Radiative Cooling, Photonics, Design and Fabrication of Micro and Nano-scale Devices, Meta-materials, Polymer materials, Transparent conducting oxides (TCOs), Solar Cells
[C.1] Z.F. Mira, D.H. Kim, G.J. Lee, S.Y. Heo, and Y.M. Song. Multilayer Selective Passive Daytime Radiative Cooler Optimization Utilizing Memetic Algorithm. Journal of Quantitative Spectroscopy & Radiative Transfer, Elsevier [Under review]
[C.1] Z.F. Mira, S. Kabir, M. Khan,and M.J. Hossain. Performance Analysis of GeO2 Doped Silica Hexagonal Photonic Crystal Fiber (H-PCF). 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, 2018. doi:10.1109/IC4ME2.2018.8465619
Softwares: Rsoft, Comsol Multiphysics, MATLAB, Orcad, Pspice, Dsch2, Microwind, Proteus
Languages: C, Assembly, Python
Graduate Level (all): Optoelectronics, Understanding of LEDs, LED fabrication process and performance evaluation, Optics and Lasers, Theory of Semiconductor Devices, Semiconductor Device Processing, Antenna Engineering
Undergraduate Level (selected): Optical Fiber Communication, Electrical Properties of Materials, Solid State Devices, Engineering Electromagnetic, Digital Signal Processing I, Microwave Engineering, Random Signals and Processes
| - GIST Scholarship (Tuition, Stipends, Health insurance, Flight reimbursement) | 2019-2021 |
|---|---|
| - MIST Commandant’s List (Session 2013-2014) | 2015 |
| - Higher Secondary Certificate Scholarship (Dinajpur Education Board) | 2014-2018 |
| - Junior Scholarship (Dinajpur Education Board) | 2009-2010 |
| - Certificate for Completing Short Course: "Microcontroller & Robotics" | 2015 |
Zafrin Ferdous Mira —<EMAIL_ADDRESS>—+82-10-2673-7093


To add multiple Risk Management models, in the Initialize
method, call the AddRiskManagement
method multiple times.
AddRiskManagement(new MaximumDrawdownPercentPerSecurity()); AddRiskManagement(new MaximumSectorExposureRiskManagementModel());
self.AddRiskManagement(MaximumDrawdownPercentPerSecurity()) self.AddRiskManagement(MaximumSectorExposureRiskManagementModel())
If you add multiple Risk Management models, the original collection of PortfolioTarget
objects from the Portfolio Construction model is passed to the first Risk Management model. The risk-adjusted targets from the first Risk Management model are passed to the second Risk Management model. The process continues sequentially until all of the Risk Management models have had an opportunity to adjust the targets.


Welcome to the home of the asset servers for GoAnimate.
It’s most likely you’re not supposed to be here.
You probably got here through the magic known as the Network tab, by poking around Wrapper: Offline’s files, or simply from the launcher.
If you meant to come here, feel free to look around and do stuff. Maybe you’ll find a secret…
Click here to back to the video list


@other_main("Student Connect | Strathmore University"){
Jomo Kenyatta University of Agriculture and Technology is situated in Juja, 36 kilometres North East of Nairobi, along Nairobi-Thika Highway.
It was started in 1981 as a Middle Level College (Jomo Kenyatta College of Agriculture and Technology (JKCAT)) by the Government of Kenya with the generous assistance from the Japanese Government. Plans for the establishment of JKCAT started in 1977. In early 1978, the founding father of the nation, Mzee Jomo Kenyatta donated two hundred hectares of farmland for the establishment of the college.
JKUAT is famous for its top notch Engineering programmes
JKUAT Warriors are the current 3 time University champions


public class ExecuteJava extends java.lang.Object implements java.lang.Runnable, TimeoutObserver
| Constructor and Description |
|---|
ExecuteJava() |
| Modifier and Type | Method and Description |
|---|---|
void |
execute(Project project)
Execute the Java class against the specified Ant Project.
|
int |
fork(ProjectComponent pc)
Run the Java command in a separate VM, this does not give you
the full flexibility of the Java task, but may be enough for
simple needs.
|
boolean |
killedProcess()
Get whether the process was killed.
|
void |
run()
Run this ExecuteJava in a Thread.
|
void |
setClasspath(Path p)
Set the classpath to be used when running the Java class.
|
void |
setJavaCommand(Commandline javaCommand)
Set the Java "command" for this ExecuteJava.
|
void |
setOutput(java.io.PrintStream out)
Deprecated.
since 1.4.x.
manage output at the task level.
|
void |
setPermissions(Permissions permissions)
Set the permissions for the application run.
|
void |
setSystemProperties(CommandlineJava.SysProperties s)
Set the system properties to use when running the Java class.
|
void |
setTimeout(java.lang.Long timeout)
Set the timeout for this ExecuteJava.
|
static void |
setupCommandLineForVMS(Execute exe,
java.lang.String[] command)
On VMS platform, we need to create a special java options file
containing the arguments and classpath for the java command.
|
void |
timeoutOccured(Watchdog w)
Mark timeout as having occurred.
|
public void setJavaCommand(Commandline javaCommand)
javaCommand
- the classname and arguments in a Commandline.public void setClasspath(Path p)
p
- an Ant Path object containing the classpath.public void setSystemProperties(CommandlineJava.SysProperties s)
s
- CommandlineJava system properties.public void setPermissions(Permissions permissions)
permissions
- the Permissions to use.public void setOutput(java.io.PrintStream out)
out
- the PrintStream where output should be sent.public void setTimeout(java.lang.Long timeout)
timeout
- timeout as Long.public void execute(Project project) throws BuildException
project
- the Project to use.BuildException
- on error.public void run()
run
in interface java.lang.Runnable
public void timeoutOccured(Watchdog w)
timeoutOccured
in interface TimeoutObserver
w
- the responsible Watchdog.public boolean killedProcess()
true
if the process was killed, false otherwise.public int fork(ProjectComponent pc) throws BuildException
pc
- the ProjectComponent to use for logging, etc.BuildException
- on error.public static void setupCommandLineForVMS(Execute exe, java.lang.String[] command)
exe
- the Execute instance to alter.command
- the command-line.


A key tension when choosing an infrastructure concerns how to manage product performance with issues of extensibility (ie, confidence in future usability of a language or tool) and ongoing maintenance. Part of this tension is that infrastructure management is a dynamic effort. For example, distributed (and/or NoSQL) systems, but they only become pertinent as a company’s user base moves into the millions (or tens of millions). This is the point at which the organization might run into database issues around contention with the write master, which basically means you’re sending too much write traffic to one server.
Part of the tension is rooted in experience: large companies, some of whom may have little experience with managing a digital-scale infrastructure, are being asked to adopt these evolving technologies that require a very specific skillset. On top of this, a common adage is that the best operators won’t use a component until they know how it breaks. So, how might one generate this experience?
To start, I’ve pulled together a few primers on more commonly used NoSQL databases to give a sense of:
- When they are used (use cases), and
- Common considerations / limitations / challenges
We’ve covered MongoDB in a prior post—for this one, let’s look at HBase.
##HBase Background
HBase is an open source, non-relational, distributed database modeled after Google’s BigTable and is written in Java. It is developed as part of Apache Software Foundation’s Apache Hadoop project and runs on top of HDFS (Hadoop Distributed File System), providing BigTable-like capabilities for Hadoop.
HBase’s use has grown since being tapped to support Facebook’s Messenger application in 2010. Some characteristics of HBase:
- Has a simpler consistency model than Cassandra.
- Very good scalability and performance for their data patterns.
- Most feature rich for their requirements: auto load balancing and failover, compression support, multiple shards per server, etc.
- HDFS, the filesystem used by HBase, supports replication, end-to-end checksums, and automatic rebalancing.
HBase depends on all nodes in the cluster having closely synchronized clocks and referring to each other consistently. Using NTP and DNS ensures that you won’t run into odd behaviors when one node A thinks that the time is tomorrow and node B thinks it’s yesterday.
##When should you look at HBase?
- Your application has a variable schema where each row is slightly different
- You can’t add columns fast enough and most of them are NULL in each row
- You find that your data is stored in collections, for example some metadata, message data or binary data that is all keyed on the same value
- You need key based access to data when storing or retrieving
Common Considerations with HBase:
- HBase doesn’t talk SQL, have an optimizer, or support cross record transactions or joins.
- Be careful when running mixed workloads on an HBase cluster. When you have SLAs on HBase access independent of any MapReduce jobs (for example, a transformation in Pig and serving data from HBase) run them on separate clusters.
- HBase is CPU and Memory intensive with sporadic large sequential I/O access while MapReduce jobs are primarily I/O bound with fixed memory and sporadic CPU.* Combined these can lead to unpredictable latencies for HBase and CPU contention between the two. A shared cluster also requires fewer task slots per node to accommodate for HBase CPU requirements (generally half the slots on each node that you would allocate without HBase).
- Keep an eye on memory swap. If HBase starts to swap there is a good chance it will miss a heartbeat and get dropped from the cluster. On a busy cluster this may overload another region, causing it to swap and a cascade of failures.
- Compactions that disrupt operations. HBase like most other NoSQL databases, stores disk writes in a sequential fashion in a write-ahead log. However, these logs at some point have to be merged and reconciled leading to heavy disk I/O. These I/O storms called compactions, significantly slow down the application and lead to spikes in response time, affecting system reliability and performance.
- Very slow crash recovery. When a node fails in an HBase cluster, it could easily take between 30 minutes and 4 hours to recover it. This is because HBase relies on a RegionServer architecture that has several moving parts that need to be reestablished and realigned for the database to come back to normal operations.
- Unreliable splitting. When one region has too much data, it needs to be split into two, leading to system unreliability. Users have tried to avoid the issue by pre-splitting the tables that requires a lot of manual work and guesstimating the size of the regions.
Hopefully this record helps clarify some situations in which HBase might be useful, and some associated maintenance considerations. Many thanks to these articles as the primary references when putting this together:
- HBase Operations and Best Practices (LinkedIn Blog)
- Apache HBase Do’s and Don’ts (Cloudera)
- Facebook’s New Real-Time Messaging System: HBase To Store 135+ Billion Messages A Month (High Scalability)
- Avoiding HBase Reliability Problems (Toad World)


Kang Daniel transforms into a spy film star for 'Touchin'' MV
by danisurstKang Daniel has made his first comeback since solo debut!
The former Wanna One center released his new single "Touchin'" on November 25 at 6 PM KST. The single has a pop sound inspired by the '80s and '90s fused with more modern EDM, rock, pop, and dance elements. The song is paired with a cinematic music video where Kang Daniel is imagined as something of a chic James Bond-esque film star.
Due to his legal dispute with his former agency, he was unable to promote his previous release on television. However, for this comeback, he is scheduled to appear on KBS's 'Music Bank' and SBS's 'Inkigayo.' He is also currently considering performing at one of the major network's year-end concerts.
Check out the music video for "Touchin'" above!


