Release notes
Common
What's new
What's fixed
- Error during compilation of the kendo.common.less file
Badge
What's fixed
- Badge typescript declaration in jQuery interface is missing (#6139)
Calendar
What's fixed
- Calendar sets aria-selected attribute incorrectly (#5434)
ColumnMenu
What's fixed
- Column Menu with Component Type "modern" is missing translations for Columns buttons (#6387)
DatePicker
What's fixed
- DatePicker - readonly method doesn't remove aria-readonly attribute (#6445)
DateRangePicker
What's fixed
- Start changes to End Date when using keyboard to select start and mouse to select end (#5160)
Draggable
What's fixed
- Kendo draggable's autoscroll does not work with overflow hidden style (#4744)
- Draggable AutoScroll does not show the top (first) node or show half of the node. (#2101)
Editor
What's fixed
- Editor value() returns empty string when replacing an element with contenteditable="false" (#6434)
- Editor - two new lines are create on pressing Shift + Enter keys (#6159)
- Spelling correction leads to DOM change and incompatibility with the fontname tool (#5644)
- When deleting with backspace in Editor the fontSize and fontName are incorrectly changed to inherited (#4066)
FileManager
What's fixed
- ToolBar captures move and up cancel events on document level (#6002)
- Breadcrumb in FileManager does not respect custom id in Schema (#5798)
Gantt
What's fixed
- Custom view option does not appear in the Gantt DropDownList. (#6440)
Grid
What's fixed
- Tabbing in InCell editable grid with frozen columns is not correct (#6412)
- Grid Column reordering does not scroll the Grid horizontally. (#6398)
- Column Menu with Component Type "modern" is missing translations for Columns buttons (#6387)
- Kendo UI Grid attaches click events to widgets inside its detail template (#6358)
- data-menu is returning a string instead of a boolean (#6296)
- The aria-label value of column header is wrong when the column title is not configured (#6275)
- Not accessible CommandButtons with detail template (#5028)
- Grid dirty cell indicator is misplaced in RTL mode (#1960)
- "k-sorted" class is not removed from the "col" element
ListBox
What's fixed
- ListBox does not work with readers (#6371)
Localization
What's fixed
- Column Menu with Component Type "modern" is missing translations for Columns buttons (#6387)
MultiSelect
What's fixed
- MultiSelect filter accepts only string as value (#6435)
- MultiSelect doesn't focus the first suggested item of the popup (#6385)
- Incorrect elements returned by virtualized MultiSelect's DataItems (#6231)
- MultiSelect with virtualization enabled and mapValueTo: "dataItem" option set returns incorrect item (#5317)
MultiViewCalendar
What's fixed
- MultiViewCalendar does not select correct range when end/start is out of the current view
Notification
What's fixed
- Notification accessibility support (#5406)
NumericTextBox
What's fixed
- Copy/Paste in NumericTextBox with Factor Property outputs wrong number (#6307)
- Numerictextbox shadow doesn't appear on focus (#3977)
- NumericTextBox does not allow numbers with commas to be pasted (#2773)
PDF Export
What's fixed
- kendo pdf uses $ global variable (#6189)
Sortable
What's fixed
- When autoScroll is enabled hint is not shown above the first item (#3289)
Splitter
What's fixed
- ToolBar captures move and up cancel events on document level (#6002)
Spreadsheet
What's fixed
- Spreadsheet's dataSource change event is not triggered on pasting into a cell (#6378)
TextBox
What's fixed
- TextBox has incorrect height in LESS-based themes (#6428)
- TextBox changes null to empty string (#6350)
- Unable to change TextBox properties through setOptions method (#6110)
Timeline
What's fixed
- Vertical Timeline wrong dot event position when .ShowDateLabels(false) (#5629)
ToolBar
What's fixed
- ToolBar captures move and up cancel events on document level (#6002)
TreeList
What's fixed
- Wrong page number after clearing filter (#6309)
- TreeList renders an undefined class when the class is specified via the attributes (#5437)
TreeView
What's fixed
- Draggable AutoScroll does not show the top (first) node or show half of the node. (#2101)


Pizza Picadilly
Profile
Cart
Specials
Menu
Order History
Login
Email:
Password:
Signup
First name:
Last name:
Email:
Password:
Confirm Password:
Street Address 1:
Street Address 2:
City:
State:
Zipcode:


Master Felix:
Ice Cold Flower! I'll put this at her bedside when she sleeps.
You must visit the six stone tablets placed by the gods around our world.
The legendary Knight disappeared to undertake this pilgrimage! However...
"What?"


Problem:
Blockchain hackathons should lead by example in the cryptography and privacy-minded community.
Registration currently requires giving personal identifying information (PII) to a trusted third party (TTP).
“Trusted third parties are security holes.” – Nick Szabo
FUBAR: Universal Blockchain Anonymous Registration
FUBAR is:
- Trustless
- Secure
- Anonymous
- Autonomous
- Self-Sovereign
- Libre
- Open Source
- Decentralized
Features
- Configurable Registration Form (add the fields you need)
- Utilizes Metamask to issue a ERC721 token used as the ticket
- Check in with QR code, burns the ticket
- Cancelling registration burns the ticket, increasing the event capacity
Future
- Speaker/presentation acceptance via public vote
- Weighted vote tokens (like Bufficoin)
- Automated IRC/Riot/Matrix invites
- Email verification/updates
- Role-based permissions
- Admin, Organizer, Volunteer, etc.
Technicality:
- Creating an ERC721 contract, getting web3.js to invoke contract functions.
Creativity:
- We innovated on the shoulders of Colorado Coin and Bufficoin, combining the concepts as components of a hackathon
platform.
Usefulness:
- Immediately useful as an event management platform for conferences, summits, hackathons, etc.
Design:
- Responsive progressive web app utilizing Material Design
- Fooby mascot image
Makes the World a Better Place:
- Decentralizes and anonymizes personal information at blockchain events
- Educates users about the importance of self-sovereign identity


@args (Boolean loggedIn, String username) #* * Copyright 2017-2019 original authors * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *# ##tag::html[]


A characteristic feature of quantum field theory is the availability of unitarily inequivalent representations of its canonical commutation relations. Under the prima facie reasonable assumption that unitary equivalence is a necessary condition for physical equivalence, this availability implies that there are many physically inequivalent quantizations of any classical field theory. To explore this dramatic non-uniqueness, and its implications for our understanding of how physical theories delimit physical possibility, I examine some of the uses to which unitarily inequivalent representations are put in another setting in which they arise: the thermodynamic limit of quantum statistical mechanics.
It is argued that if the nature of the "equivalence" between first-quantized particle theories and second- quantized (Fock Space) theories is examined closely, if the inadequacies of de Muynck`s "indexed particle" version of Fock Space are recognized, and if the question is not begged against modal metaphysics, then van Fraassen`s attempted deflation of ontological issues in quantum theory can be seen to fail.
The hidden-variables model constructed by Karl Hess and Walter Philipp is claimed by its authors to exploit a "loophole" in Bell`s theorem; the claim is made by Hess and Philipp that the parameters employed in their model extend beyond those considered by Bell. Furthermore, they claim that their model satisfies Einstein locality and is free of any "suspicion of spooky action at a distance." Both of these claims are false; the Hess-Philipp model achieves agreement with the quantum-mechanical predictions, not by circumventing Bell`s theorem, but via Parameter Dependence.
Uncertainty about the actual orientation of the measurement device has been claimed to open a loophole for hidden variable models of quantum mechanics. In this paper I describe the statistics of inaccurate spin measurements by unsharp spin observables. A no-go theorem for hidden variable models of the inaccurate measurement statistics follows: There is a finite set of directions for which not all results of inaccurate spin measurements can be predetermined in a non-contextual way. In contrast to an earlier theorem [Breuer, Phys. Rev. Lett. 88(2002), 240402] this result does not rely on the assigment of approximate truth values, and it holds under weaker assumptions on the measurement inaccuracy.


--- title: Specialty Leasing subtitle: Specialty Leasing Partnerships permalink: "/retail-places/specialty-leasing/" class: specialty-leasing parent: Retail Places description: categories: specialty-leasing: name: Deborah Georgetti-Piro title: SVP Business Development category: team link: email<EMAIL_ADDRESS>phone:<PHONE_NUMBER> ---
Common area visibility from all angles brings consumers to your doorstep. Plus, we’ll help find the right neighbors to create a shopping environment that best suits your brand, and targets your customers.
Step outside of the four walls and realize the potential of your concept at the hub of a vibrant community. Our properties have the outdoor space to accommodate a variety of large format seasonal uses from ice skating to national tours, along with the team to help make it a success.


Everything Announced At ExileCon 2019's Opening Keynote
by Leah WilliamsExileCon, a celebration of the things Path of Exile, kicked off in Auckland, New Zealand on Saturday with a keynote presentation from Chris Wilson, the founder of developer Grinding Gear Games. From Path of Exile 2 to brand new expansions, Wilson showed off the future of Path of Exile, and it's an exciting future indeed. Here are all the major announcements, in one handy location.
Path of Exile 2
Path of Exile 2 is coming, with a gorgeous new visual overhaul, new skill systems and a new, seven act story.
Conquerors of Atlas
Conquerors of Atlas is a brand new end game story expansion, taking place after the fall of the Elder, and will require players to take on the Conquerors to reclaim and expand the Atlas map.
Metamorph League
Metamorph League is a new challenge league, where players find and collect samples from monsters to combine and create their own monsters, known as Metamorphs. This expansion also adds new catalysts that can improve mods on your equipped jewellery.
Path of Exile Mobile and Mac OS
Path of Exile is coming to mobile! While no date was revealed, the hype for the game to make the leap to mobile is real. The gameplay trailer looks great, and Grinding Gear Games promised gameplay with "zero compromises". It was also confirmed that Path of Exile would be coming to Mac OS.
Those are all the major reveals from ExileCon's keynote, with many more to come over the course of the weekend. Stay tuned for the rest of our coverage of ExileCon 2019!
Full disclosure: This writer attended ExileCon as a guest of Grinding Gear Games.


