seven items, six of which were also in the original classifier derived from the imbalanced data. Additionally, the ensuing ADTree remained largely unchanged from the original (data not shown), lending further support to the robustness of our classifier and supporting the notion that the imbalance of classes did not bias our results.



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