Structures
The following structures are available globally.
-
Undocumented
See moreDeclaration
Swift
public struct BlockID : CustomStringConvertible, Hashable
-
The location of an entity
See moreDeclaration
Swift
public struct EntityLocation : Hashable, DeserializableDataType
-
The velocity of an entity
See moreDeclaration
Swift
public struct EntityVelocity : Hashable, DeserializableDataType
-
Types of dimensions in minecraft.
See moreDeclaration
Swift
public struct Dimension : Hashable
-
Undocumented
See moreDeclaration
Swift
public struct UserLoginCredentialsAgent : Encodable
-
Credentials for the mojang authentication server.
See moreAttention
When logging in with these credentials multiple times (like 3) in a short period of time from the same ip event if the username and password are correct the ip will be blocked.Declaration
Swift
public struct UserLoginPasswordCredentials : UserLoginCredentials
-
Undocumented
See moreDeclaration
Swift
public struct UserLoginPasswordCredentialsPayload : Encodable
-
Undocumented
See moreDeclaration
Swift
public struct UserLoginSessionCredentialsPayload : Encodable
-
Credentials used to refresh a login.
See moreDeclaration
Swift
public struct SimpleUserLoginSessionCredentials : UserLoginSessionCredentials
-
Default session server service for the mojang servers.
See moreDeclaration
Swift
public struct SessionServerService : SessionServerServiceProtocol
-
SessionServerService for offline connection.
See moreDeclaration
Swift
public struct OfflineSessionService : SessionServerServiceProtocol
-
The response of a successfull user login.
See moreDeclaration
Swift
public struct UserLoginResponse : UserLoginSessionCredentials, Codable, Equatable
-
The login server for the default mojang servers.
See moreDeclaration
Swift
public struct UserLoginService : UserLoginServiceProtocol
-
A minecraft block position struct.
See moreDeclaration
Swift
public struct BlockPosition : Serializable, Hashable
-
Undocumented
See moreDeclaration
Swift
public struct ChatMessage : DeserializableDataType
-
An identifier of the form namespace:identifier
See moreDeclaration
Swift
public struct Identifier : Serializable, Hashable
-
NBT data tree.
See moreDeclaration
Swift
public struct NBT : Hashable, DeserializableDataType
-
A wrapper for a node with a name.
See moreDeclaration
Swift
public struct NamedNBTNodeWrapper
-
An integer which can be serialized with a variable length.
See moreDeclaration
Swift
public struct VarInt<IntegerType> : Serializable where IntegerType : VarIntIntegerType
-
A packet to send to the server after connecting to start the communitation.
See moreDeclaration
Swift
public struct HandshakePacket : BufferSerializablePacket, HandshakePacketIDProvider
-
Received when the server closes the connection while logging in.
See moreNote
The reason will be described in themessage
field.Declaration
Swift
public struct DisconnectPacket : DeserializablePacket, LoginPacketIDProvider
-
Requests the start of encryption.
See moreDeclaration
Swift
public struct EncryptionRequestPacket : DeserializablePacket, LoginPacketIDProvider
-
Received from the server when the login was successful.
See moreDeclaration
Swift
public struct LoginSuccessPacket : DeserializablePacket, LoginPacketIDProvider
-
Packet to enable or disable compression
See moreDeclaration
Swift
public struct SetCompressionPacket : DeserializablePacket, LoginPacketIDProvider
-
Response to the
See moreEncryptionRequestPacket
. This packet is essential for the login.Declaration
Swift
public struct EncryptionResponsePacket : BufferSerializablePacket, LoginPacketIDProvider
-
Requests the initiation of the login request.
See moreDeclaration
Swift
public struct LoginStartPacket : BufferSerializablePacket, LoginPacketIDProvider
-
The default packet library which includes all packets currently implemented
See moreDeclaration
Swift
public struct DefaultPacketLibrary : PacketLibrary
-
Updates the advancments of the player.
See moreDeclaration
Swift
public struct AdvancementsPacket : DeserializablePacket, PlayPacketIDProvider
-
Attaches an entity to another one.
See moreDeclaration
Swift
public struct AttachEntityPacket : DeserializablePacket, PlayPacketIDProvider
-
Perfroms an action of a block.
See moreDeclaration
Swift
public struct BlockActionPacket : DeserializablePacket, PlayPacketIDProvider
-
Tells the client to show a block break animation.
See moreDeclaration
Swift
public struct BlockBreakAnimationPacket : DeserializablePacket, PlayPacketIDProvider
-
Informs the client about a change of a block.
See moreDeclaration
Swift
public struct BlockChangePacket : DeserializablePacket, PlayPacketIDProvider
-
Changes the game state.
See moreDeclaration
Swift
public struct ChangeGameStatePacket : DeserializablePacket, PlayPacketIDProvider
-
Packet containing the chunk data.
See moreDeclaration
Swift
public struct ChunkDataPacket : DeserializablePacket, PlayPacketIDProvider, CustomStringConvertible
-
Sent by the server when someone picks up an item lying on the ground.
See moreAttention
The entity is destroyed in a seperated packet.Declaration
Swift
public struct CollectItemPacket : DeserializablePacket, PlayPacketIDProvider
-
Removes all entities with the given id from the world.
See moreDeclaration
Swift
public struct DestroyEntitiesPacket : DeserializablePacket, PlayPacketIDProvider
-
Plays an effect.
See moreDeclaration
Swift
public struct EffectPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the equipment of an entity.
See moreDeclaration
Swift
public struct EntityEquipmentPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the head look direction of an entity.
See moreDeclaration
Swift
public struct EntityHeadLookPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the location and look direction of an entity.
See moreDeclaration
Swift
public struct EntityLookAndRelativeMovePacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the look direction of an entity.
See moreDeclaration
Swift
public struct EntityLookPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the metadata of an entity.
See moreDeclaration
Swift
public struct EntityMetadataPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the properties of an entity
See moreDeclaration
Swift
public struct EntityPropertiesPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the location of an entity.
See moreDeclaration
Swift
public struct EntityRelativeMovePacket : DeserializablePacket, PlayPacketIDProvider
-
Packet send once per second with the current time.
See moreDeclaration
Swift
public struct EntityStatusPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the location and look direction of an entity.
See moreDeclaration
Swift
public struct EntityTeleportPacket : DeserializablePacket, PlayPacketIDProvider
-
Updaes the veloxity of an entity.
See moreDeclaration
Swift
public struct EntityVelocityPacket : DeserializablePacket, PlayPacketIDProvider
-
Packet informing the client that its selected slot has changed.
See moreDeclaration
Swift
public struct HeldItemChangedReceivedPacket : DeserializablePacket, PlayPacketIDProvider
-
A packet received from the server after a successfull switch of the connection state from login to play.
See moreDeclaration
Swift
public struct JoinGamePacket : DeserializablePacket, PlayPacketIDProvider
-
A packet send in regular time intervalls to request a confirmation that the connection is still alive.
See moreAttention
Every client must handle this packet or it will be kickedDeclaration
Swift
public struct KeepaliveRequestPacket : DeserializablePacket, PlayPacketIDProvider
-
A packet received if multiple blocks changed in the same chunk in the same tick.
See moreDeclaration
Swift
public struct MultiBlockChangePacket : DeserializablePacket, PlayPacketIDProvider
-
Spawns a new particle
See moreDeclaration
Swift
public struct ParticlePacket : DeserializablePacket, PlayPacketIDProvider
-
Updates for the player list.
See moreDeclaration
Swift
public struct PlayerListPacket : DeserializablePacket, PlayPacketIDProvider
-
Temporary type for decoding action data.
See moreDeclaration
Swift
private struct PlayerUpdate<Payload> : DeserializableDataType where Payload : DeserializableDataType
-
The server send the current player location and look direction.
See moreDeclaration
Swift
public struct PlayerPositionAndLookReceivedPacket : DeserializablePacket, PlayPacketIDProvider
-
Packet send to transmitt plugin message.
See moreDeclaration
Swift
public struct PluginMessageReceivedPacket : DeserializablePacket, PlayPacketIDProvider
-
Packet containing a new chat message received from the server.
See moreDeclaration
Swift
public struct ReceiveChatMessagePacket : DeserializablePacket, PlayPacketIDProvider
-
Tells the client to play an animation.
See moreDeclaration
Swift
public struct ReceivedAnimationPacket : DeserializablePacket, PlayPacketIDProvider
-
Informs the player about its abilities.
See moreDeclaration
Swift
public struct ReceivedPlayerAbilitiesPacket : DeserializablePacket, PlayPacketIDProvider
-
A packet indicating that the server difficulty changed.
See moreDeclaration
Swift
public struct ServerDifficultyPacket : DeserializablePacket, PlayPacketIDProvider
-
Sets the experience of the player.
See moreDeclaration
Swift
public struct SetExperiencePacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the content of a slot
See moreDeclaration
Swift
public struct SetSlotPacket : DeserializablePacket, PlayPacketIDProvider
-
Tells the client to play a sound.
See moreDeclaration
Swift
public struct SoundEffectPacket : DeserializablePacket, PlayPacketIDProvider
-
Send by the server when a experience orb spawns
See moreDeclaration
Swift
public struct SpawnExperienceOrbPacket : DeserializablePacket, PlayPacketIDProvider
-
Tells the client to spawn a global entity. Currently the only global entity is the thunderbolt strikes.
See moreDeclaration
Swift
public struct SpawnGlobalEntityPacket : DeserializablePacket, PlayPacketIDProvider
-
Spawns a new mob in the world
See moreDeclaration
Swift
public struct SpawnMobPacket : DeserializablePacket, PlayPacketIDProvider
-
Spawns a new object in the world.
See moreDeclaration
Swift
public struct SpawnObjectPacket : DeserializablePacket, PlayPacketIDProvider
-
Tells the client to spawn a painting.
See moreDeclaration
Swift
public struct SpawnPaintingPacket : DeserializablePacket, PlayPacketIDProvider
-
Tells the client to spawn a player.
See moreDeclaration
Swift
public struct SpawnPlayerPacket : DeserializablePacket, PlayPacketIDProvider
-
Sends the spawn location to the client.
See moreDeclaration
Swift
public struct SpawnPositionPacket : DeserializablePacket, PlayPacketIDProvider
-
Send as response to to a statistics request.
See moreDeclaration
Swift
public struct StatisticsPacket : DeserializablePacket, PlayPacketIDProvider
-
A statistics object.
See moreDeclaration
Swift
public struct Statistic : DeserializableDataType
-
Packet send once per second with the current time.
See moreDeclaration
Swift
public struct TimeUpdatePacket : DeserializablePacket, PlayPacketIDProvider
-
Tells the client to unload a chunk
See moreDeclaration
Swift
public struct UnloadChunkPacket : DeserializablePacket, PlayPacketIDProvider
-
Packet send once per second with the current time.
See moreDeclaration
Swift
public struct UnlockRecipesPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates data of a block entity
See moreDeclaration
Swift
public struct UpdateBlockEntityPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the players health status
See moreDeclaration
Swift
public struct UpdateHealthPacket : DeserializablePacket, PlayPacketIDProvider
-
Sent by the server when items in multiple slots (in a window) are added/removed.
See moreDeclaration
Swift
public struct WindowItemsPacket : DeserializablePacket, PlayPacketIDProvider
-
Updates the world border.
See moreDeclaration
Swift
public struct WorldBorderPacket : DeserializablePacket, PlayPacketIDProvider
-
Sends a status update to the server.
See moreDeclaration
Swift
public struct ClientStatusPacket : BufferSerializablePacket, PlayPacketIDProvider
-
The packet to send after receiveing a keepalive packet from the server.
See moreDeclaration
Swift
public struct KeepaliveResponsePacket : BufferSerializablePacket, PlayPacketIDProvider
-
Updates the players look direction
See moreDeclaration
Swift
public struct PlayerLookPacket : BufferSerializablePacket, PlayPacketIDProvider
-
A packet which will send a chat message to the server.
See moreDeclaration
Swift
public struct SendChatMessagePacket : BufferSerializablePacket, PlayPacketIDProvider
-
Answer to a PlayerPositionAndLookPacket
See moreDeclaration
Swift
public struct TeleportConfirmPacket : BufferSerializablePacket, PlayPacketIDProvider
-
Spawns a new object in the world.
See moreDeclaration
Swift
public struct StatusResponsePacket : DeserializablePacket, StatusPacketIDProvider
-
The packet to send after receiveing a keepalive packet from the server.
See moreDeclaration
Swift
public struct StatusRequestPacket : BufferSerializablePacket, StatusPacketIDProvider