Name :Aarna Cruise
Contact No:<PHONE_NUMBER>
Email<EMAIL_ADDRESS>PROFILE
Creative UX Designer with 6+ years of experience in optimizing user experience through innovative solutions and
dynamic
interface designs.Successful in enhancing user engagement for well-known brands, providing a compelling user
experience
to improve brand loyalty and customer retention
PROFESSIONAL EXPERIENCE
User Experience Designer, September 2015 – Present ALLSTATE, Chicago, IL
- Create interactive 100+ user-centered designs, delivering innovative concepts, wireframes, low and high
fidelity prototypes, and engaging user experiences
- Facilitate 10+ members’ development team to ensure timely completion of projects
- Collaborate with senior level usability specialists and technology/business leaders to interpret
requirements into interactive experiences
- Conduct research and develop creative processes by sharing new trends and development technologies with
other designers
User Experience Designer, June 2011 – August 2015 TECH USA, Maitland, FL
- Examined 15+ existing sites in terms of persuasiveness, emotional design, and conversions such as measuring
properties for usefulness, visual design, and content
- Developed user interfaces and interaction flows of 20+ software applications for a variety of screen
sizes, tablets, and mobile platforms
- Achieved product milestones by establishing UX goals, priorities, and deliverables
- Employed effective user-centered design (UCD) approach to develop mobile applications and responsive designs
- Redesigned website which improved conversions by 200%, using JavaScript and HTML
EDUCATION
- UNIVERSITY OF NORTH TEXAS, Denton, TX . Masters in Interaction Design, May 2017
- B.S. WEB DEVELOPMENT .California State Univercity ,Fullerton CA -2013
- A A PROGRAMMING Fullerton college ,Fullerton CA -2010
- CERTIFIED Java Programmer 2010
ADDITIONAL SKILLS
- Advanced knowledge of HTML5 and CSS3 techniques
- Proficient MS Office (Word, Excel, and PowerPoint), Adobe Creative Suite, InDesign, Photoshop, Dreamweaver, and After Effects
- PHP,Web Design,Web Development
SOCIAL LINKS


The exam will begin promptly at Berkeley Time.
02:00:00
We want to make sure that we receive all of your exams.


Toggle navigation
<%= scriptAppName %>
Pricing Features
Service Features
Charging Products
Rules Library
User
Action
Another action
Something else here
Separated link


read_all_bytes(s = current_reader(), bufsize = 1024)
Return an unsigned byte array with all the remaining bytes from the binary
input stream s
. The bytes are read as chunks of size bufsize
.
If there are no more bytes in the stream, return the eof object.


Pirelli P Zero World opens in Melbourne
The store is Pirelli's fifth around the world, and is designed to make the most of Melbourne's strong prestige market relative to the rest of Australia.
by Scott Collie- shares
Pirelli has opened the Southern Hemisphere's first P Zero World in Preston, Victoria, to showcase everything the brand has to offer – when it comes to tyres, and more.
Designed to serve as a flagship store for the Pirelli brand – in a similar vein to the Mercedes Me store in the Melbourne CBD – P Zero World in Preston is the fifth store of its kind around the world.
The shop has been developed in conjunction with Mag Wheel Tyre, and part of its role is to serve as a regular tyre shop. There are five tyre-fitting bays, and the store stocks the full Pirelli range; from tyres for family SUVs to high-end, bespoke rubber for cars like the Lamborghini Urus.
According to the company, the majority of tyres sold at its P Zero World location before launch were 18-inch units or higher.
Along with the car tyres, the shop stocks motorbike and pushbike rubber. Pirelli will also use the store to showcase and sell its 'Pirelli Design' gear, developed alongside brands like Montblanc, MV Agusta, Puma and Roger Dubuis.
Pirelli has chosen Melbourne (or 13km north of the Melbourne CBD, to be precise) because of the concentration of high-end vehicles. Of the 5000 'prestige cars' registered in Australia this year, it says the highest number live in Victoria.
And although it offers tyres for garden variety SUVs and passenger cars, Pirelli is pitching P Zero World as somewhere owners of exotics and high-end performance cars can come to have their cars taken care of.
"We are focused on prestige and premium tyres," Dimitris Papadakos, CCO for Pirelli north-east Asia and Pacific, told media in Melbourne.
"We are preparing something similar to the expectations to the users of these cars. So let's say the owner of Lamborghini, Ferrari, Bugatti, Pagani, they're going to find a place similar to the expectations according to the car they have."
Melbourne also has a high concentration of Italian residents – apparently more than 36 per cent of Australia's Italian population lives there, making it a natural fit for Pirelli's flagship store.
The first P Zero World was launched in Los Angeles in 2016, while boutiques in Munich, Monaco and Dubai followed.
1
28
nextprevious
Pirelli P Zero World opens in Melbourne


Pizza Pizzazz
Select a pizza:
Select a pizza
Vegan Margherita Pizza
Very Veggie
Tofurky Lovers
Select your own topping:
Select a topping
Black Olives
Artichoke Hearts
Sundried Tomatoes
all of the toppings
Select a size:
Select a size
Small
Medium
Large
Get Price
A
pizza with
will cost
.


This section contains the general FAQs for common questions!
Please refer to either the short documentation section or the long documentation section for a thorough explanation.
Well there are different frameworks for resource loading. Shale Remoting, Spring Resource loading, JSF 2.0 resource loading.
It really depends on your needs of what you want to use
Shale Remoting supports JSF only since Shale is a pure JSF extension framework. Spring Resource Loading does not cover the client side aspects, it is code only JSF 2.0 simply is not here yet, and will also be jsf only
If you feel that one of those frameworks suits your needs, feel free to use it.
However, Weblets is here and works universally!
JSF 2.0 has specified localized resources, Weblets does not have them yet, however, having localized resources is a highly interesting, feature and definitely will be part of a future Weblets version.
As of Weblets 1.0 I would not recommend to serve all resources from weblets in high load sites although java has become quite fast and weblets supports mechanisms to reduce the server load it depends on the configuration.
If you have a working HTTP Server serves the static resources, app server serves the dynamic ones then you will not gain anything speedwise from using Weblets, on the contrary, you might lose performance or increase loads because Weblets serves the resources from your local classpath!
With later versions this mechanism might be expanded, if you want to use Weblets now use the weblets frontend API and never direct url calls, that way you stay on the save side and will get external urls once a redirection mechanism is in place!
Example, a web application has two customers, both of them should get different resources depending on the customer.
There are several ways to do that, a) You simply could use the el or dynamic string generation to switch dynamically between differente weblets
#weblet.url[dynamicbean.customerweblet]['/path/to/resource']
The other option would be to use an api extension point.
Weblets for instance has a managed bean which serves as the source of the url and resource functions. It does nothing fancy. It just defines two methods and returns jsf dummy maps
The class for the bean can be found under net.java.dev.weblets.impl.faces.FacesElWebletImpl You could instead of using the weblet bean define your own beans class which adds the customer information and alters the weblet name on the fly!
In other frameworks like jsp based ones you could write your own decorating tools class and use that one as weblet serving bean!
Check your log for error messages or warning messages, check your web.xml for path patterns which have to be set. If you are in a jsf environment and you seem to have any settings correct, yet it still fails to start, switch to the Weblets Servlet!
Also if you cannot find the cause of your problem, consult the mailing lists the developers lurk in the weblets mailinglist, happy to answer every question.
Sorry, you are out of luck, Weblets currently supports Servlet 2.4 and JDK4+.
Please send in patches over the bugtracker or mailing lists, every donation is welcome. Weblets is a non profit community project and thus is a two way street, you will get a lot but also every helping hand no matter how small the help is, is welcome! Even documentation error reports and/or fixes are welcome!


[t]This is the URL of your personal feed. Click on the icon, or copy/paste the URL to your favourite RSS reader.[/t]


About
Requirements Analysis, Design, Development, Testing, and Support of mission-critical applications with Software Programming and Analytical skills using Agile processes. Professional experience and aspirations include applications of Data Structures & Algorithms, Cloud Computing, Machine Learning, Big Data Analytics and Development of Web Applications.
Education
| Qualification | Speciality | Years | Institution |
|---|---|---|---|
| Master of Science | Computer Science | 2017 – 2019 | University of Texas at Arlington, Texas |
| Bachelor of Technology | Computer Science and Engineering | 2013 – 2017 | Jaypee Institute of Information Technology, India |
Technical Skills
Work Experience
Microsoft Corporations - Microsoft Teams IC3 Messaging
Software Engineer II - L 62
Sept 2022 - Present
- Worked as a team to improve data management and compliance of data storage. Worked to improve Thread Migrations/User Migration.
- Improved DTF framework by building libraries for efficient migration of data from different Geo’s.
- Collaborated across multiple teams to develop new features, stabilize systems, and improve infrastructure usability.
- Took part in design reviews for new features, addressed feedback, and acknowledged new features.
- Improved migrations by 10X implementing an L1 cache solution reducing calls to DB and helping in improving performance with downstream services.
- Performed code reviews, upheld coding standards, enhanced monitoring, and put efforts to bring down operational issues and improve process to have efficient operational readiness.
Amazon Web Services - S3 (Simple Storage Service)
Software Dev Engineer
June 2021 - Sept 2022
- Participated in Agile Team to Coordinate, Design, and Develop features to support storage management teams.
- Worked as part of the Metering/Intelligent Tiering team to assist in products to provide metering and insights across storage classes.
- Collaborated in a cross-team effort to make important life cycle features included to support millions of use cases.
- Developed complex algorithms to support test cases, integration tests, and come up with use cases that consider possibilities of uses of features.
- Designed Systems, System Changes, and test reflections to support Proof of concept, integration packages, integration methods, recommendations, capacity planning, and cost planning wrt to feature request.
- Participated in code reviews and worked on feature release for S3 Lifecycle Filters to integrate into storage metering to reflect changes based on customer configurations and also worked on introducing a new storage class - Glacier Instant Retrieval Archive recently launched in public.
Capital One Financial Services
Software Engineer
Feb 2020 – June 2021
- Participated in Agile Team to Co-ordinate, Design, and Develop cross-platform web application solutions to help multiple teams with GoLang, Flask, React, OAuth 2.0, and GraphQL.
- Strengthened Cloudbox – internal storage legacy application to support storing of files in S3 bucket with a simple UI with security controls and recognition which are generated by automated processes to support 30+ data engineering teams.
- Re-Engineered BFF for storage application used by front line loan agents with 10.5 Million+ monthly files using Python FastAPI, React, OAuth 2.0.
Projects / Publications
Map-Reduce Clustering on Hadoop and FIFA Visualization with ML Models
Jan 2019 – May 2019
Technologies: PySpark, Apache Spark, Pipelining, HDFS, Distributed Systems, Jupyter Notebook, Python, Big Data
Used PySpark on San Diego Supercomputer to create solutions for the given dataset on distributed mode on the FIFA dataset to assist models to generate solutions like best Players, Teams, and winning attributes. Engineered various optimizations to reduce shuffling and TTL of mappers. Classification was performed on a large dataset using algorithms such as Logistic Regression, Random Forest, and Decision Trees. Algorithms were executed on multi-node clusters to compare performance.
Medical Recommendation Intelligent System using Crawler in Java
Aug 2016 – May 2017
Technologies: Java 8, Advanced Algorithms, Crawler, Data Mining, Regression, NLP, Artificial Intelligence, Random Forest, Trees
Built a BFF in PHP to use the backend product in Java which used the novel use to predict ailments given the symptoms using symptoms to classify into diseases using data set crawled from WebMD.com.
MaxConnect4: Artificial Intelligence Advanced Algorithm Project
Aug 2017 – Dec 2017
Technologies: Java 8, Advanced Algorithms, Alpha Beta Pruning, A*, Artificial Intelligence, Trees
Interactive AI-based Implemented using Depth-limited Minimax algorithm with alpha-beta pruning. The algorithm computes 18 moves or able to achieve a depth of 17 in search tree in just over 30 seconds.
Connect With Me