The current website is known as of {{DATE}} to handle your password in plain text. Upon account registration, your entered password may be emailed back to you in plain text. This is a BAD THING.


Virtual Diary Mental Health Analysis
EmoTool
The COVID-19 pandemic has had a number of consequences--one of which is the population's mental health. Many are experiencing poor mental health for the first time while others have lost access to some of the resources they used to manage their mental health.  Without analyzing the body language of their patients, therapists will not be able to accurately glean their patients' health. Our remote mental health monitoring tool aims to solve this issue.


{% extends 'accounts/layout.html' %} {% block content %}
We've emailed you instructions for resetting your password, if an account exists with the email you entered. You should receive them shortly. If you don't receive an email, please make sure you've entered the address you registered with, and check your spam folder.


WebView Custom View
NEW:
Hey!
IMPROVE:
Hello!
FIX:
Hi!
FIX:
Hey again!
FIX:
What?
FIX:
This is an example.
MISC:
How are you?
Material guidelines for dialogs:
http://www.google.com/design/spec/components/dialogs.html
.


#ftl output_format="HTML"> <#macro dashboardLayout title="Dashboard Layout">
Paste the following at the top of your ftl.html:
This is a modal


The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested. Sections 1.10.32 and 1.10.33 from "de Finibus Bonorum et Malorum" by Cicero are also reproduced in their exact original form, accompanied by English versions from the 1914 translation by H. Rackham.
Read Morereproduced below for those interested. Sections 1.10.32 and 1.10.33 from "de Finibus Bonorum et Malorum" by Cicero form, accompanied
Read Morereproduced below for those interested. Sections 1.10.32 and 1.10.33 from "de Finibus Bonorum et Malorum" by Cicero form, accompanied
Read Morereproduced below for those interested. Sections 1.10.32 and 1.10.33 from "de Finibus Bonorum et Malorum" by Cicero form, accompanied
Read More


Greenlight Discharge Dashboard
Partnered with senior quality leadership of Presbyterian Hospital and floor-based medicine team who had created checklist initiative.
The initial request of a 'texting notification' solution was redirected towards a simple digital version of the working checklist, in order allow asynchronous communication of tasks required, between physician and discharge staff. Physician engagement incentives could be explored, by offering an alternative modality apart from direct phone calls. This solution was created as a prototype, as part of a staged integration into an existing, in-house workflow tool.
Met with attending, nurse manager, and quality leader to understand request, needs, and previous work.
Observed work flow with social work, case managers, charge nurse, RNs, pharmacy, and physical therapy at discharge rounds.

Work flow relied on paper and manual delegation of tasks.
After initial improvement in number of discharges by noon after implementation of the
paper checklist, rates had not continued to decline.

Staged approach was proposed as a way to test assumptions, from fake-back-end (bottom) to prototype to potential integration into existing interface (top).
Version 1.0, deployed for use at Presbyterian Hospital. Launched in mid-July across 7 teams.
Dashboard enabled staff (SW, CRC, Pharmacy, PT) to asynchronously view checklist and delegate tasks.
Visit live site - (Must be on UPHS wifi. In production, please view only!)

Observed interactions with dashboard, and implemented changes based on ongoing feedback from residents and discharge team.


U.S. to end sanctions waiver related to Iran's Fordow nuclear site -Pompeo
by ReutersWASHINGTON, Nov 18 (Reuters) - Secretary of State Mike Pompeo said on Monday that the United States is terminating the sanctions wavier related to Iran's Fordow nuclear plant, adding that it is closely monitoring ongoing protests in Iran and is deeply concerned by reports of several fatalities.
"The right amount of uranium enrichment for the world's largest state sponsor of terror is zero...There is no legitimate reason for Iran to resume enrichment at this previously clandestine site," Pompeo said at a press briefing about the Fordow site, where the U.N. atomic watchdog says the country has been enriching uranium.
He added the waiver will be ended Dec. 15.
(Reporting by Doina Chiacu and Matt Spetalnick; Writing by Lisa Lambert)


3 references to Serializer
System.Management.Automation (3)
engine\remoting\common\fragmentor.cs (1)
1081
Serializer serializer = new
Serializer
(xmlWriter, _serializationContext);
engine\serialization.cs (2)
192
:
this
(writer, new SerializationContext())
206
:
this
(writer, new SerializationContext(depth, useDepthFromTypes))


About Us (company history and mission)
Our company started in 2018 when salmon shortage has just begun. Price of salmon started to go up and we wanted to provide a cheap way for people to still experience the taste of salmon without paying a fortune!


Installing Php Image Host
PHP Image Host REQUIRES php 5 or greater and the gd image library
Follow the steps below to install your copy of Php Image Host:
- Create a mysql database on your site to use with Php Image Host. If you do not know how to do this ask your web hosting company.
- Unzip the phpimagehost.zip archive to an empty folder on your computer.
- Edit the "/includes/db.conf.php.sample" file with your own database settings and save it as "/includes/db.conf.php".
- Upload the contents of the phpimagehost archive to your web server.
-
Set the permissions on the "/images" "/thumbs"
and "/temp" directories so that php scripts can write to them.
If your server is apache and has mod_rewrite enabled you should also create a file called .htaccess in the root directory of your installation and set the permissions on that file so the script can write to it too.
This is required to support bandwidth monitoring and search engine friendly urls. file so that the script can create the rules in this file for monitoring image bandwidth and search engine friendly urls.
If you do not know how to do this, please ask your web hosting company, or ask for help in the support forum. - In your webbrowser, navigate to the url where you have uploaded the files.
-
If you entered the correct database settings in the db.conf.php file above
you should see the installation screen. If you get error messages about the
database then check your database settings in the /includes/db.conf.php file.
If you get other errors then please ask for help in the support forum. - Now enter the information required by the form. Then submit the form to install.
- If the install process has worked, you should be able to login to your admin area using the username admin and the password password. Please change the admin username and password immediately on the admin settings page.
- Congratulations! Your new image hosting site is setup and ready to go :)


---
# Feel free to add content and custom Front Matter to this file.
# To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults
layout: default
title: Parsons Template name
---
Coding Unit01 - Parsons Programming Puzzle 01
Arrange the code to create a Python turtle screen. Hint: remember to indent if required.
Coding Unit01 - Parsons Programming Puzzle 02
Arrange the code to create a Python turtle and move him forward 50 pixels.
Coding Unit01 - Parsons Programming Puzzle 03
Arrange the code to make a Python turtle leave a red line as he goes North for 100 pixels.


For this course, I have used one of my very simple tree from a few different organisms within the Archaea superkingdom. More specifically, it is the concatenated ribosomal protein tree within archaea, with representatives from the different Phyla: Asgard archaea (Thorarchaeota, Odinarchaeota, Lokiarchaeota and Heimdallarchaeota), TACK, Euryarchaeota and DPANN. The maximum likelihood phylogeny was reconstructed in IQ-TREE with the LG+F+I+G4 model. The alignment was trimmed to 6,732 positions with the BMGE (Block Mapping and Gathering with Entropy) tool.
ggtree
is an R package that extends ggplot2 for visualizating and annotating phylogenetic trees with their covariates and other associated data. ggtree
in combination with treeio
supports several file formats, including:
read.tree
for reading Newick files.read.phylip
for reading Phylip files.read.jplace
for reading Jplace files.read.nhx
for reading NHX files.read.beast
for parsing output of BEASTread.codeml
for parsing output of CODEML (rst and mlc files)read.codeml_mlc
for parsing mlc file (output of CODEML)read.hyphy
for parsing output of HYPHYread.jplace
for parsing jplace file including output from EPA and pplacerread.nhx
for parsing NHX file including output from PHYLODOG and RevBayesread.paml_rst
for parsing rst file (output of BASEML and CODEML)read.r8s
for parsing output of r8sread.raxml
for parsing output of RAxMLoutput
##
## Phylogenetic tree with 25 tips and 24 internal nodes.
##
## Tip labels:
## DPANN_Pace_RBG_13_36_9, DPANN_Woese_UBA94, EURY_Thermoplasma_volcanium_GSS1, Eury_Halobaculum_gomorrense, Eury_Methanothermococcus_okinawensis, TACK_Bathy_SMTZ80, ...
## Node labels:
## 100, 10.9, 100, 100, 72, 35.9, ...
##
## Rooted; includes branch lengths.
Just like with ggplot2
we created a basic canvas with ggplot(...)
and added layers with +geom_???()
, we can do the same here. The ggtree
package gives us a geom_tree()
function. Because ggtree
is built on top of ggplot2
, you get ggplot2
’s default gray theme with white lines. You can override this with a theme from the ggtree
package.
Because you’ll almost always want to add a tree geom and remove the default background and axes, the ggtree()
function is essentially a shortcut for ggplot(...) + geom_tree() + theme_tree()
.
One can also customize how the tree looks like, just as in ggplot
You can also use different orientations to show the tree.
Just like in ggplot()
you can add layers to the tree as geoms
.
Before we can go further into annotating the tree, we need to understand how ggtree is handling the tree structure internally. Some of the functions in ggtree for annotating clades need a parameter specifying the internal node number. To get the internal node number, user can use geom_text
to display it, where the label is an aesthetic mapping to the “node variable” stored inside the tree object (think of this like the continent variable inside the gapminder object).
Another way to get the internal node number is using the function MRCA()
which stands for Most Recent Common Ancestor. From the tree above, if you would like to get the number of the particular node that is common to all the “Lokiarchaea”. You can do so like below:
Now, let us see how we can use this information to make it scientifically intuitive.
ALternatively, we can also highlight an entire clade:
You can make connections to represent processes like gene transfer events on the tree.
p +
geom_hilight(node=30, fill = "orange") +
geom_cladelabel(node=30, label="Euryarchaeota", color="orange") +
geom_hilight(node=46, fill = "green") +
geom_cladelabel(node=46, label="Lokiarchaeota", color="green") +
geom_taxalink("Eury_Methanothermococcus_okinawensis", "Loki_GC14", color = "red")
plot
You can group the different tips using the groupOTU
function or you can group the tips based on a node using groupClade
function. These groups you can then further use it for other geoms and so on.
Let us say, we want to show the Asgrad archaea separately from the other archaea in this tree. One way to this would be:
arch_tree <- groupOTU(arch_tree, c("DPANN_Pace_RBG_13_36_9", "DPANN_Woese_UBA94",
"EURY_Thermoplasma_volcanium_GSS1", "Eury_Halobaculum_gomorrense", "Eury_Methanothermococcus_okinawensis",
"TACK_Bathy_SMTZ80", "TACK_Nitrososphaera_viennensis", "TACK_Sulfolobus_islandicus_LS215"))
ggtree(arch_tree, aes(color=group)) + geom_tiplab()
plot
We can also get more detailed:
cls <- list(Heimdallarchaeota = c("Heim_AB125", "Heim_LC2", "Heim_LC3"),
Thorarchaeota = c("Baja_Thor", "Thor_AB25", "Thor_MP11T", "Thor_SMTZ45", "Thor_SMTZ_145", "Thor_MP8T", "Thor_MP9T", "Thor_SMTZ_183"),
Lokiarchaeota = c("Loki_CR4", "Loki_GC14", "Baja_Loki2", "Baja_Loki1", "Baja_Loki3"),
Odinarchaeota = "Odin_LCB4")
arch_tree <- groupOTU(arch_tree, cls)
ggtree(arch_tree) + geom_tiplab(aes(color=group)) +
scale_color_manual(values=c("black", rainbow(4)), labels = c("OutGroup", names(cls)))
plot
One thing to notice hear that you can group tips from different clades together as well.
Now, if we are only interested in showing the differences based on the clades, we can use groupClade
.
If you notice the tree object, it actually contains node.label
that are bootstrap values.
output
##
## Phylogenetic tree with 25 tips and 24 internal nodes.
##
## Tip labels:
## DPANN_Pace_RBG_13_36_9, DPANN_Woese_UBA94, EURY_Thermoplasma_volcanium_GSS1, Eury_Halobaculum_gomorrense, Eury_Methanothermococcus_okinawensis, TACK_Bathy_SMTZ80, ...
## Node labels:
## 100, 10.9, 100, 100, 72, 35.9, ...
##
## Rooted; includes branch lengths.
There are different ways we could show this values on the tree. For example,
Now, let us say that you only want to show the values that are above 80, then you would have to do some trick here, like:
ggtree(arch_tree) +
geom_nodepoint(aes(subset = as.numeric(label) > 80), size = 3) +
geom_tiplab() +
theme_tree()
plot
Note Notice that the node.label
are in string, so must remember to use as.numeric()
option be able to filter them to show them.
Note If the tree does not have bootstrap values with in the treefile, one can add them separately as a data.frame
using %<+%
function, as shown in the Adding data to tree section below.
It is possible to specific tips
from the tree using drop.tip()
fuction. Let us say, we want to remove Thor_SMTZ45
and Baja_Loki1
, becuase of the reason that their genomes are not close to complete. We can do that by:
to_drop <- c("Thor_SMTZ45", "Baja_Loki1")
arch_tree_reduced <- drop.tip(arch_tree, to_drop)
arch_tree_reduced <- groupClade(arch_tree_reduced, c(37, 43, 33, 25))
ggtree(arch_tree_reduced) + geom_tiplab(aes(color=group)) +
scale_color_manual(values=c(rainbow(4), "black"),
labels = c("Odin", "Thor", "Loki", "Heimdall", "OutGroup"))
plot
One can also subset a tree, by using the internal node number as shown in the exercises earlier. We can do this by using the tree_subset()
function:
Note The branch lengths and all the other information from the tree are maintained in the new object, when remove tips or subset a tree.
In this part, let us try to see what kind of ways one could add metadata to a tree.
env_info <- read.table("data/tree_env.tsv", sep="\t",
stringsAsFactor=F, row.names = 1, header = T)
p <- ggtree(arch_tree) +
geom_tiplab(aes(color=group)) +
scale_color_manual(values=c(rainbow(4), "black"),
labels = c("Odin", "Thor", "Loki", "Heimdall", "OutGroup")) +
theme(legend.position = "none")
gheatmap(p, env_info, offset = 4, width=1.5,
colnames_angle = 90, colnames_offset_y = 5,
legend_title = "Environment sampled")
plot
Similarly we can plot a heatmap:
env_hmap <- read.table("data/tree_hmap.tsv", sep="\t",
stringsAsFactor=F, row.names = 1, header = T)
p <- ggtree(arch_tree) +
geom_tiplab(aes(color=group)) +
scale_color_manual(values=c(rainbow(4), "black"),
labels = c("Odin", "Thor", "Loki", "Heimdall", "OutGroup")) +
theme(legend.position = "none")
gheatmap(p, env_hmap, offset = 4, width=1.5,
colnames_angle = 90, colnames_offset_y = 5,
legend_title = "Environment sampled")
plot
One can use phylopic database, which is part of the ggtree
package to use many silhouette images of organisms. Here, we can see how one could add some of those images to a tree.
if (!requireNamespace("ggimage", quietly = TRUE))
install.packages("ggimage", dependencies = TRUE )
library(ggimage)
p <- ggtree(arch_tree) +
geom_tiplab(aes(color=group)) +
scale_color_manual(values=c(rainbow(4), "black"),
labels = c("Odin", "Thor", "Loki", "Heimdall", "OutGroup")) +
theme(legend.position = "none")
p + geom_phylopic(size = Inf, image = "e4dde003-8a3b-4c9e-b4ed-1f6e4d894163",
color="steelblue", alpha = .3)
plot
One can add specific images to the specific nodes as well. For this, we would include the phylopic
information as a dataframe and which specific node number, should these images be matched. Then we could add this information to the ggtree
object using %<+%
function.
phylopic_info <- data.frame(node = c(39, 46, 35, 27, 20),
phylopic = c("6b79f4f2-3c34-4130-9e60-4ba2fe68ff48",
"f04e96a5-8c19-4caf-af64-ee51e69431fd",
"e4dde003-8a3b-4c9e-b4ed-1f6e4d894163",
"3cabf30c-eed6-4dc4-824e-f7eac992f554",
"5254d3c6-6865-46c4-beaa-4a9306a50bc8"))
p <- ggtree(arch_tree) +
geom_tiplab(aes(color=group)) +
scale_color_manual(values=c(rainbow(4), "black"),
labels = c("Odin", "Thor", "Loki", "Heimdall", "OutGroup")) +
theme(legend.position = "none")
p %<+% phylopic_info +
geom_nodelab(aes(image=phylopic),
geom="phylopic", alpha=.8, color='steelblue') +
geom_tiplab(aes(image=phylopic),
geom="phylopic", alpha=.8, color='steelblue')
plot
Note Notice that for Odin
, we had to use geom_tiplab()
as it is a tip and not a node!
There are more fun images in phylopic
that one can use:
phylopic_info <- data.frame(node = c(39, 46, 35, 27, 38),
phylopic = c("bac25f49-97a4-4aec-beb6-f542158ebd23",
"f598fb39-facf-43ea-a576-1861304b2fe4",
"aceb287d-84cf-46f1-868c-4797c4ac54a8",
"0174801d-15a6-4668-bfe0-4c421fbe51e8",
"72f2f854-f3cd-4666-887c-35d5c256ab0f"))
p <- ggtree(arch_tree) +
geom_tiplab(aes(color=group)) +
scale_color_manual(values=c(rainbow(4), "black"),
labels = c("Odin", "Thor", "Loki", "Heimdall", "OutGroup")) +
theme(legend.position = "none")
p %<+% phylopic_info +
geom_nodelab(aes(image=phylopic), geom="phylopic",
alpha=.8, color='steelblue')
plot
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggimage_0.2.8 treeio_1.10.0 ggtree_2.0.4 forcats_0.5.0
## [5] stringr_1.4.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.2
## [9] tibble_3.0.4 tidyverse_1.3.0 reshape2_1.4.4 ggplot2_3.3.2
## [13] dplyr_1.0.2 captioner_2.2.3 bookdown_0.20 knitr_1.29
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 lubridate_1.7.9 ape_5.4-1
## [4] lattice_0.20-40 assertthat_0.2.1 digest_0.6.27
## [7] R6_2.5.0 cellranger_1.1.0 plyr_1.8.6
## [10] backports_1.1.10 reprex_0.3.0 evaluate_0.14
## [13] httr_1.4.2 pillar_1.4.6 rlang_0.4.8
## [16] curl_4.3 lazyeval_0.2.2 readxl_1.3.1
## [19] rstudioapi_0.11 blob_1.2.1 magick_2.5.0
## [22] rmarkdown_2.3 labeling_0.4.2 munsell_0.5.0
## [25] broom_0.7.0 compiler_3.6.3 modelr_0.1.8
## [28] xfun_0.18 gridGraphics_0.5-0 pkgconfig_2.0.3
## [31] htmltools_0.5.0 tidyselect_1.1.0 fansi_0.4.1
## [34] crayon_1.3.4 dbplyr_1.4.4 withr_2.3.0
## [37] grid_3.6.3 nlme_3.1-144 jsonlite_1.7.1
## [40] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0
## [43] magrittr_1.5 scales_1.1.1 tidytree_0.3.3
## [46] cli_2.1.0 stringi_1.5.3 farver_2.0.3
## [49] fs_1.5.0 xml2_1.3.2 ellipsis_0.3.1
## [52] rvcheck_0.1.8 generics_0.0.2 vctrs_0.3.4
## [55] tools_3.6.3 ggplotify_0.0.5 glue_1.4.2
## [58] hms_0.5.3 parallel_3.6.3 yaml_2.2.1
## [61] colorspace_1.4-1 BiocManager_1.30.10 rvest_0.3.6
## [64] haven_2.3.1
End of document