public class PatternCard
extends java.lang.Object
implements java.io.Serializable
| Modifier and Type | Field and Description |
|---|---|
private boolean |
custom |
private int |
difficulty |
private java.lang.String |
name |
private java.util.ArrayList<GlassBox> |
pattern |
| Constructor and Description |
|---|
PatternCard()
Class default constructor re-definition
|
PatternCard(PatternCard patternCard)
Class copy constructor, create a new Pattern Card from an old Pattern Card,useful to create a safe copy
|
| Modifier and Type | Method and Description |
|---|---|
private boolean |
checkFirstDice(int index)
helper method to check if the first dice placed on the card is on edge or a corner
|
private boolean |
checkNearConstraint(Dice dice,
int index)
helper method used from the other method of the class to check if the dice obey to the restrictions
of the the near dice positioned
|
private boolean |
checkNearEmpty(int index)
helper method to check if the box at the specified index is empty
|
Dice |
getDice(int index)
method that provide the caller the dice positioned on the card at a specified index in the box list
|
int |
getDifficulty()
method that provide the caller the difficulty of the pattern card
|
java.lang.String |
getName()
method that provide the caller the name of the pattern card
|
java.util.ArrayList<GlassBox> |
getPattern()
method that provide the caller the list of the box of the pattern card at the moment of the call
|
boolean |
isCustom()
method that provide the caller information about the card, if it is a custom card created by the player
or is a standard card provided by the game
|
private boolean |
isPatternEmpty(PatternCard patternCard)
helper method to check if the pattern card is empty (no dice placed on it) or not
|
PatternCard |
loadCard(java.io.InputStream inputStream)
method that allow the caller to load a pattern card from a file json format
|
PatternCard |
loadPatternForTesting()
helper method that allow the caller to load a blank pattern card from file to use it for testing
|
java.util.ArrayList<PatternCard> |
loadPatternList()
method that allow the caller to load the list of the standard pattern card from file json
|
void |
putAnyDice(Dice dice,
int index)
method that allow the caller to put a dice if the box is empty ignoring the restrictions of the card
|
void |
putDice(Dice dice,
int index)
method that allow the user to put the dice on the box checking the constraint color or value
|
void |
putDiceCopper(Dice dice,
int index)
method that allow the user to put the dice on the box ignoring shade restrictions
|
void |
putDiceEglomise(Dice dice,
int index)
method that allow the user to put the dice on the box ignoring color restrictions
|
void |
putDiceOnPattern(Dice dice,
int index,
PatternCard patternCard)
method that allow the user to put a dice on a box of the pattern card indicated
|
void |
putDiceOnPatternCopper(Dice dice,
int index,
PatternCard patternCard)
method that allow the user to put a dice on a box of the pattern card indicated ignoring shade restrictions
|
void |
putDiceOnPatternEglomise(Dice dice,
int index,
PatternCard patternCard)
method that allow the user to put a dice on a box of the pattern card indicated ignoring color restrictions
|
Dice |
removeDice(int index)
method that allow the user to remove a dice from the box specified as parameter
|
void |
setCustom(boolean custom)
method that allow the caller to set if a card is a custom one or is a standard one
|
java.lang.String |
toString()
Override of the Object toString method to provide the caller of a String version of a Pattern Card
|
private java.lang.String name
private int difficulty
private java.util.ArrayList<GlassBox> pattern
private boolean custom
public PatternCard()
public PatternCard(PatternCard patternCard)
patternCard
- to copypublic boolean isCustom()
public int getDifficulty()
public java.lang.String getName()
public java.util.ArrayList<GlassBox> getPattern()
public Dice getDice(int index)
index
- of the box listpublic void setCustom(boolean custom)
custom
- boolean that specified the characteristic of the cardpublic void putAnyDice(Dice dice, int index)
dice
- to put on the boxindex
- of the box listpublic void putDice(Dice dice, int index) throws InvalidMoveException
dice
- to put on the boxindex
- of the box listInvalidMoveException
- if something goes wrongpublic void putDiceEglomise(Dice dice, int index) throws InvalidMoveException
dice
- to put on the boxindex
- of the box listInvalidMoveException
- if something goes wrongpublic void putDiceCopper(Dice dice, int index) throws InvalidMoveException
dice
- to put on the boxindex
- of the box listInvalidMoveException
- if something goes wrongpublic Dice removeDice(int index)
index
- of the box listpublic void putDiceOnPattern(Dice dice, int index, PatternCard patternCard) throws InvalidMoveException
dice
- to put on the boxindex
- of the box listpatternCard
- where to put the diceInvalidMoveException
- if the move is incorrectpublic void putDiceOnPatternEglomise(Dice dice, int index, PatternCard patternCard) throws InvalidMoveException
dice
- to put on the boxindex
- of the box listpatternCard
- where to put the diceInvalidMoveException
- if the move is incorrectpublic void putDiceOnPatternCopper(Dice dice, int index, PatternCard patternCard) throws InvalidMoveException
dice
- to put on the boxindex
- of the box listpatternCard
- where to put the diceInvalidMoveException
- if the move is incorrectprivate boolean checkNearConstraint(Dice dice, int index)
dice
- to put on the boxindex
- of the box listprivate boolean checkNearEmpty(int index)
index
- to checkprivate boolean checkFirstDice(int index)
index
- to checkprivate boolean isPatternEmpty(PatternCard patternCard)
patternCard
- to checkpublic java.util.ArrayList<PatternCard> loadPatternList()
public PatternCard loadCard(java.io.InputStream inputStream)
inputStream
- to get the resources as a streampublic PatternCard loadPatternForTesting()
public java.lang.String toString()
toString
in class java.lang.Object


Projects
IVoted
IVoted is a one-stop-shop for voters to get the information they need to register, get to the polls, and select candidates. This app is designed to organize relevant voting information from across the web for voters.This mobile-first web app is built in Rails using Foundation and scoured the web to pull together disparate sources of data to present each user with the information they need, the app presents data to the user in the form of a simple, user-friendly report.


Bring your payment of {{currentRegistration.remainingBalance | localizedCurrency:conference.currency.currencyCode}} to the event.


{% extends "layout.html" %} {% block title %}Calendar{% endblock %} {% block body %}
You may download the data file here.


If you have upper motor neuron lesions, you have damage to certain nerve cells that help you move. Medicines and therapy can help control symptoms like muscle spasms and help you walk and talk more easily.
Giving your brain downtime is crucial to its function. Practicing daily meditation can help alleviate stress and calm hyperactivity.
Neurological physiotherapy is the treatment of patients who have a neurological disorder. Neurological disorders are those affecting the brain, spinal cord and nerves; such as stroke, MS and Parkinson's disease.


description = "This is main layout of theme" forceUrl = 0 [homepage] security = "user" [account] redirect = "dashboard-teacher" [account_elearning] [sociallogin] [session] [sidebar_teacher] [sidebar_user] ==


| Class Singleton<T> Final | |
| Description: | Simple wrapper for the singleton pattern to save the developer from creating their own instance/getter global/function. |
| Details: | Singleton<MyClass>.Instance() This will return the singleton instance of MyClass, creating it if necessary. This uses the default New() constructor. |
| Table of contents: |


--- layout: post title: As the App Culture Builds, Dell Accelerates its Shift to Services with New Line of Servers, Flash Capabilities date: '2012-02-27T21:57:56-08:00' tags: tumblr_url: http://alexhwilliams.tumblr.com/post/18426715578/as-the-app-culture-builds-dell-accelerates-its-shift ---


{{ digitalImageSelect['resource_title'] }} ({{ digitalImageSelect['identifier'] }})
Thumbnails are not displayed by default because the resource includes more than 30 images.
Use the field below to specify which images to include,
or click 'Select items from thumbnails' to show all {{ digitalImageSelect['allImages'].length }}
thumbnail images and select them individually.
Select items from thumbnails
Specific items for reproduction