About Me
After coaching Crossfit for roughly 8 years I wanted to try and change up my overall career focus. Though I love coaching I wanted to challenge myself to learn something that is very foreign and new to me. That is why I am here. When I entered the Georgia Tech BootCamp I had zero working knowledge of anything I was getting into, and though that was terrifying it was also very intriguing. I am giving myself a chance to grow and struggle and I believe I will be much better for it in the end. I believe that my years spent as a coach will help me communicate with others and give and receive criticism in an effective manner. I really enjoy working with others and look forward to get started on some cool and fun projects.
Outside of jumping headfirst into uncharted territory, I find that the things I enjoy most are very simple.  We have two "classy ladies" (senior dogs) who we are very grateful have kept happy and healthy through the years. We will go and hike and be adventurous but we will also spend hours at home cooking and watching netflix.


Day 1(Intra Hall Open House):
• The Hall president and Second Senate Member shall conduct an Open House session with all the hall boarders where various problems faced by them shall be discussed. Also, a representative from a NGO would visit and encourage the students to discuss on a particular social issue centred around the theme of Live Your Life and come up with solutions to tackle it.
• Observed institute wide between 8:30 PM to 10:00 PM.
• The students shall assemble in the common ground where the Hall President shall address them and the Hall may also decide to conduct any event that can elicit participation of the entire hall population like movie screening, games like Tambola, Antakshari etc.
• The minutes of the Open House shall be submitted to the Dean of Student Affairs and the Vice President, Technology Students’ Gymkhana.
Day 2(Night OUT Day):
• Blackout for 2 hours between 8:30 pm and 10:30 pm.
• Power Supply and LAN to be shut down in every hall.
• Aim to bring people out of their rooms and communicate with their peers.
• Light up only few of the common places in the campus where people can gather and communicate.
• Plan surprise events in the Jnan Ghosh stadium in association with Technology Dance Society and Technology Dramatics Society.
Day 3(Yaadein):
• A big formation shall be made by the graduating batch at the Jnan Ghosh stadium in the evening that addresses the issue of Substance Abuse with an appropriate message. This theme will be different for different years based on discussion with Dean Of Student Affairs and Vice President, Technology Students’ Gymkhana.
• Photographs of the Formation by the graduating batch shall be included in Imprint - the annual yearbook and Alankar.
• The Video of the formation shall be released through the youtube channel of Technology Students’ Gymkhana- KGP Talks.
• Director’s Anecdotes: If possible the Director will be interacting informally with the students at the venue. The session may happen from 6PM-7PM in the evening.
Firstly, this caters to the needs of the euphoric gripped students who have no intention of letting their energy levels drop.
Implementation of this proposal would help us make Spring Fest a 24 x 7 delight by keeping it alive throughout those four days.
Screening of Classic International movies is likely to attract associations of some highly prestigious international organizations like cultural centers and libraries of various countries which are keen to share their culture with us.
Alliance Francaise, American Library, Goethe-Institute, Russian Cultural Centre are some of the active organizations who screen their films in India through their embassy.
Having an association with organizations of such caliber would definitely elevate the standards and quality of the fest.
There is no better slot for having Cyber Arena conducted. If the Game is on, then night it is to accomplish the mission.
A lot of students have been observed to Jam and play informal events like Antakshari and Dumb Charades throughout the night. Conducting new and innovative events will add to the splendour of the night life seen in the fest.
A Night Flea Market will be a great opportunity for Spring Fest to associate with organizations who create brilliant native art and help revive the social aspect of Spring Fest by helping people remember the forgotten arts.
• Participating students shall only be Post Graduate and Research Scholar students of the institute.
• The Social and Cultural Weekend shall be a two day affair.
• The Social and Cultural Weekend shall be organized in the Autumn Semester. The exact dates shall be communicated by the Technology Students’ Gymkhana after consultation with the Post Graduate Representative and the Research Scholar Representative to the Institute Senate.
• There shall be five events conducted in these two days:
• Day 1(Saturday) Events:
- Elocution ( Timings: 10 AM - 12 NOON at Netaji Auditorium)
- Eastern/Western Vocals ( Timings: 7 PM - 10 PM at Netaji Auditorium)
• Day 2(Sunday) Events:
- Dramatics (6 PM-10 PM at Netaji Auditorium)
- Fine Arts (Timings: 10 AM -1 PM at S-302 or S-301)
• The performances in the various events shall be covered both through the Facebook page of Technology Students’ Gymkhana and the youtube channel of Technology Students’ Gymkhana - KGP Talks.
• Videos of past Gold winning performances in the Social and Cultural General Championship events will be screened for the benefit of the participants before the commencement of the Social and Cultural weekend.
• This will address the problem of less participation of the Research Scholars and Post graduate students in the General Championship Social and Cultural.
• There are events of each genre of the General Championship Social and Cultural in the Social and Cultural Weekend. This will help them in getting oriented with the General Championship.
• The Social and Cultural Weekend can also be used as a platform for proper information dissemination to the Research Scholars and Post Graduate students
Helps in making the students in the campus more socially aware.
• Introduces students to the basic concepts involved in doing a case study that has an impact on the society.
• By doing this event, more number of students will be able to learn the concepts involved in a case study.
• Will give a boost to the literature enthusiasts of the campus.
• Practically feasible ideas given as a solution can be implemented by the participating NGO thus bringing about an actual impact on the society.
Some of the common techniques used are to create creative and intelligent video advertisements for branding of Spring Fest that would be shared by everyone due to the fun factor in it. Another popular method is to conduct interesting informal events with Mall Partners and posting the videos of these events on our site and Youtube channel. The current publicity via social media has ensured we convey the vital information to all the followers of Spring Fest. But through Guerrilla marketing, every post we share makes a deep impression on the viewer, thereby increasing the publicity as well as increasing the viewer’s base through repeated sharing.
i. Causes exponential growth in viewership. Reaches a large Audience.
ii. Reduction of Workload. We are just making the Advertisements. People are advertising it.
iii. Totally cost free marketing strategy.
iv. Increases the traffic to the Website, Likes on the Facebook Page and number of people talking about this.
v. Leaves a long lasting impression on the viewers.
vi. Video Advertisements would raise the standards of marketing and publicity in Social Media which was till now done mainly through posters.
vii.It would elevate the marketing techniques of Spring Fest above the contemporary methods followed by other fests.


Process Safety Engineering Software Development
RYSK Consulting is an engineering, safety, and software development consulting company based in Tulsa, OK. We have done work all over the world on a wide variety of plants and processes.
What we do
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Ready to start your next project with us? Give us a call or send us an email and we will get back to you as soon as possible.
+1-918-640-1377


--- layout: default title: Codeship For Your Organization bodyclass: is-blue active: getting-started --- {% assign collections = site.collections | sort: 'sort_order' %}
Learn how to use Codeship to help manage your high-performance engineering teams.
We've built Codeship to make it easy to use the widest variety of tools, services and vendors possible.
Learn how to manage teams with different security roles and project access on Codeship, including via the API.
Read our security article or ask a security question to learn about how we've built Codeship with security and trust in mind at all times.
We provide highly-technical support and can help you at any point - from setup to deploy! Just let us know how we can help.


| HBASE-13594 |
MultiRowRangeFilter shouldn't call HBaseZeroCopyByteString.wrap() directly |
Major |
. |
Ted Yu |
Ted Yu |
| HBASE-13704 |
Hbase throws OutOfOrderScannerNextException when MultiRowRangeFilter is used |
Major |
Client |
Aleksandr Maksymenko |
Aleksandr Maksymenko |
| HBASE-14206 |
MultiRowRangeFilter returns records whose rowKeys are out of allowed ranges |
Critical |
Filters |
Anton Nazaruk |
Anton Nazaruk |
| HBASE-15975 |
logic in TestHTableDescriptor#testAddCoprocessorWithSpecStr is wrong |
Trivial |
test |
huaxiang sun |
huaxiang sun |
| HBASE-15990 |
The priority value of subsequent coprocessors in the Coprocessor.Priority.SYSTEM list are not incremented by one |
Minor |
. |
ChiaPing Tsai |
ChiaPing Tsai |
| HBASE-15946 |
Eliminate possible security concerns in RS web UI's store file metrics |
Major |
. |
Sean Mackrory |
Sean Mackrory |
| HBASE-16007 |
Job's Configuration should be passed to TableMapReduceUtil#addDependencyJars() in WALPlayer |
Major |
. |
Ted Yu |
Ted Yu |
| HBASE-15746 |
Remove extra RegionCoprocessor preClose() in RSRpcServices#closeRegion |
Minor |
Coprocessors, regionserver |
Matteo Bertozzi |
Stephen Yuan Jiang |
| HBASE-16033 |
Add more details in logging of responseTooSlow/TooLarge |
Major |
. |
Yu Li |
Yu Li |
| HBASE-16045 |
endtime argument for VerifyReplication was incorrectly specified in usage |
Major |
. |
Ted Yu |
Ted Yu |
| HBASE-16061 |
Allow logging to a buffered console |
Major |
. |
Elliott Clark |
Elliott Clark |
| HBASE-15783 |
AccessControlConstants#OP_ATTRIBUTE_ACL_STRATEGY_CELL_FIRST not used any more. |
Major |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-16032 |
Possible memory leak in StoreScanner |
Major |
. |
Yu Li |
Yu Li |
| HBASE-16012 |
Major compaction can't work due to obsolete scanner read point in RegionServer |
Major |
Compaction, Scanners |
Guanghao Zhang |
Guanghao Zhang |
| HBASE-16070 |
Mapreduce Serialization classes do not have Interface audience |
Major |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-16090 |
ResultScanner is not closed in SyncTable#finishRemainingHashRanges() |
Major |
. |
Ted Yu |
Ted Yu |
| HBASE-15976 |
RegionServerMetricsWrapperRunnable will be failure when disable blockcache. |
Major |
. |
Liu Junhong |
Jingcheng Du |
| HBASE-16129 |
check_compatibility.sh is broken when using Java API Compliance Checker v1.7 |
Major |
test |
Dima Spivak |
Dima Spivak |
| HBASE-16093 |
Splits failed before creating daughter regions leave meta inconsistent |
Critical |
master, Region Assignment |
Elliott Clark |
Elliott Clark |
| HBASE-16135 |
PeerClusterZnode under rs of removed peer may never be deleted |
Major |
Replication |
Duo Zhang |
Duo Zhang |
| HBASE-16091 |
Canary takes lot more time when there are delete markers in the table |
Major |
canary |
Vishal Khandelwal |
Vishal Khandelwal |
| HBASE-16201 |
NPE in RpcServer causing intermittent UT failure of TestMasterReplication#testHFileCyclicReplication |
Major |
. |
Yu Li |
Yu Li |
| HBASE-16160 |
Get the UnsupportedOperationException when using delegation token with encryption |
Blocker |
. |
Colin Ma |
Colin Ma |
| HBASE-16055 |
PutSortReducer loses any Visibility/acl attribute set on the Puts |
Critical |
security |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-16207 |
can't restore snapshot without "Admin" permission |
Major |
master, snapshots |
Matteo Bertozzi |
Matteo Bertozzi |
| HBASE-16227 |
[Shell] Column value formatter not working in scans |
Major |
. |
Appy |
Appy |
| HBASE-16095 |
Add priority to TableDescriptor and priority region open thread pool |
Major |
. |
Enis Soztutar |
Enis Soztutar |
| HBASE-16244 |
LocalHBaseCluster start timeout should be configurable |
Major |
hbase |
Siddharth Wagle |
|
| HBASE-16272 |
Overflow in ServerName's compareTo method |
Major |
hbase |
huaxiang sun |
huaxiang sun |
| HBASE-16281 |
TestMasterReplication is flaky |
Major |
. |
Phil Yang |
Phil Yang |
| HBASE-16300 |
LruBlockCache.CACHE_FIXED_OVERHEAD should calculate LruBlockCache size correctly |
Major |
. |
Yu Sun |
Yu Sun |
| HBASE-16288 |
HFile intermediate block level indexes might recurse forever creating multi TB files |
Critical |
HFile |
Enis Soztutar |
Enis Soztutar |
| HBASE-16296 |
Reverse scan performance degrades when using filter lists |
Major |
Filters |
James Taylor |
Ted Yu |
| HBASE-16284 |
Unauthorized client can shutdown the cluster |
Major |
. |
Deokwoo Han |
Deokwoo Han |
| HBASE-16319 |
Fix TestCacheOnWrite after HBASE-16288 |
Trivial |
. |
Andrew Purtell |
Andrew Purtell |
| HBASE-16350 |
Undo server abort from HBASE-14968 |
Major |
. |
Enis Soztutar |
Enis Soztutar |


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--- layout: page title: "Table of Contents" description: "" ---
Introduction
About
Key Concepts*
What is Happiness?
Causes and Conditions of Happiness
Why True Happiness
Acceptance Does Not Mean Giving Up*
How to Attain Happiness?
Why Mindfulness?
Sitting Meditation
Walking Meditation
Mindfulness in Daily Life
Creating a Habit For Your Practice
Putting our Happiness Outside of Ourselves
Comparing Oneself to Others
Fear of Being Meaningless
Mistake in Life


 You can change these values for individual images later.
For the target, convert the raw instrumental magnitude to this color band.
{% include 'partials/filter_selector.html' with selected_band=lightcurve.magband.band image_filters=magband_filters %}The filter used to make observations.
{% include 'partials/filter_selector.html' with selected_band=lightcurve.filter.band %}Second-order extinction is nearly constant and usually only significant for B-V color index measurements. Set to 0 if you do not want to include a term for second-order extinction.
Select the optical filter associated with each image.
{% include 'partials/image_filter_selector.html' %}Point source extraction is the process that determines the location of stars and other objects in your image. This information is then used to process photometry, which measures the number of photons emitted by objects in your image.
To start tuning photometry, we recommend that you use a single image that is representative of most of the images in your uploaded dataset.
Start with your first image. Once you have tuned the photometry parameters to your satisfaction, apply those settings to all images.
Comparison stars are known stars that appear in all observations. This software will automatically select comparison stars.
{% if lightcurve.is_photometry_complete %}Photometry is still pending for some images. Refresh this page when it is complete to see a full list of comparison stars.
{% endif %} {% if lightcurve.is_photometry_complete %}Magnitudes shown are from the first image. To review comparison stars in more detail, review the 'Reference Stars' page for each image.
{% include 'partials/comparison_stars.html' %} {% else %}Uh oh - there are no common stars. Please tune your photometry to make sure some known stars are picked up, then try again.
{% endif %} {% endif %}To compute color index and the hidden transform to standard magnitudes, pair images of the same field in different bands. Color Index CI = Image1 - Image2.
The reduction process followed here is adapted from Brian Warner's Practical Guide to Lightcurve Photometry and Analysis. The goal of the photometry reduction process is to convert our raw instrumental observations to a standard magnitude system, correcting for atmospheric distortion and other factors along the way.
The equation for reduction of instrumental magnitudes to standard magnitudes is:
Mt = (mt - mc) - k"f Xt (CIt - CIc) + Tf (CIt - CIc) + Mc
If CI is skipped, the equation becomes:
Mt = (mt - mc) + Mc
Now we will compute the transformation coefficient Tf and the hidden transforms for your image.
 This option is the quickest way to build a lightcurve, but it could include bad datapoints or images. The alternative is to go through each image and click 'Add to Lightcurve' after reviewing it. Regardless of your choice, you are encouraged to review all image analyses for correctness.
If you don't want to run all reductions now, skip below to run reductions image-by-image.
Images that are marked as "Review pending" are ready to be reduced.
Once you've run some reductions, use the button below to add all completed image reductions to the light curve plot. Or, you can go image-by-image and add them individually.
After adding images to your lightcurve, you can view the plot of standard magnitudes over time.
Now that you've created an initial lightcurve, you can review and verify each image in detail.
☑ Initial processing and astrometry (0 of {{ images | length }})
☐ Apply global settings
☐ Select filters
☐ Initial photometry processed (? of {{ images | length }})
☐ Tune photometry
☐ Select comparison stars (? selected)
☐ Select targets (? of {{ images | length }})
☐ Select image pairs (? selected)
☐ Review transform calculations
☐ Run image reductions (? of ?)
☐ Review analyses (? of {{ images | length}})


Ruff ride: Florida dog bumps car into motion, gets solo trip
PORT ST. LUCIE, Fla. — Florida authorities say they responded to a rogue vehicle spinning in reverse around a suburban cul-de-sac with a lone occupant inside: a Black Labrador.
by Canadian PressPORT ST. LUCIE, Fla. — Florida authorities say they responded to a rogue vehicle spinning in reverse around a suburban cul-de-sac with a lone occupant inside: a Black Labrador.
News outlets report residents called police Thursday after seeing the dog trapped and clambering around in the car as it spun in circles in a Port St. Lucie neighbourhood.
Police say they think the dog’s owner stepped away from the running car and that’s when the pet knocked it into reverse. Neighbor Anne Sabol says she watched the furry speed racer take out a mailbox and a trash can.
Sabol says the dog was in high spirits after being rescued, adding it “jumped out of the car, wagging his tail.”
Police stopped the joyride by punching a passcode into the driver’s door. No one was hurt.
The Associated Press