Packet Propagation¶
The bulk of a Monte Carlo Radiative Transfer calculation is spent on determining the propagation history of the different packets. After a packet is initialised, it is launched and may then perform interactions with the surrounding material. This occurs again in a probabilistic manner. The packet propagation is followed until it escapes through the outer boundary of the computational domain at which point the packet contributes to the synthetic spectrum, the main product of a TARDIS calculation. The different spectral features are simply a combined product of the changes in the packet properties induced in the radiation-matter interactions.
Initialization¶
During each TARDIS Monte Carlo simulation cycle, a large number \(N\) of Monte Carlo packets is initialised at the lower boundary of the computational (i.e. the photosphere). Since the inner boundary is currently treated as a black-body in TARDIS, \(N\) packets with energies
are initialised (the black body temperature \(T_{\mathrm{phot}}\), the photospheric radius \(R_{\mathrm{phot}}\), the Stefan-Boltzmann constant \(\sigma_{\mathrm{R}}\) and the physical duration of the simulation \(\Delta t\) appear here). To commence the packet propagation, each packet is assigned a initial propagation direction (recall that \(\mu = \cos \theta\) with \(\theta\) being the angle enclosed by the photon path and the radial direction)
and an initial frequency \(\nu\) in random number experiments, using a Random number generator which provides uniformly distributed numbers \(z\) on the interval \([0,1]\). The frequency assignment is more involved than selecting an initial propagation direction, since the Planck function has to be sampled. TARDIS uses the technique described in [CarterCashwell75] and summarized in [Bjorkmann1999] for this purpose.
Propagation in a spherical domain¶
Once the initial packet properties are assigned, the propagation process commences. Without interacting, a packet, like a photon, will propagate on a straight trajectory.
Note
Since TARDIS is designed for systems for which the Newtonian limit may be safely applied, no general relatistic effects which would force photons on curved trajectories are included.
In spherical geometry, this propagation process may be illustrated by the following sketch (taken from [Noebauer14]):
The packet starts the propagation at \(r_i\) along the direction \(\mu_i\). After covering a distance \(l\), the packet is now located at
Note that the propagation direction has also changed and now takes the value
Numerical and Physical Events¶
As a packet propagates through the computational domain, a number of events may trigger changes in the packet properties. Naturally, physical radiation-matter interactions are such events. These always occur after the packet has covered a distance corresponding to the optical depth (see Random Sampling)
which is again assigned probabilistically, in accordance with the stochastic nature of the Monte Carlo approach. Translating the optical depth to the next interaction into a physical distance is not straight-forward in the presence of frequency-dependent interaction process such as atomic line interactions. The detailed procedure is outlined in the following section.
In addition to the physical processes, numerical events which are a consequence of the spatial discretization of the computational domain require interrupting the propagating process. In TARDIS, as in many other numerical codes, physical quantities are stored on a discrete mesh. Within the different cells, which in our case are radial shells, these discrete values determine a (spatially) constant plasma state. As a consequence, whenever a packet propagates into a new cell, important quantities which are relevant for performing radiation-matter interactions have to be re-evaluated in accordance with the new state of the ambient material. Thus, during the packet propagation, the distance to the next radial shell is tracked to predict when the packet crosses into a new shell. Special care is taken at the edges of the computational domain. If a packet crosses back into the photosphere, it is discarded. Its propagation is stopped and it is no longer considered. Instead processing the next packet of the population is started. Similarly, the propagation is stopped if the packet escapes through the outer surface of the domain. However, in this case the packet contributes to the final emergent spectrum (see Spectrum Formation).
Physical Events¶
As noted above, translating the optical depth, which determines when the next physical interaction occurs, is non-trivial as soon as frequency-dependent processes are considered. Currently, TARDIS incorporates the electron scatterings and interactions with atomic line transitions. These two interactions mechanisms constitute the main sources of opacity in Type Ia supernovae.
Since the main focus of TARDIS is to calculate optical spectra, electron-scatterings are treated in the elastic low-energy limit as classical Thomson scatterings. In this case, the electron scattering process is frequency independent. It’s opacity only depends on the number density of free electrons \(n_e\)
The Thomson cross section \(\sigma_{\mathrm{T}}\), which is a constant, appears here. As a consequence to the frequency independence, a Monte Carlo packet accumulates optical depth along a path of length \(l\) due to Thomson scattering according to
The situation is complicated by the inclusion of frequency dependent bound-bound interactions, i.e. interactions with atomic line transitions. Photons and thus Monte Carlo packets can only interact with a line transition if their frequency in the co-moving frame (see Reference Frames) corresponds to the energy difference between the atomic levels linked by the transition, i.e. if it comes into resonance. As a photon/packet propagates through the homologously expanding ejecta, its co-moving frame frequency is continuously red-shifted. Thus, during its propagation through the SN ejecta, a photon/packet may come into resonance with many line transitions. This and the fact that line transitions have a finite width given by the line profile function (in the case at hand, this width is mainly given by thermal broadening) would render the determination of the line optical depth accumulated along the photon/packet trajectory a complicated task. Fortunately, the typical conditions in supernova ejecta warrant the use of the so-called Sobolev approximation (see Sobolev Approximation). Roughly speaking, this approximation replaces the line profile function with a \(\delta\) distribution around the natural line frequency. Thus, photons/packets may only interact with a line transition if their co-moving frame frequency exactly equals the natural frequency of the line. The location at which this occurs is referred to as the resonance or Sobolev point. This effectively reduces the line optical depth determination to a pure local problem.
With these assumptions, the calculation of the optical depth a packet accumulates along its trajectory currently adopted in TARDIS proceeds according to the following scheme (which was originally introduced by [MazzaliLucy93]): given the current lab-frame frequency of the packet, the distance to the next Sobolev point (i.e. to the next line resonance) is calculated
Until this location, the packet continuously accumulates optical depth due to electron-scattering. At the Sobolev point, the accumulated optical depth is instantaneously incremented by the full line optical depth. Afterwards, the procedure is repeated, now with respect to next transition in the frequency-ordered list of all possible atomic line transitions. The point at which the accumulated optical depth surpasses the value determined in the random number experiment described above (determining the distance to the next physical interaction), determines the type of interaction the packet performs and at which location in the spatial mesh. The entire process is summarized in the sketch below (taken from [Noebauer14], adapted from [MazzaliLucy93]):
Three possible cases are highlighted. In the first case, the drawn optical depth value is reached on one of the path segments between successive Sobolev points, while the packet accumulates electron scattering optical depth. Thus, the packet performs a Thomson scattering. In the second case, the accumulated optical depth is reached during the instantaneous increment by the line optical depth at one of the Sobolev points. As a consequence, the packet performs an interaction with the corresponding atomic line transition. Finally, if the packet reaches the shell boundary before the optical depth value necessary for a physical interaction is achieved, a numerical event grid cell cross event is reached (see above).
To conclude the description of the physical interaction mechanism, some details about the changes to the packet properties in case of interactions are provided. If the packet experiences a Thomson scattering, a new propagation direction is assigned. Since this process is isotropic, the new direction is sampled according to
In addition, energy conservation has in the local co-moving frame has to be obeyed. Thus, the packets energy and frequency in the lab-frame suffer from the relativistic Doppler shift
Here, the subscripts highlight the packet properties before (\(i\) for initial) and after (\(f\) for final) the scattering. Also, the common parameter of special relativity, \(\beta = v / c\), is used.
Line interactions proceed in a similar fashion. Since we assume that the re-emission process occurs isotropically as well, the same directional sampling as described above is used. Energy conservation again dictates how the packet energy after the line interaction event is determined. The important difference is the assignment of the post interaction frequency. This depends on the selected line interaction mode (see Line Interaction Modes).
Note
Note that the inclusion of special relativistic effects in TARDIS is at best to first order in \(\beta\).
Implementation: Main Propagation loop¶
In summary of the concepts outlined above, the main Monte Carlo process within TARDIS consists of successively processing all packets with represent the radiation field emitted by the photosphere in the following way:
- initialize the packet: assign initial energy, direction and frequency
- launch the packet: now the propagation of this packet is followed until one of the termination events is triggered
- follow the propagation:
- calculate the distance to the next shell and determine the distance to the next physical interaction
- the packet covers the shorter of these two distances:
- if the new shell is reached first, propagate into the shell and recalculate both distances
- if the packet has crossed through the inner domain boundary (photosphere), terminate propagation
- likewise in case the packet escapes through outer boundary (ejecta surface): account for contribution to emergent spectrum and terminate propagation
- if the interaction location is reached first, propagated to this location, perform interaction and recalculate both distances
- repeat this procedure until one of the two termination events occurs
The following flow chart summarizes this process again:
digraph { start[shape="box", style=rounded, label="Start"]; end[shape="box", style=rounded, label="End"]; allpacketsprocessed[shape="diamond", style="", label="All packets\nprocessed?"]; shortestdistance[shape="diamond", style="", label="Distance to next\nshell shortests?"]; outeredge[shape="diamond", style="", label="Escaping through\nsurface?"]; inneredge[shape="diamond", style="", label="Crossing into\nphotosphere?"]; nextpacket[shape="box", style="rounded", label="Select next\npacket"]; initpacket[shape="box", style="rounded", label="Initialize packet"]; calculatedistances[shape="box", style="rounded", label="Calculated distances:\nto next cell, to next interaction"]; crossintoshell[shape="box", style="rounded", label="Move packet into\nnext cell"]; terminate[shape="box", style="rounded", label="Terminate propagation,\ndiscard packet"]; interact[shape="box", style="rounded", label="Move packet to interaction location,\nperform interaction"]; spectralcontrib[shape="box", style="rounded", label="Determine contribution to spectrum"]; start -> allpacketsprocessed; allpacketsprocessed -> nextpacket[label="no"]; allpacketsprocessed -> end[label="yes"]; nextpacket -> initpacket; initpacket -> calculatedistances; calculatedistances -> shortestdistance; shortestdistance -> outeredge[label="yes"]; shortestdistance -> interact[label="no"]; interact -> calculatedistances; crossintoshell -> calculatedistances; outeredge -> spectralcontrib[label="yes"] outeredge -> inneredge[label="no"] inneredge -> terminate[label="yes"] inneredge -> crossintoshell[label="no"]; spectralcontrib -> terminate; terminate -> allpacketsprocessed; allpacketsprocessed[label="All packets\nprocessed?"] nextpacket[label="Select next packet\nfrom pool"] shortestdistance[label="Distance to cell\nedge shortest?"] }


Python Premise Syntax
Python_premise
python_premise ::= pattern '=' python_exp | pattern 'in' python_exp | 'check' python_exp | python_statements
Each of these clauses results in a Python expression being executed. Their meaning is as follows:
- pattern '=' python_exp
python_exp is evaluated and the result matched with pattern. If the result does not match, the clause fails.
The clause always fails on backtracking, meaning that it only produces a single result (contrasted with in).
- pattern 'in' python_exp
python_exp is evaluated to produce a Python iterable and the first element from the resulting iterable is matched with pattern. On backtracking, successive elements from the iterable are matched with pattern. When the result is exhausted, the clause fails.
This has the effect of offering each element of the result, one at a time, to the subsequent premise clauses. Each element is thus acted upon individually.
- 'check' python_exp
- python_exp is evaluated. If the result is Python "true" the clause succeeds, otherwise it fails. The clause always fails on backtracking.
Python_statements
python_statements ::= 'python' python_statement | 'python' NL INDENT {python_statement NL} DEINDENT
This clause allows the inclusion of arbitrary Python statements in your rules. This premise always succeeds; and then fails on backtracking.
The current knowledge_engine object is available within python_statements as the variable called engine.
Caution!
Always keep in mind the difference between pattern variables and Python variables. Pattern variables are always indicated with a $ and are only bound to a value during inferencing.
Thus, a python_statement may not set a pattern variable. Storing a value computed by Python into a pattern variable can only be done using the python_premise:
<pattern> = <some python expression>
When a pattern variable is used within a Python expression or statement, it must be fully bound.
Python variables are not visible to the inference engine. They are local variables that are also not visible to Python code in other rules or other invocations of the same rule.
Finally, Python variables in the when clause of a backward-chaining rule are not visible to the Python code in the with clause of the same rule. (These end up in two different Python functions after the .krb file is compiled). So this won't work:
some_bc_rule use some_goal(...) when ... python x_list = <some python expression> ... with for x in x_list: process(x)
In this case, assign the value of the Python variable to a pattern variable in the when clause and then use that pattern variable in the with clause:
some_bc_rule use some_goal(...) when ... python x_list = <some python expression> ... $x_list = tuple(x_list) with for x in $x_list: process(x)