Pilot the AR.Drone 2.0 directly from your browser. Extend the application with plugins to add features such as video recording, autonomous flight, face recognition, and more. It makes it a very friendly environment to quickly build and experiment with your drone (e.g. during a nodecopter event).
Built-in plugins
video-png stream the video to the browser through static image loading, works great in every browser.
video-stream uses node-dronestream to stream the raw h264 video feed via webscokets and rendering in Javascript. Need a modern browser and CPU.
hud to visualize a head-up display with artificial horizon, compass, altimeter, etc. Based on nodecopter-cockpit
pilot to control the drone remotely using the keyboard. Based on drone-browser.
blackbox records all mission data (raw video, navdata, etc.) on the disk.
replay replays a mission by injecting the data at the client level. Makes it a very friendly tool to code/test/debug when you can't fly.
Additional plugins
If you have written your own webflight plugins, please add them to this list by editing this page.
copterface detect faces and track them by rotating the drone. A port of the copterface project to the webflight environment.
traffic Air traffic overlay plugin for ardrone-webflight that uses ADS-B data.
gamepad controls the drone with a gamepad for much smoother flights.
Install
WebFlight requires a recent nodejs (built and tested with node > 0.10) as well as npm and bower for dependency management.
git clone git@github.com:eschnou/ardrone-webflight
cd ardrone-webflight
npm install
bower install
Usage
- Copy the config.js.sample to config.js and edit to select your plugins
- Connect to the drone's wifi
- Run
node app.js
- Point your browser to http://localhost:3000/
Remote control
If you have enabled the pilot plugin, you can fly the drone with the following keys. Yes, these are azerty bindings :-) If you need qwerty ones, just hack the plugin. I'll need to find a way to provide custome configuration etc.
Use Z, S, Q, D
to move front, back and sideways. Use your cursors
to go up/down or turn
clockwise/counter clockwise. Use t
to takeoff and l
for landing.
Adding your own plugin
There is no tutorial yet, in the meanwhile, just have a look at the built in plugins, it is faily straightforward.
Support
If you encounter issues, please add them to the issue tracker. You can find me on twitter (@eschnou) or on the #nodecopter IRC channel on #freenode.
Thanks
This work is based on the integration of nodecopter-cockpit and drone-browser, refactored in a plugin architecture. Thanks to @bkw and @functino for sharing!
Also a big thank you to @felixge for his node-ar-drone library which pushed me into buying a drone and become crazy about these little flying robots!
Donate
If you like this project, please consider donating. The less time I need to work, the more I can spend on open source projects :-)
Please Donate To Bitcoin Address: [[address]]
License
The MIT License
Copyright (c) 2013 by the AUTHORS
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Laravel provides a unified API for various caching systems. The cache configuration is located at config/cache.php
. In this file you may specify which cache driver you would like used by default throughout your application. Laravel supports popular caching backends like Memcached and Redis out of the box.
The cache configuration file also contains various other options, which are documented within the file, so make sure to read over these options. By default, Laravel is configured to use the file
cache driver, which stores the serialized, cached objects in the filesystem. For larger applications, it is recommended that you use an in-memory cache such as Memcached or APC. You may even configure multiple cache configurations for the same driver.
Before using a Redis cache with Laravel, you will need to install the predis/predis
package (~1.0) via Composer.
Cache::put('key', 'value', $minutes);
$expiresAt = Carbon::now()->addMinutes(10);
Cache::put('key', 'value', $expiresAt);
Cache::add('key', 'value', $minutes);
The add
method will return true
if the item is actually added to the cache. Otherwise, the method will return false
.
if (Cache::has('key'))
{
//
}
$value = Cache::get('key');
$value = Cache::get('key', 'default');
$value = Cache::get('key', function() { return 'default'; });
Cache::forever('key', 'value');
Sometimes you may wish to retrieve an item from the cache, but also store a default value if the requested item doesn't exist. You may do this using the Cache::remember
method:
$value = Cache::remember('users', $minutes, function()
{
return DB::table('users')->get();
});
You may also combine the remember
and forever
methods:
$value = Cache::rememberForever('users', function()
{
return DB::table('users')->get();
});
Note that all items stored in the cache are serialized, so you are free to store any type of data.
If you need to retrieve an item from the cache and then delete it, you may use the pull
method:
$value = Cache::pull('key');
Cache::forget('key');
When using multiple cache stores, you may access them via the store
method:
$value = Cache::store('foo')->get('key');
All drivers except database
support the increment
and decrement
operations:
Cache::increment('key');
Cache::increment('key', $amount);
Cache::decrement('key');
Cache::decrement('key', $amount);
Note: Cache tags are not supported when using the
file
ordatabase
cache drivers. Furthermore, when using multiple tags with caches that are stored "forever", performance will be best with a driver such asmemcached
, which automatically purges stale records.
Cache tags allow you to tag related items in the cache, and then flush all caches tagged with a given name. To access a tagged cache, use the tags
method.
You may store a tagged cache by passing in an ordered list of tag names as arguments, or as an ordered array of tag names:
Cache::tags('people', 'authors')->put('John', $john, $minutes);
Cache::tags(['people', 'artists'])->put('Anne', $anne, $minutes);
You may use any cache storage method in combination with tags, including remember
, forever
, and rememberForever
. You may also access cached items from the tagged cache, as well as use the other cache methods such as increment
and decrement
.
To access a tagged cache, pass the same ordered list of tags used to save it.
$anne = Cache::tags('people', 'artists')->get('Anne');
$john = Cache::tags(['people', 'authors'])->get('John');
You may flush all items tagged with a name or list of names. For example, this statement would remove all caches tagged with either people
, authors
, or both. So, both "Anne" and "John" would be removed from the cache:
Cache::tags('people', 'authors')->flush();
In contrast, this statement would remove only caches tagged with authors
, so "John" would be removed, but not "Anne".
Cache::tags('authors')->flush();
To execute code on every cache operation, you may listen for the events fired by the cache:
Event::listen('cache.hit', function($key, $value) {
//
});
Event::listen('cache.missed', function($key) {
//
});
Event::listen('cache.write', function($key, $value, $minutes) {
//
});
Event::listen('cache.delete', function($key) {
//
});
When using the database
cache driver, you will need to setup a table to contain the cache items. You'll find an example Schema
declaration for the table below:
Schema::create('cache', function($table)
{
$table->string('key')->unique();
$table->text('value');
$table->integer('expiration');
});
Using the Memcached cache requires the Memcached PECL package to be installed.
The default configuration uses TCP/IP based on Memcached::addServer:
'memcached' => array(
array('host' => '<IP_ADDRESS>', 'port' => 11211, 'weight' => 100),
),
You may also set the host
option to a UNIX socket path. If you do this, the port
option should be set to 0
:
'memcached' => array(
array('host' => '/var/run/memcached/memcached.sock', 'port' => 0, 'weight' => 100),
),


Functions for obtaining N-sp predictions based on protein sequence.
get_signalp5()
Query SignalP 5.0 web server.
get_signalp()
Query SignalP web server.
get_targetp()
Query TargetP web server.
get_phobius()
Query Phobius web server.
Functions for obtaining GPI predictions based on protein sequence.
get_big_pi()
Query big-PI Plant Predictor web server.
get_pred_gpi()
Query PredGPI web server.
get_netGPI()
Query NetGPI - 1.1 web server.
Functions for domain, transmembrane region and disorder prediction based on protein sequence.
get_hmm()
Query hmmer web server.
get_cdd()
Query the Conserved Domain Database.
get_espritz()
Query Espritz web server.
pfam2go()
Add GO terms based on pfam accessions
get_tmhmm()
Query TMHMM-2.0 web server.
predict_hyp()
Predict hydroxyproline positions in plant proteins based on primary structure
scan_ag()
Find AG glycomodules in protein sequences
maab()
MAAB classification of hydroxyproline rich glycoproteins
plot_prot()
Protein structure diagram.
scan_nglc()
Detection of motifs for N-glycosylation on asparagine residues.
Data frame with 2700 Arabidopsis thaliana protein sequences predicted to contain a N-sp by either Phobius or SignalP with length of 100 - 400 amino acids.
at_nsp
2700 Arabidopsis thaliana protein sequences.
CTDC()
Composition descriptor (CTDC)
getAAindex()
Extract amino acid attributes
getKmer()
Extract simetric k-mers around prolines
extractMBdesc()
Normalized Moreau-Broto Autocorrelation Descriptor
QSOlevel()
Quasi-Sequence-Order (QSO) Descriptor
sub_hyp()
Substitute prolines to hydroxyprolines
split_fasta()
Split a fasta formatted file.


Processing
Drawing
Here are my video and interactive sketches experimentations programmed with the Processing framework. You can see (and interact with) them by clicking the images below. Some of the source code can be found here. Note: Currently there is a problem, it will be fixed soon.
Here are some samples from recent work I made for the Drawing class I took in Spring '18 and some pieces from my high school portfolio


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|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object edu.cmu.sphinx.util.props.ConfigurableAdapter edu.cmu.sphinx.frontend.BaseDataProcessor edu.cmu.sphinx.frontend.window.RaisedCosineWindower
public class RaisedCosineWindower
Slices up a Data object into a number of overlapping windows (usually referred to as "frames" in the speech world). In order to minimize the signal discontinuities at the boundaries of each frame, we multiply each frame with a raised cosine windowing function. Moreover, the system uses overlapping windows to capture information that may occur at the window boundaries. These events would not be well represented if the windows were juxtaposed.
The number of
resulting windows depends on the window size
and the window
shift
(commonly known as frame shift in speech world). Figure 1 shows the relationship between the original data
stream, the window size, the window shift, and the windows returned.
Figure 1: Relationship between original data, window size, window shift, and the windows returned.
The
raised cosine windowing function will be applied to each such window. Since the getData()
method returns a
window, and multiple windows are created for each Data object, this is a 1-to-many processor. Also note that the
returned windows should have the same number of data points as the windowing function.
The applied windowing function, W(n), of length N (the window size), is given by the following formula:
W(n) = (1-a) - (a * cos((2 * Math.PI * n)/(N - 1)))where a is commonly known as the "alpha" value. This variable can be set by the user using the property defined by
PROP_ALPHA
. Please follow the links to the see the constant field values. Some values of alpha
receive special names, since they are used so often. A value of 0.46 for the alpha results in a window named Hamming
window. A value of 0.5 results in the Hanning window. And a value of 0 results in the Rectangular window. The default
for this system is the Hamming window, with alpha 0.46 !). Figure 2 below shows the Hamming window function (a =
0.46), using our default window size of 25.625 ms and assuming a sample rate of 16kHz, thus yielding 410 samples per
window.
Figure 2: The Hamming window function.
Data
| Field Summary | |
|---|---|
static java.lang.String |
PROP_ALPHA
The property for the alpha value of the Window, which is the value for the RaisedCosineWindow. |
static java.lang.String |
PROP_WINDOW_SHIFT_MS
The property for window shift in milliseconds, which has a default value of 10F. |
static java.lang.String |
PROP_WINDOW_SIZE_MS
The property for window size in milliseconds. |
static java.lang.String |
WINDOW_SHIFT_SAMPLES
|
static java.lang.String |
WINDOW_SIZE_SAMPLES
|
| Constructor Summary | |
|---|---|
RaisedCosineWindower()
|
|
RaisedCosineWindower(double alpha,
float windowSizeInMs,
float windowShiftInMs)
|
| Method Summary | |
|---|---|
Data |
getData()
Returns the next Data object, which is usually a window of the input Data, with the windowing function applied to it. |
int |
getSampleRate()
|
float |
getWindowShiftInMs()
Returns the shift size used to window the incoming speech signal. |
void |
initialize()
Initializes this DataProcessor. |
void |
newProperties(PropertySheet ps)
This method is called when this configurable component needs to be reconfigured. |
long |
roundToFrames(long samples)
Rounds a given sample-number to the number of samples will be processed by this instance including the padding samples at the end.. |
| Methods inherited from class edu.cmu.sphinx.frontend.BaseDataProcessor |
|---|
getPredecessor, getTimer, setPredecessor |
| Methods inherited from class edu.cmu.sphinx.util.props.ConfigurableAdapter |
|---|
getName, toString |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
|---|
@S4Double(defaultValue=25.625) public static final java.lang.String PROP_WINDOW_SIZE_MS
@S4Double(defaultValue=10.0) public static final java.lang.String PROP_WINDOW_SHIFT_MS
@S4Double(defaultValue=0.46) public static final java.lang.String PROP_ALPHA
public static final java.lang.String WINDOW_SHIFT_SAMPLES
public static final java.lang.String WINDOW_SIZE_SAMPLES
| Constructor Detail |
|---|
public RaisedCosineWindower(double alpha, float windowSizeInMs, float windowShiftInMs)
public RaisedCosineWindower()
| Method Detail |
|---|
public void newProperties(PropertySheet ps) throws PropertyException
Configurable
newProperties
in interface Configurable
newProperties
in class ConfigurableAdapter
ps
- a property sheet holding the new data
PropertyException
- if there is a problem with the properties.public void initialize()
BaseDataProcessor
initialize
in interface DataProcessor
initialize
in class BaseDataProcessor
public Data getData() throws DataProcessingException
getData
in interface DataProcessor
getData
in class BaseDataProcessor
DataProcessingException
- if a data processing error occurredData
public float getWindowShiftInMs()
public int getSampleRate()
public long roundToFrames(long samples)
samples
-
|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


{{aData.codeLayoutName}}.xml
{{aData.codeActivityName}}.java
Design Guide documents for developers can be opened with Safari and Chrome.