Condon
This page contains an index of all the individuals in the database with the surname of Condon. Selecting the person’s name will take you to that person’s individual page.
| Given Name | Birth | Death | Partner | Parents |
|---|---|---|---|---|
| Alberta “Marjorie” | c1918 | Ridge, Leo Ronald | Condon, Sydney James Harding, Susan Mary | |
| Alethia Joyce | 1922 | 1969-09-04 | Condon, Sydney James Harding, Susan Mary | |
| Arthur Edward | c1913 | 1992-01-03 | Walsh, Irene Elizabeth | Condon, Sydney James Harding, Susan Mary |
| Edna | c1915 | Condon, Sydney James Harding, Susan Mary | ||
| Eunice | c1920 | c1915 | Condon, Sydney James Harding, Susan Mary | |
| Ronald | c1926 | Dyer, Lorraine | Condon, Sydney James Harding, Susan Mary | |
| Sydney James | 1879 | Harding, Susan Mary | ||
| Sydney Kenneth | c1924 | 1980-03-27 | Fletcher, Ivy | Condon, Sydney James Harding, Susan Mary |
| [Living] | McKenzie, Melville | Condon, Sydney James Harding, Susan Mary | ||
| [Living] | Condon, Ronald Dyer, Lorraine | |||
| [Living] | Condon, Sydney Kenneth Fletcher, Ivy | |||
| [Living] | Condon, Sydney Kenneth Fletcher, Ivy | |||
| [Living] | Condon, Arthur Edward Walsh, Irene Elizabeth | |||
| [Living] | Condon, Arthur Edward Walsh, Irene Elizabeth |


--- header: overlay_image: splash2.jpg caption: "Photo credit: [**Unsplash**](https://unsplash.com)" layout: archive author_profile: true --- {% include base_path %}


---
layout: page
title: "This is the title for your homepage"
date: 2013-08-16 23:48
comments: false
show_title: false
sidebar: false
hero_unit: true
is_homepage: true
---
{% include custom/homepage/grid_item_left.html %}
{% include custom/homepage/grid_item_middle.html %}
{% include custom/homepage/grid_item_right.html %}


Store
Thank you
for your purchase of
Inspect your transaction on the blockchain
record today's banana price on chain


set the attribute kui-fixed-width to true on the kui-icon element to set one or more icons to the same fixed width. This is great to use when varying icon widths (e.g. a tall but skinny icon atop a wide but short icon) would throw off vertical alignment. For clarity in the following example, we've added a background color on the icon so you can see the fixed width and also bumped up the font-size of the parent element.


Full Query Language Syntax
This section discusses the query language syntax, as defined in the Java Persistence API 2.0 specification available at http://jcp.org/en/jsr/detail?id=317. Much of the following material paraphrases or directly quotes the specification.
BNF Symbols
Table 34-1 describes the BNF symbols used in this chapter.
Symbol |
Description |
|---|---|
::= |
The element to the left of the symbol is defined by the constructs on the right. |
* |
The preceding construct may occur zero or more times. |
{...} |
The constructs within the braces are grouped together. |
[...] |
The constructs within the brackets are optional. |
| |
An exclusive OR. |
BOLDFACE |
A keyword; although capitalized in the BNF diagram, keywords are not case-sensitive. |
White space |
A whitespace character can be a space, a horizontal tab, or a line feed. |
BNF Grammar of the Java Persistence Query Language
Here is the entire BNF diagram for the query language:
QL_statement ::= select_statement | update_statement | delete_statement select_statement ::= select_clause from_clause [where_clause] [groupby_clause] [having_clause] [orderby_clause] update_statement ::= update_clause [where_clause] delete_statement ::= delete_clause [where_clause] from_clause ::= FROM identification_variable_declaration {, {identification_variable_declaration | collection_member_declaration}}* identification_variable_declaration ::= range_variable_declaration { join | fetch_join }* range_variable_declaration ::= abstract_schema_name [AS] identification_variable join ::= join_spec join_association_path_expression [AS] identification_variable fetch_join ::= join_specFETCH join_association_path_expression association_path_expression ::= collection_valued_path_expression | single_valued_association_path_expression join_spec::= [LEFT [OUTER] |INNER] JOIN join_association_path_expression ::= join_collection_valued_path_expression | join_single_valued_association_path_expression join_collection_valued_path_expression::= identification_variable.collection_valued_association_field join_single_valued_association_path_expression::= identification_variable.single_valued_association_field collection_member_declaration ::= IN (collection_valued_path_expression) [AS] identification_variable single_valued_path_expression ::= state_field_path_expression | single_valued_association_path_expression state_field_path_expression ::= {identification_variable | single_valued_association_path_expression}.state_field single_valued_association_path_expression ::= identification_variable.{single_valued_association_field.}* single_valued_association_field collection_valued_path_expression ::= identification_variable.{single_valued_association_field.}* collection_valued_association_field state_field ::= {embedded_class_state_field.}*simple_state_field update_clause ::=UPDATE abstract_schema_name [[AS] identification_variable] SET update_item {, update_item}* update_item ::= [identification_variable.]{state_field | single_valued_association_field} = new_value new_value ::= simple_arithmetic_expression | string_primary | datetime_primary | boolean_primary | enum_primary simple_entity_expression | NULL delete_clause ::= DELETE FROM abstract_schema_name [[AS] identification_variable] select_clause ::= SELECT [DISTINCT] select_expression {, select_expression}* select_expression ::= single_valued_path_expression | aggregate_expression | identification_variable | OBJECT(identification_variable) | constructor_expression constructor_expression ::= NEW constructor_name(constructor_item {, constructor_item}*) constructor_item ::= single_valued_path_expression | aggregate_expression aggregate_expression ::= {AVG |MAX |MIN |SUM} ([DISTINCT] state_field_path_expression) | COUNT ([DISTINCT] identification_variable | state_field_path_expression | single_valued_association_path_expression) where_clause ::= WHERE conditional_expression groupby_clause ::= GROUP BY groupby_item {, groupby_item}* groupby_item ::= single_valued_path_expression having_clause ::= HAVING conditional_expression orderby_clause ::= ORDER BY orderby_item {, orderby_item}* orderby_item ::= state_field_path_expression [ASC |DESC] subquery ::= simple_select_clause subquery_from_clause [where_clause] [groupby_clause] [having_clause] subquery_from_clause ::= FROM subselect_identification_variable_declaration {, subselect_identification_variable_declaration}* subselect_identification_variable_declaration ::= identification_variable_declaration | association_path_expression [AS] identification_variable | collection_member_declaration simple_select_clause ::= SELECT [DISTINCT] simple_select_expression simple_select_expression::= single_valued_path_expression | aggregate_expression | identification_variable conditional_expression ::= conditional_term | conditional_expression OR conditional_term conditional_term ::= conditional_factor | conditional_term AND conditional_factor conditional_factor ::= [NOT] conditional_primary conditional_primary ::= simple_cond_expression |( conditional_expression) simple_cond_expression ::= comparison_expression | between_expression | like_expression | in_expression | null_comparison_expression | empty_collection_comparison_expression | collection_member_expression | exists_expression between_expression ::= arithmetic_expression [NOT] BETWEEN arithmetic_expressionAND arithmetic_expression | string_expression [NOT] BETWEEN string_expression AND string_expression | datetime_expression [NOT] BETWEEN datetime_expression AND datetime_expression in_expression ::= state_field_path_expression [NOT] IN (in_item {, in_item}* | subquery) in_item ::= literal | input_parameter like_expression ::= string_expression [NOT] LIKE pattern_value [ESCAPE escape_character] null_comparison_expression ::= {single_valued_path_expression | input_parameter} IS [NOT] NULL empty_collection_comparison_expression ::= collection_valued_path_expression IS [NOT] EMPTY collection_member_expression ::= entity_expression [NOT] MEMBER [OF] collection_valued_path_expression exists_expression::= [NOT] EXISTS (subquery) all_or_any_expression ::= {ALL |ANY |SOME} (subquery) comparison_expression ::= string_expression comparison_operator {string_expression | all_or_any_expression} | boolean_expression {= |<> } {boolean_expression | all_or_any_expression} | enum_expression {= |<> } {enum_expression | all_or_any_expression} | datetime_expression comparison_operator {datetime_expression | all_or_any_expression} | entity_expression {= |<> } {entity_expression | all_or_any_expression} | arithmetic_expression comparison_operator {arithmetic_expression | all_or_any_expression} comparison_operator ::= = |> |>= |< |<= |<> arithmetic_expression ::= simple_arithmetic_expression | (subquery) simple_arithmetic_expression ::= arithmetic_term | simple_arithmetic_expression {+ |- } arithmetic_term arithmetic_term ::= arithmetic_factor | arithmetic_term {* |/ } arithmetic_factor arithmetic_factor ::= [{+ |- }] arithmetic_primary arithmetic_primary ::= state_field_path_expression | numeric_literal | (simple_arithmetic_expression) | input_parameter | functions_returning_numerics | aggregate_expression string_expression ::= string_primary | (subquery) string_primary ::= state_field_path_expression | string_literal | input_parameter | functions_returning_strings | aggregate_expression datetime_expression ::= datetime_primary | (subquery) datetime_primary ::= state_field_path_expression | input_parameter | functions_returning_datetime | aggregate_expression boolean_expression ::= boolean_primary | (subquery) boolean_primary ::= state_field_path_expression | boolean_literal | input_parameter enum_expression ::= enum_primary | (subquery) enum_primary ::= state_field_path_expression | enum_literal | input_parameter entity_expression ::= single_valued_association_path_expression | simple_entity_expression simple_entity_expression ::= identification_variable | input_parameter functions_returning_numerics::= LENGTH(string_primary) | LOCATE(string_primary, string_primary[, simple_arithmetic_expression]) | ABS(simple_arithmetic_expression) | SQRT(simple_arithmetic_expression) | MOD(simple_arithmetic_expression, simple_arithmetic_expression) | SIZE(collection_valued_path_expression) functions_returning_datetime ::= CURRENT_DATE | CURRENT_TIME | CURRENT_TIMESTAMP functions_returning_strings ::= CONCAT(string_primary, string_primary) | SUBSTRING(string_primary, simple_arithmetic_expression, simple_arithmetic_expression)| TRIM([[trim_specification] [trim_character] FROM] string_primary) | LOWER(string_primary) | UPPER(string_primary) trim_specification ::= LEADING | TRAILING | BOTH
FROM Clause
The FROM clause defines the domain of the query by declaring identification variables.
Identifiers
An identifier is a sequence of one or more characters. The first character must be a valid first character (letter, $, _) in an identifier of the Java programming language, hereafter in this chapter called simply “Java”. Each subsequent character in the sequence must be a valid nonfirst character (letter, digit, $, _) in a Java identifier. (For details, see the Java SE API documentation of the isJavaIdentifierStart and isJavaIdentifierPart methods of the Character class.) The question mark (?) is a reserved character in the query language and cannot be used in an identifier.
A query language identifier is case-sensitive, with two exceptions:
Keywords
Identification variables
An identifier cannot be the same as a query language keyword. Here is a list of query language keywords:
ABS |
ALL |
AND |
ANY |
AS |
ASC |
AVG |
BETWEEN |
BIT_LENGTH |
BOTH |
BY |
CASE |
CHAR_LENGTH |
CHARACTER_LENGTH |
CLASS |
COALESCE |
CONCAT |
COUNT |
CURRENT_DATE |
CURRENT_TIMESTAMP |
DELETE |
DESC |
DISTINCT |
ELSE |
EMPTY |
END |
ENTRY |
ESCAPE |
EXISTS |
FALSE |
FETCH |
FROM |
GROUP |
HAVING |
IN |
INDEX |
INNER |
IS |
JOIN |
KEY |
LEADING |
LEFT |
LENGTH |
LIKE |
LOCATE |
LOWER |
MAX |
MEMBER |
MIN |
MOD |
NEW |
NOT |
NULL |
NULLIF |
OBJECT |
OF |
OR |
ORDER |
OUTER |
POSITION |
SELECT |
SET |
SIZE |
SOME |
SQRT |
SUBSTRING |
SUM |
THEN |
TRAILING |
TRIM |
TRUE |
TYPE |
UNKNOWN |
UPDATE |
UPPER |
VALUE |
WHEN |
WHERE |
||
It is not recommended that you use an SQL keyword as an identifier, because the list of keywords may expand to include other reserved SQL words in the future.
Identification Variables
An identification variable is an identifier declared in the FROM clause. Although they can reference identification variables, the SELECT and WHERE clauses cannot declare them. All identification variables must be declared in the FROM clause.
Because it is an identifier, an identification variable has the same naming conventions and restrictions as an identifier, with the exception that an identification variables is case-insensitive. For example, an identification variable cannot be the same as a query language keyword. (See the preceding section for more naming rules.) Also, within a given persistence unit, an identification variable name must not match the name of any entity or abstract schema.
The FROM clause can contain multiple declarations, separated by commas. A declaration can reference another identification variable that has been previously declared (to the left). In the following FROM clause, the variable t references the previously declared variable p:
FROM Player p, IN (p.teams) AS t
Even if it is not used in the WHERE clause, an identification variable's declaration can affect the results of the query. For example, compare the next two queries. The following query returns all players, whether or not they belong to a team:
SELECT p FROM Player p
In contrast, because it declares the t identification variable, the next query fetches all players who belong to a team:
SELECT p FROM Player p, IN (p.teams) AS t
The following query returns the same results as the preceding query, but the WHERE clause makes it easier to read:
SELECT p FROM Player p WHERE p.teams IS NOT EMPTY
An identification variable always designates a reference to a single value whose type is that of the expression used in the declaration. There are two kinds of declarations: range variable and collection member.
Range Variable Declarations
To declare an identification variable as an abstract schema type, you specify a range variable declaration. In other words, an identification variable can range over the abstract schema type of an entity. In the following example, an identification variable named p represents the abstract schema named Player:
FROM Player p
A range variable declaration can include the optional AS operator:
FROM Player AS p
To obtain objects, a query usually uses path expressions to navigate through the relationships. But for those objects that cannot be obtained by navigation, you can use a range variable declaration to designate a starting point, or root.
If the query compares multiple values of the same abstract schema type, the FROM clause must declare multiple identification variables for the abstract schema:
FROM Player p1, Player p2
For an example of such a query, see Comparison Operators.
Collection Member Declarations
In a one-to-many relationship, the multiple side consists of a collection of entities. An identification variable can represent a member of this collection. To access a collection member, the path expression in the variable’s declaration navigates through the relationships in the abstract schema. (For more information on path expressions, see Path Expressions.) Because a path expression can be based on another path expression, the navigation can traverse several relationships. See Traversing Multiple Relationships.
A collection member declaration must include the IN operator but can omit the optional AS operator.
In the following example, the entity represented by the abstract schema named Player has a relationship field called teams. The identification variable called t represents a single member of the teams collection.
FROM Player p, IN (p.tea ms) t
Joins
The JOIN operator is used to traverse over relationships between entities and is functionally similar to the IN operator.
In the following example, the query joins over the relationship between customers and orders:
SELECT c FROM Customer c JOIN c.orders o WHERE c.status = 1 AND o.totalPrice > 10000
The INNER keyword is optional:
SELECT c FROM Customer c INNER JOIN c.orders o WHERE c.status = 1 AND o.totalPrice > 10000
These examples are equivalent to the following query, which uses the IN operator:
SELECT c FROM Customer c, IN(c.orders) o WHERE c.status = 1 AND o.totalPrice > 10000
You can also join a single-valued relationship:
SELECT t FROM Team t JOIN t.league l WHERE l.sport = :sport
A LEFT JOIN or LEFT OUTER JOIN retrieves a set of entities where matching values in the join condition may be absent. The OUTER keyword is optional.
SELECT c.name, o.totalPrice FROM Order o LEFT JOIN o.customer c
A FETCH JOIN is a join operation that returns associated entities as a side effect of running the query. In the following example, the query returns a set of departments and, as a side effect, the associated employees of the departments, even though the employees were not explicitly retrieved by the SELECT clause.
SELECT d FROM Department d LEFT JOIN FETCH d.employees WHERE d.deptno = 1
Path Expressions
Path expressions are important constructs in the syntax of the query language, for several reasons. First, path expressions define navigation paths through the relationships in the abstract schema. These path definitions affect both the scope and the results of a query. Second, path expressions can appear in any of the main clauses of a query (SELECT, DELETE, HAVING, UPDATE, WHERE, FROM, GROUP BY, ORDER BY). Finally, although much of the query language is a subset of SQL, path expressions are extensions not found in SQL.
Examples of Path Expressions
Here, the WHERE clause contains a single_valued_path_expression; the p is an identification variable, and salary is a persistent field of Player:
SELECT DISTINCT p FROM Player p WHERE p.salary BETWEEN :lowerSalary AND :higherSalary
Here, the WHERE clause also contains a single_valued_path_expression; t is an identification variable, league is a single-valued relationship field, and sport is a persistent field of league:
SELECT DISTINCT p FROM Player p, IN (p.teams) t WHERE t.league.sport = :sport
Here, the WHERE clause contains a collection_valued_path_expression; p is an identification variable, and teams designates a collection-valued relationship field:
SELECT DISTINCT p FROM Player p WHERE p.teams IS EMPTY
Expression Types
The type of a path expression is the type of the object represented by the ending element, which can be one of the following:
Persistent field
Single-valued relationship field
Collection-valued relationship field
For example, the type of the expression p.salary is double because the terminating persistent field (salary) is a double.
In the expression p.teams, the terminating element is a collection-valued relationship field (teams). This expression’s type is a collection of the abstract schema type named Team. Because Team is the abstract schema name for the Team entity, this type maps to the entity. For more information on the type mapping of abstract schemas, see Return Types.
Navigation
A path expression enables the query to navigate to related entities. The terminating elements of an expression determine whether navigation is allowed. If an expression contains a single-valued relationship field, the navigation can continue to an object that is related to the field. However, an expression cannot navigate beyond a persistent field or a collection-valued relationship field. For example, the expression p.teams.league.sport is illegal because teams is a collection-valued relationship field. To reach the sport field, the FROM clause could define an identification variable named t for the teams field:
FROM Player AS p, IN (p.teams) t WHERE t.league.sport = ’soccer’
WHERE Clause
The WHERE clause specifies a conditional expression that limits the values returned by the query. The query returns all corresponding values in the data store for which the conditional expression is TRUE. Although usually specified, the WHERE clause is optional. If the WHERE clause is omitted, the query returns all values. The high-level syntax for the WHERE clause follows:
where_clause ::= WHERE conditional_expression
Literals
There are four kinds of literals: string, numeric, Boolean, and enum.
String literals: A string literal is enclosed in single quotes:
'Duke'
If a string literal contains a single quote, you indicate the quote by using two single quotes:
'Duke''s'
Like a Java String, a string literal in the query language uses the Unicode character encoding.
Numeric literals: There are two types of numeric literals: exact and approximate.
An exact numeric literal is a numeric value without a decimal point, such as 65, –233, and +12. Using the Java integer syntax, exact numeric literals support numbers in the range of a Java long.
An approximate numeric literal is a numeric value in scientific notation, such as 57., –85.7, and +2.1. Using the syntax of the Java floating-point literal, approximate numeric literals support numbers in the range of a Java double.
Boolean literals: A Boolean literal is either TRUE or FALSE. These keywords are not case-sensitive.
Enum literals: The Java Persistence query language supports the use of enum literals using the Java enum literal syntax. The enum class name must be specified as a fully qualified class name:
SELECT e FROM Employee e WHERE e.status = com.xyz.EmployeeStatus.FULL_TIME
Input Parameters
An input parameter can be either a named parameter or a positional parameter.
A named input parameter is designated by a colon (:) followed by a string; for example, :name.
A positional input parameter is designated by a question mark (?) followed by an integer. For example, the first input parameter is ?1, the second is ?2, and so forth.
The following rules apply to input parameters.
They can be used only in a WHERE or HAVING clause.
Positional parameters must be numbered, starting with the integer 1.
Named parameters and positional parameters may not be mixed in a single query.
Named parameters are case-sensitive.
Conditional Expressions
A WHERE clause consists of a conditional expression, which is evaluated from left to right within a precedence level. You can change the order of evaluation by using parentheses.
Operators and Their Precedence
Table 34-2 lists the query language operators in order of decreasing precedence.
Type |
Precedence Order |
|---|---|
Navigation |
. (a period) |
Arithmetic |
+ – (unary) * / (multiplication and division) + – (addition and subtraction) |
Comparison |
= > >= < <= <> (not equal) [NOT] BETWEEN [NOT] LIKE [NOT] IN IS [NOT] NULL IS [NOT] EMPTY [NOT] MEMBER OF |
Logical |
NOT AND OR |
BETWEEN Expressions
A BETWEEN expression determines whether an arithmetic expression falls within a range of values.
These two expressions are equivalent:
p.age BETWEEN 15 AND 19 p.age >= 15 AND p.age <= 19
The following two expressions also are equivalent:
p.age NOT BETWEEN 15 AND 19 p.age < 15 OR p.age > 19
If an arithmetic expression has a NULL value, the value of the BETWEEN expression is unknown.
IN Expressions
An IN expression determines whether a string belongs to a set of string literals or whether a number belongs to a set of number values.
The path expression must have a string or numeric value. If the path expression has a NULL value, the value of the IN expression is unknown.
In the following example, the expression is TRUE if the country is UK , but FALSE if the country is Peru.
o.country IN ('UK', 'US', 'France')
You may also use input parameters:
o.country IN ('UK', 'US', 'France', :country)
LIKE Expressions
A LIKE expression determines whether a wildcard pattern matches a string.
The path expression must have a string or numeric value. If this value is NULL, the value of the LIKE expression is unknown. The pattern value is a string literal that can contain wildcard characters. The underscore (_) wildcard character represents any single character. The percent (%) wildcard character represents zero or more characters. The ESCAPE clause specifies an escape character for the wildcard characters in the pattern value. Table 34-3 shows some sample LIKE expressions.
Expression |
TRUE |
FALSE |
|---|---|---|
address.phone LIKE '12%3' |
'123' '12993' |
'1234' |
asentence.word LIKE 'l_se' |
'lose' |
'loose' |
aword.underscored LIKE '\_%' ESCAPE '\' |
'_foo' |
'bar' |
address.phone NOT LIKE '12%3' |
'1234' |
'123' '12993' |
NULL Comparison Expressions
A NULL comparison expression tests whether a single-valued path expression or an input parameter has a NULL value. Usually, the NULL comparison expression is used to test whether a single-valued relationship has been set:
SELECT t FROM Team t WHERE t.league IS NULL
This query selects all teams where the league relationship is not set. Note that the following query is not equivalent:
SELECT t FROM Team t WHERE t.league = NULL
The comparison with NULL using the equals operator (=) always returns an unknown value, even if the relationship is not set. The second query will always return an empty result.
 In other words, it tests whether a collection-valued relationship has been set.
If the collection-valued path expression is NULL, the empty collection comparison expression has a NULL value.
Here is an example that finds all orders that do not have any line items:
SELECT o FROM Order o WHERE o.lineItems IS EMPTY
Collection Member Expressions
The [NOT] MEMBER [OF] collection member expression determines whether a value is a member of a collection. The value and the collection members must have the same type.
If either the collection-valued or single-valued path expression is unknown, the collection member expression is unknown. If the collection-valued path expression designates an empty collection, the collection member expression is FALSE.
The OF keyword is optional.
The following example tests whether a line item is part of an order:
SELECT o FROM Order o WHERE :lineItem MEMBER OF o.lineItems
Subqueries
Subqueries may be used in the WHERE or HAVING clause of a query. Subqueries must be surrounded by parentheses.
The following example finds all customers who have placed more than ten orders:
SELECT c FROM Customer c WHERE (SELECT COUNT(o) FROM c.orders o) > 10
Subqueries may contain EXISTS, ALL, and ANY expressions.
EXISTS expressions: The [NOT] EXISTS expression is used with a subquery and is true only if the result of the subquery consists of one or more values and is false otherwise.
The following example finds all employees whose spouses are also employees:
SELECT DISTINCT emp FROM Employee emp WHERE EXISTS ( SELECT spouseEmp FROM Employee spouseEmp WHERE spouseEmp = emp.spouse)
ALL and ANY expressions: The ALL expression is used with a subquery and is true if all the values returned by the subquery are true or if the subquery is empty.
The ANY expression is used with a subquery and is true if some of the values returned by the subquery are true. An ANY expression is false if the subquery result is empty or if all the values returned are false. The SOME keyword is synonymous with ANY.