Help us by filling in some information about yourself! What skills do you have? What projects interest you? We will not print your answers unless you tell us it's ok.
Your email won't be given/shown, but your skills or participation may be shown.
Just a spot for you to give us a short bio, or tell us anything you'd like.


Time Scales in Plasmas
Ride the wave or stick with the buddy system?
2016-02-14
Plasmas being significantly comprised of charged particles have an interesting feature, namely that they are heavily affected by electromagnetic fields. This means that regions of plasma can distribute energy and impact other regions of plasma without using the typical method of collisions. For example when you start using a space heater in a room, part of how the heat gets distributed throughout is by the collision of gas particles. But with plasmas, how important are these field effects and how important are particle-particle collision effects? We're going to talk about density and temperature a bit first, then get into how they impact the rate at which different effects take place.
Getting down to basics.
$&n&$ is the density of particles, however since you typically have at the very least two different kinds of particles, some negative species (often electrons) and some positive species (atomic ions, like hydrogen or helium), in most mathematical treatments you break up the density into how much of each species you have $&n_s&$ so if you have say, an equal distribution of hydrogen and helium and electrons all hot enough to be stripped of all their electrons and uniformly distributed through out, you would have $&n_0=n_H=n_{He}=\frac{1}{3}n_e&$ . Density is measured in quantity per unit volume, such as cubic meters, cubic centimeters, or cubic light years.
$&\tau&$ is the temperature of the system. In many disciplines, temperatures are talked about in units Kelvin, or maybe Celsius, or heaven forbid Rankine. Then when they want to do actual math with them they multiply them by Boltzmann's constant to turn them into some unit of energy, like Joules or Ergs or electron Volts (eV). This is because that's what temperature is, it's an aggregate kinetic energy, a system with twice the temperature will have particles that, on average, have twice the kinetic energy. This also means that $&\tau&$ and $&mv^2&$ can be swapped back and forth with some care and things will stay consistent. Now, physicists, being people, are incredibly lazy, and whenever presented with the choice will write less. So instead of writing $&k_b*T&$ over and over again, we just write $&\tau&$ . This almost never gets confusing when we also use $&\tau&$ as a way to denote time. Another reason for using energies instead of temperatures in plasmas is because they get hot enough that the temperatures tend to be quite large. Since we're dealing with ionized gases, the natural temperature scale is on the order of 1 to 10 eV, because that's how much energy it takes to strip an electron from an atom. 1 eV is over ten thousand Kelvin. While we can still do math on big numbers as thoroughly as we can on small numbers, it is harder to keep in mind relative scale the larger things get. Trying to keep things simple is important, especially when things get intrinsically weirder.
Plasma Frequency $&\omega_p&$
One of the most important time scales in a plasma is the plasma frequency. Most plasmas are, when you zoom out and squint, neutral. It's when you get close up that there are variations in that neutrality. The reason for it being over all neutral is because electric fields act over infinite distances if there's a net charge, and it will add roaming charges distinctiveness as part of the plasma whole, their kinetic energy adapted to serve the collective. But internally there are fluctuations that propagate as waves. Like how a pond over all has a level surface, but when you drop a stone in it, ripples form that make a particular region deeper or shallower. Now the same thing happens in plasmas, only instead of depth, it's charge density. These kinds of changes happen on the scale of the plasma frequency which is $&\omega_p = \sqrt{\frac{n_0e^2}{\epsilon_0 m_e}}=\sqrt{k_p n_0}&$ and is derived from one of the most egregious spherical-cow methods I've had the pleasure of following along so far. It's right up there with assuming stars are cubes to figure out the minimum mass of a black hole, which is a beautiful kind of hilarity in it's own right. The gist of it is we model the plasma as being comprised of two overlapping slabs that interact via electric fields, immobile ions in one slab, light weight electrons in the other. We then pull the electron slab over to the side somewhat so that they no longer fully overlap. Now there's a positive charge density on one side and negative charge density on the other, this causes attraction, restored overlapping and then overshooting, starting the whole process over again. A bit of calculus produces the result that this process has the frequency above, which we elevate to the position of $&\omega_p&$ , it then turns out that this value repeatedly comes up over and over again and it's rather amazing that it can be derived in such a simple way. I've included a little toy below where you can see how frequency changes with density. Density being presented as the opacity of the two slabs. The frequency does change proportionately with density properly, but scaled down considerably slower, typical plasma frequencies are millions or even trillions of cycles per second.
Ion Collision Frequency $&\nu_{ei}&$
So the time scale on which electromagnetic interactions happen is pretty tidy and the derivation was concise. Maybe this whole plasma business isn't so hard after all? next up we have collision frequency $&\nu_c&$ . Well, if we're trying to figure out how often something collides with some set of targets, we think about how big is the target, how many targets are there, and how fast the thing is moving through the targets. Ok, how many is easy, that's just density, particles per volume. How fast the thing is moving through targets is also easy, that's just velocity of our subject, we'll say its an electron for now. so we're at $&\nu_c=n_t*v_e*area&$ , well, how do we calculate area?
For a neutral atom, the electron will bounce off it hits it but not really notice otherwise.
so we end up with something within an order of magnitude of $&\pi a_0^2&$ where $&a_0&$ is the bohr radius, a piddling tiny distance clocking in at about $&5*10^{-11}&$ meters. Which is a really insignificantly tiny number. For context, if all the information in a reasonable quality copy of the film The Shawshank Redemption(700~ megabytes) represented 1 whole meter, the width of an atom is roughly the first bit, the first 1 or 0 of that movie. So, less than a single pixel of a single frame. Unless the electron is almost moving directly at the atom it's going to do nothing at all in response to it's existence. But that's neutral atoms, plasma's are fun because it's the ions and electrons that are doing the cool stuff, how do ions effect the path of an electron?
Oh dang, you get some pretty wicked redirections going inside that circle, but even never going near it you can see pretty noticeable bending. What's going on here? Well, one useful way to approach this problem is through thinking about potential energy. The same way a bicycle will want to roll down a hill, an electron will want to "roll" down to an ion. The given height of the hill can be either positive or negative depending on if it's opposite or similar charges and goes like $&\frac{1}{r}&$ , so as you get really close, the hill gets incredibly steep. But it never goes quite to zero, even far away. So you can kind of imagine the potential for a charged particle being like the horn of a trombone or cone the flairs out towards the edges. Meanwhile for a neutral particle, a reasonable metaphor would just be a cylinder that goes up forever, nothings getting any closer than the surface of the barrel.
So saying we know what the electron's initial velocity is, we know it's kinetic energy $&KE = \frac{m_e}{2}v_e^2&$ . We could say that the electron has collided with the ion if it would have passed inside a distance that the potential energy is greater than the kinetic energy. The full expression for potential energy between two charged particles is, within a plus or minus sign, $&PE= \frac{q^2}{4\pi \epsilon_0 r}&$ . So when $&\frac{PE}{KE}\ge1&$ we've had a collision. So we'll define a term, radius of collision, $&r_c&$ such that $&\frac{q^2}{4\pi \epsilon_0 r_c} \frac{1}{\frac{m_e}{2}v_e^2} = 1 &$ which after a bit of algebra and tidying gives us $&r_c = \frac{q^2}{2\pi \epsilon_0 m_e v_e^2} &$ (This is why the circle would change size when you fiddled with horizontal slider bar, you were changing the velocity of the electron.) . If this looks complicated, that is because it is much more complicated than the simple constant $&a_0&$ we had for neutral collisions, it's an actual function with dependence on something that might change, the electron velocity. But everything else in that expression is basically a constant, so we could capture all of it's behavior by bundling the rest of the constants together $&r_c = \frac{k}{v_e^2} &$ . This'll make other expressions a bit easier to work with and we'll unpack it later. For a bit of context, if the electron has a kinetic energy of 1 eV (moderately warm, but nothing exceptional), $&r_c&$ would be about a hundred times larger than the bohr radius we talked about earlier. So, we have a radius, area is just radius squared times pi, $&A_c = \pi r_c^2 = \frac{\pi k^2}{v_e^4}&$ . Slap that into our collision frequency expression and we get $&\nu_c =n_i v_e \frac{\pi k^2}{v_e^4} = \frac{n_i \pi k^2}{v_e^3}&$.
Considering $&\frac{\nu}{\omega_p}&$
So this provides with two different time scales, one on which electrostatic phenomenon take place, and one on which particle-particle phenomenon take place. The resulting ratio is a more rigorous way to ask the question "Yes, these collisions are happening, but are they happening enough to matter?". Collisions cause entropy to increase and are generally the source of a lot of hairiness in describing a plasma with math. Knowing when they can be ignored safely is a nice thing to be able to do. Comparing collision frequency with the plasma frequency is a rigorous way to do this, as they both represent time scales on which things happen. If the collision frequency is much much smaller than the plasma frequency, their ratio is basically 0 and collisions will play a very small part in how the plasma changes. This relationship can be described, ignoring coefficients as $&\frac{\nu}{\omega_p}\propto \frac{\sqrt{n_0}}{v_e^3}\approx \frac{\sqrt{n_0}}{\tau^{3/2}} &$ . We see that high densities and lower temperatures lead to collisional systems. The problem, though, is that if you have a lot of particles in close proximity that aren't very hot, the inter particle forces will encourage it to be a gas, or even a liquid or a solid. This results in most collisional plasmas being found in the centers of stars or on the wave fronts of explosions.


10 references to ProcessActions
Microsoft.Management.Infrastructure.CimCmdlets (10)
CimAsyncOperation.cs (2)
161
ProcessActions
(cmdletOperation);
182
ProcessActions
(cmdletOperation);
CimSessionOperations.cs (1)
978
this.cimTestSession.
ProcessActions
(cmdletOperation);
GetCimAssociatedInstanceCommand.cs (1)
226
operation.
ProcessActions
(this.CmdletOperation);
GetCimClassCommand.cs (1)
190
cimGetCimClass.
ProcessActions
(this.CmdletOperation);
GetCimInstanceCommand.cs (1)
485
cimGetInstance.
ProcessActions
(this.CmdletOperation);
InvokeCimMethodCommand.cs (1)
402
cimInvokeMethod.
ProcessActions
(this.CmdletOperation);
NewCimInstanceCommand.cs (1)
371
cimNewCimInstance.
ProcessActions
(this.CmdletOperation);
RemoveCimInstanceCommand.cs (1)
270
cimRemoveInstance.
ProcessActions
(this.CmdletOperation);
SetCimInstanceCommand.cs (1)
319
cimSetCimInstance.
ProcessActions
(this.CmdletOperation);


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