The following example finds all employees whose salaries are higher than the salaries of the managers in the employee’s department:
SELECT emp FROM Employee emp WHERE emp.salary > ALL ( SELECT m.salary FROM Manager m WHERE m.department = emp.department)
Functional Expressions
The query language includes several string, arithmetic, and date/time functions that may be used in the SELECT, WHERE, or HAVING clause of a query. The functions are listed in Table 34-4, Table 34-5, and Table 34-6.
In Table 34-4, the start and length arguments are of type int and designate positions in the String argument. The first position in a string is designated by 1.
Function Syntax |
Return Type |
|---|---|
CONCAT(String, String) |
String |
LENGTH(String) |
int |
LOCATE(String, String [, start]) |
int |
SUBSTRING(String, start, length) |
String |
TRIM([[LEADING|TRAILING|BOTH] char) FROM] (String) |
String |
LOWER(String) |
String |
UPPER(String) |
String |
The CONCAT function concatenates two strings into one string.
The LENGTH function returns the length of a string in characters as an integer.
The LOCATE function returns the position of a given string within a string. This function returns the first position at which the string was found as an integer. The first argument is the string to be located. The second argument is the string to be searched. The optional third argument is an integer that represents the starting string position. By default, LOCATE starts at the beginning of the string. The starting position of a string is 1. If the string cannot be located, LOCATE returns 0.
The SUBSTRING function returns a string that is a substring of the first argument based on the starting position and length.
The TRIM function trims the specified character from the beginning and/or end of a string. If no character is specified, TRIM removes spaces or blanks from the string. If the optional LEADING specification is used, TRIM removes only the leading characters from the string. If the optional TRAILING specification is used, TRIM removes only the trailing characters from the string. The default is BOTH, which removes the leading and trailing characters from the string.
The LOWER and UPPER functions convert a string to lowercase or uppercase, respectively.
In Table 34-5, the number argument can be an int, a float, or a double.
Function Syntax |
Return Type |
|---|---|
ABS(number) |
int, float, or double |
MOD(int, int) |
int |
SQRT(double) |
double |
SIZE(Collection) |
int |
The ABS function takes a numeric expression and returns a number of the same type as the argument.
The MOD function returns the remainder of the first argument divided by the second.
The SQRT function returns the square root of a number.
The SIZE function returns an integer of the number of elements in the given collection.
In Table 34-6, the date/time functions return the date, time, or timestamp on the database server.
Function Syntax |
Return Type |
|---|---|
CURRENT_DATE |
java.sql.Date |
CURRENT_TIME |
java.sql.Time |
CURRENT_TIMESTAMP |
java.sql.Timestamp |
Case Expressions
Case expressions change based on a condition, similar to the case keyword of the Java programming language. The CASE keyword indicates the start of a case expression, and the expression is terminated by the END keyword. The WHEN and THEN keywords define individual conditions, and the ELSE keyword defines the default condition should none of the other conditions be satisfied.
The following query selects the name of a person and a conditional string, depending on the subtype of the Person entity. If the subtype is Student, the string kid is returned . If the subtype is Guardian or Staff, the string adult is returned. If the entity is some other subtype of Person, the string unknown is returned.
SELECT p.name CASE TYPE(p) WHEN Student THEN 'kid' WHEN Guardian THEN 'adult' WHEN Staff THEN 'adult' ELSE 'unknown' END FROM Person p
The following query sets a discount for various types of customers.
UPDATE Customer c SET c.discount = CASE c.level WHEN 'Gold' THEN 20 WHEN 'SILVER' THEN 15 WHEN 'Bronze' THEN 10 ELSE 5 END
NULL Values
If the target of a reference is not in the persistent store, the target is NULL. For conditional expressions containing NULL, the query language uses the semantics defined by SQL92. Briefly, these semantics are as follows.
If a comparison or arithmetic operation has an unknown value, it yields a NULL value.
Two NULL values are not equal. Comparing two NULL values yields an unknown value.
The IS NULL test converts a NULL persistent field or a single-valued relationship field to TRUE. The IS NOT NULL test converts them to FALSE.
Boolean operators and conditional tests use the three-valued logic defined by Table 34-7 and Table 34-8. (In these tables, T stands for TRUE, F for FALSE, and U for unknown.)
AND |
T |
F |
U |
|---|---|---|---|
T |
T |
F |
U |
F |
F |
F |
F |
U |
U |
F |
U |
OR |
T |
F |
U |
|---|---|---|---|
T |
T |
T |
T |
F |
T |
F |
U |
U |
T |
U |
U |
Equality Semantics
In the query language, only values of the same type can be compared. However, this rule has one exception: Exact and approximate numeric values can be compared. In such a comparison, the required type conversion adheres to the rules of Java numeric promotion.
The query language treats compared values as if they were Java types and not as if they represented types in the underlying data store. For example, a persistent field that could be either an integer or a NULL must be designated as an Integer object and not as an int primitive. This designation is required because a Java object can be NULL, but a primitive cannot.
Two strings are equal only if they contain the same sequence of characters. Trailing blanks are significant; for example, the strings 'abc' and 'abc ' are not equal.
Two entities of the same abstract schema type are equal only if their primary keys have the same value. Table 34-9 shows the operator logic of a negation, and Table 34-10 shows the truth values of conditional tests.
NOT Value |
Value |
|---|---|
T |
F |
F |
T |
U |
U |
Conditional Test |
T |
F |
U |
|---|---|---|---|
Expression IS TRUE |
T |
F |
F |
Expression IS FALSE |
F |
T |
F |
Expression is unknown |
F |
F |
T |
SELECT Clause
The SELECT clause defines the types of the objects or values returned by the query.
Return Types
The return type of the SELECT clause is defined by the result types of the select expressions contained within it. If multiple expressions are used, the result of the query is an Object[], and the elements in the array correspond to the order of the expressions in the SELECT clause and in type to the result types of each expression.
A SELECT clause cannot specify a collection-valued expression. For example, the SELECT clause p.teams is invalid because teams is a collection. However, the clause in the following query is valid because the t is a single element of the teams collection:
SELECT t FROM Player p, IN (p.teams) t
The following query is an example of a query with multiple expressions in the SELECT clause:
SELECT c.name, c.country.name FROM customer c WHERE c.lastname = 'Coss' AND c.firstname = 'Roxane'
This query returns a list of Object[] elements; the first array element is a string denoting the customer name, and the second array element is a string denoting the name of the customer’s country.
The result of a query may be the result of an aggregate function, listed in Table 34-11.
Name |
Return Type |
Description |
|---|---|---|
Double |
Returns the mean average of the fields |
|
Long |
Returns the total number of results |
|
The type of the field |
Returns the highest value in the result set |
|
The type of the field |
Returns the lowest value in the result set |
|
Long (for integral fields) Double (for floating-point fields) BigInteger (for BigInteger fields) BigDecimal (for BigDecimal fields) |
Returns the sum of all the values in the result set |
For select method queries with an aggregate function (AVG, COUNT, MAX, MIN, or SUM) in the SELECT clause, the following rules apply:
The AVG, MAX, MIN, and SUM functions return null if there are no values to which the function can be applied.
The COUNT function returns 0 if there are no values to which the function can be applied.
The following example returns the average order quantity:
SELECT AVG(o.quantity) FROM Order o
The following example returns the total cost of the items ordered by Roxane Coss:
SELECT SUM(l.price) FROM Order o JOIN o.lineItems l JOIN o.customer c WHERE c.lastname = 'Coss' AND c.firstname = 'Roxane'
The following example returns the total number of orders:
SELECT COUNT(o) FROM Order o
The following example returns the total number of items that have prices in Hal Incandenza’s order:
SELECT COUNT(l.price) FROM Order o JOIN o.lineItems l JOIN o.customer c WHERE c.lastname = 'Incandenza' AND c.firstname = 'Hal'
The DISTINCT Keyword
The DISTINCT keyword eliminates duplicate return values. If a query returns a java.util.Collection, which allows duplicates, you must specify the DISTINCT keyword to eliminate duplicates.
Constructor Expressions
Constructor expressions allow you to return Java instances that store a query result element instead of an Object[].
The following query creates a CustomerDetail instance per Customer matching the WHERE clause. A CustomerDetail stores the customer name and customer’s country name. So the query returns a List of CustomerDetail instances:
SELECT NEW com.xyz.CustomerDetail(c.name, c.country.name) FROM customer c WHERE c.lastname = 'Coss' AND c.firstname = 'Roxane'
ORDER BY Clause
As its name suggests, the ORDER BY clause orders the values or objects returned by the query.
If the ORDER BY clause contains multiple elements, the left-to-right sequence of the elements determines the high-to-low precedence.
The ASC keyword specifies ascending order, the default, and the DESC keyword indicates descending order.
When using the ORDER BY clause, the SELECT clause must return an orderable set of objects or values. You cannot order the values or objects for values or objects not returned by the SELECT clause. For example, the following query is valid because the ORDER BY clause uses the objects returned by the SELECT clause:
SELECT o FROM Customer c JOIN c.orders o JOIN c.address a WHERE a.state = 'CA' ORDER BY o.quantity, o.totalcost
The following example is not valid, because the ORDER BY clause uses a value not returned by the SELECT clause:
SELECT p.product_name FROM Order o, IN(o.lineItems) l JOIN o.customer c WHERE c.lastname = 'Faehmel' AND c.firstname = 'Robert' ORDER BY o.quantity
GROUP BY and HAVING Clauses
The GROUP BY clause allows you to group values according to a set of properties.
The following query groups the customers by their country and returns the number of customers per country:
SELECT c.country, COUNT(c) FROM Customer c GROUP BY c.country
The HAVING clause is used with the GROUP BY clause to further restrict the returned result of a query.
The following query groups orders by the status of their customer and returns the customer status plus the average totalPrice for all orders where the corresponding customers has the same status. In addition, it considers only customers with status 1, 2, or 3, so orders of other customers are not taken into account:
SELECT c.status, AVG(o.totalPrice) FROM Order o JOIN o.customer c GROUP BY c.status HAVING c.status IN (1, 2, 3)


ZendMarkup is currently shipped with two parsers, a BBCode parser and a Textile parser.
The parsers of ZendMarkup are classes that convert text with markup to a token tree. Although we are using the BBCode parser as example here, the idea of the token tree remains the same across all parsers. We will start with this piece of BBCode for example:
1 | [b]foo[i]bar[/i][/b]baz
|
Then the BBCode parser will take that value, tear it apart and create the following tree:
You will notice that the closing tags are gone, they don’t show up as content in the tree structure. This is because the closing tag isn’t part of the actual content. Although, this does not mean that the closing tag is just lost, it is stored inside the tag information for the tag itself. Also, please note that this is just a simplified view of the tree itself. The actual tree contains a lot more information, like the tag’s attributes and its name.
The BBCode parser is a ZendMarkup parser that converts BBCode to a token tree. The syntax of all BBCode tags is:
1 | [name(=(value|"value"))( attribute=(value|"value"))*]
|
Some examples of valid BBCode tags are:
1 2 3 4 | [b]
[list=1]
[code file=Zend/Markup.php]
[url="http://framework.zend.com/" title="Zend Framework!"]
|
By default, all tags are closed by using the format ‘[/tagname]’.
The Textile parser is a ZendMarkup parser that converts Textile to a token tree. Because Textile doesn’t have a tag structure, the following is a list of example tags:
| Sample input | Sample output |
|---|---|
| *foo* | foo |
| _foo_ | foo |
| ??foo?? | foo |
| -foo- | foo |
| +foo+ | foo |
| ^foo^ | foo |
| ~foo~ | foo |
| %foo% | foo |
| PHP(PHP Hypertext Preprocessor) | <acronym title=”PHP Hypertext Preprocessor”>PHP</acronym> |
| “Zend Framework”:http://framework.zend.com/ | Zend Framework |
| h1. foobar |
foobar
 |
| h6. foobar |
foobar
 |
| !http://framework.zend.com/images/logo.gif! |  |
Also, the Textile parser wraps all tags into paragraphs; a paragraph ends with two newlines, and if there are more tags, a new paragraph will be added.
The Textile parser also supports two types of lists. The numeric type, using the “#” character and bullet-lists using the “*” character. An example of both lists:
1 2 3 4 5 | # Item 1
# Item 2
* Item 1
* Item 2
|
The above will generate two lists: the first, numbered; and the second, bulleted. Inside list items, you can use normal tags like strong (*), and emphasized (_). Tags that need to start on a new line (like ‘h1’ etc.) cannot be used inside lists.
The source code of this file is hosted on GitHub. Everyone can update and fix errors in this document with few clicks - no downloads needed.


This section summarizes natural constants available in EasySpin. These include
EasySpin provides a set of fundamental physical constants that are commonly used in EPR and other areas of spectroscopy and physical chemistry.
Their values are drawn from the CODATA recommended list of fundamental physical constants, which is regularly updated. Every release of EasySpin contains the most up-to-date values available from CODATA at the time of the release.
For each constant, also its uncertainty is available. For example, the value of the Bohr magneton (in J T-1) and its uncertainty can be obtained using bmagn
muB = bmagn; % just the value [muB,sigma] = bmagn; % the value and the uncertainty
The following constants provided by EasySpin are directly relevant to EPR:
| gfree | g value of the free electron |
|---|---|
| bmagn | Bohr magneton, in J T-1 |
| nmagn | nuclear magneton, in J T-1 |
| planck | Planck constant, in J s |
| hbar | reduced Planck constant, in J s |
| mu0 | magnetic constant (vacuum permeability), in N A-2 |
Here is a complete list of available physical constants in EasySpin:
| amu | Atomic mass unit, in kg |
|---|---|
| angstrom | Angstrom, molecular-scale length unit, in m |
| avogadro | Avogadro number, in mol-1 |
| barn | Barn, unit of nuclear electric quadrupole moments, in m2 |
| bmagn | Bohr magneton, in J T-1 |
bohrrad |
Bohr radius, in m |
| boltzm | Boltzmann constant, in J K-1 |
| clight | speed of light in vaccum, in m s-1 |
| echarge | elementary electric charge, in C |
| emass | mass of the electron, in kg |
| eps0 | electric constant (vacuum permittivity), in C V-1 m-1 |
| evolt | electron volt, in J |
| faraday | Faraday constant, in C mol-1 |
| gfree | g factor of the free electron |
| hartree | hartree (atomic unit of energy), in J |
| hbar | reduced Planck constant, in J s |
| molgas | Molar gas constant, in J K-1 mol-1 |
| mu0 | magnetic constant (vacuum permeability), in N A-2 |
| nmagn | Nuclear magneton, in J T-1 |
| nmass | Mass of the neutron, in kg |
| planck | Planck constant, in J s |
| pmass | Mass of the proton, in kg |
| rydberg | Rydberg constant, in m-1 |
Data about nuclear isotopes are also available via a set of EasySpin functions and via a graphical interface. If you call isotopes, a user interface will pop up. Alternatively, you can use one of the following functions to access isotope data:
| nucabund | Natural abundance of nuclear isotopes |
|---|---|
| nucgval | Nuclear g values |
| nucabund | Natural abundance of nuclear isotopes |
| nucqmom | Nuclear quadrupole moments, in barn |
| nucspin | Nuclear spin quantum numbers |
Data for all isotopes is included, not just for the magnetic ones. All data are taken directly from the latest primary reference sources (N.J. Stone, Table of Nuclear Magnetic Dipole and Electric Quadrupole Moments; N. E. Holden, Table of the Isotopes, in CRC Handbook of Physics and Chemistry).


@*
* Copyright 2019 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import config.Fields._
@import config.AppConfig
@import controllers.routes._
@import uk.gov.hmrc.play.views.html._
@(form: Form[_])(implicit req: Request[_], messages: Messages, cfg: AppConfig)
@main_template(messages("locationOfGoods.title")) {
@helpers.form(action = LocationOfGoodsController.onSubmit()) {
@components.back_link()
@components.error_summary(form.errors)


Hi,
I'm Darshan
I'm pursuing my diploma
of Computer Engineering in Singapore Polytechnic
Purpose of my website
Student from Singapore Polytechnic
Hi, my name is S. Darshan Nair. I am a student who is currently pursuing a diploma of Computer Engineering in Singapore Polytechnic. My purpose of this website is to upload my reports on the projects I have done in Singapore Polytechnic. In addition, I would also state my experiences, mistakes I have done and skills that I have obtained from doing various projects!
I can be contacted at
s.darshannair@gmail.com


About
-
01 For Teachers
At the end of a lecture, we'll give you a report on the overall attentiveness and engagement of your students. We also provide real-time alerts when the majority of the class is confused! It's perfect to help read the room while being in virtual settings!
-
02 For Students
Don't worry, our goal was not to let teachers spy on you. We're students too, and we know how easy it is to pick up your phone during a lecture and scroll through Tik Tok. We'll alert you to help you get back on track and ultimately help you focus during school so you can have more time in your day.
-
03 For Parents
You no longer have to be watching over your child's shoulder as they attend class online! You can sign up to receive to daily or weekly updates about your child's engagement in classes.
Team
Chris Vanderloo
Software EngineerChris is a senior at Rensselaer Polytechnic Institute studying Computer Science and Cognitive Science
Sabhya Raju
Software EngineerSabhya is a junior at the University of Pennsylvania studying Computer Science and Economics
Alex Rambasek
Software EngineerAlex is a junior at Case Western Reserve University studying Computer Science and Mathematics
Amanda Tran
Data ScientistAmanda is a senior at Cal Poly SLO studying Statistics and Data Science