Elie to Cambo Sands
The Elie to Cambo Sands (also known as Kingsbarns) section is 16 miles long (25.8 km) and can be rough underfoot. It is full of geological, historical and wildlife interest, but it also includes several short sections of stone steps that may be uneven or slippy. The path will lead you through quaint little fishing villages. WEAR SNEAKERS.
The Elie to St Monans section begins in Ruby Bay, once a site famous for Red Garnet gemstones. Follow the path, taking a recommened and short detour to Elie Lighthouse and Lady Tower built for Lady Janet Anstruther as a summer house in the 1760's. Further on are the ruins of Ardross and Newark Castles. The route from here to St Monans has a high tide diversion which borders fields, crosses a bridge and rejoins the low tide route near the stunning 14th century St Monans Church.
Contuine past the restored St. Monans Windmill, which once used to pump seawater into the adjacent salt pans. The limestone beds here are rich in fossils. The next village of Pittenweem is Fife’s only working fishing harbour, and is the site of a cave used by St Fillan in the 7th century. Skirt the golf course and the rocky shore past Billow Ness to the four old Royal Burghs which constitute Anstruther. This popular tourist village is notworthy for it's award winning fish and chips, Scottish Fisheries Museum and the Reaper (a herring drifter built in 1900 and used in the filming of the hugely popular Outlander series). From Anstruther, there are daily boat trips to the Isle of May nature reserve. SUMMER ONLY.
Pass through Cellardyke, with its picturesque harbour, towards Crail and onto open pasture. Livestock may be present in this area so please proceed with caution. The Caiplie Caves are a prominent weathered sandstone feature situated almost halfway between Cellardyke and Crail. The route continues past an old salt works before winding its way via some stone steps to Crail, a traditional fishing village with a 17th century harbour. The Path leading into Fife Ness is challenging in places with narrower and altogether rougher terrain. It passes through the Scottish Wildlife Trust's Kilminning Coast Wildlife Reserve. Hikers should note that sheep and cattle graze the area at certain times of year.
You are now at the most easterly point in Fife and the path leads hikers past King Constantine’s Cave, where Constantine was killed in 874. Beyond the golf course the route continues along the shore beneath the Randerston cliffs. WAIT for low tide before attempting this section. The path then leads to a bridge over Cambo Burn before contining along a sandy track through the dunes. An alternative route follows Kingsbarns beach.
Allow 5 - 6 hours to complete the whole route.
Archeology & Geology
Top sites in this area include
- St Fillan's Cave
- Caiplie Caves
- Constantines Cave
Architecture
Top sites for this area include:
- Lady's Tower
- Ardross Castle
- Newark Castle
- St Monans Church
- St Monans Windmill
Culture
Top sites for this area include:
- St Monans Church
- Scottish Fisheries Museum
- Crail Museum & Heritage Centre
- Cambo Gardens
History & Heritage
Top sites for this area include
- Lady's Tower
- Ardross Castle
- Newark Castle
- St Monans Church
- St Monans Windmill
Industry
Top sites for this area include:
- St Monans Windmill
- Anstruther Harbour
- Crail Harbour
Beaches
Top sites for this area include:
- Anstruther Billowness Beach
- Anstruther Harbour
- Lifeboat Sands
- Crail Harbour
- Crail Roome Bay
- Cambo Sands


Did PM raise persecution issue with Hasina, asks Congress
Bill is a bid to divert attention from NRC failure: MP
by Special CorrespondentThe Congress on Monday asked if Prime Minister Narendra Modi had ever raised the issue of religious persecution with his Bangladeshi counterpart, Sheikh Hasina, and accused the Modi government of bringing the Citizenship (Amendment) Bill to distract from its failure to prepare an error-free National Register of Citizens (NRC).
Addressing a press conference, Lok Sabha member from Assam, Gaurav Gogoi, said the Bill symbolised the “dictatorial approach” of the Union government as it has led to unrest among various groups in northeast India.
The Bill not only violates the basic principles of the Constitution but will also deepen the faultlines between tribals and non-tribals, Mr. Gogoi said.
“This Bill is a symbol of the dictatorial attitude of the BJP. If they care about the people and the sentiments of the Northeast, then they would not have brought this Bill and bulldozed it in such a fashion,” he said.
“This Bill alleges religious persecution in Bangladesh, yet in the past six years, we have never heard Prime Minister Narendra Modi raise this issue with Prime Minister Sheikh Hasina. Can the BJP government give any evidence as to when Prime Minister Shri Narendra Modi raised the subject of religious persecution with his Bangladeshi counterpart,” he asked.


WikiCommons2AFrame will create a 360 Degree Image on the fly and create a AFrame VR. The WikiCommon image as source image is used (see Create 360 Degree Image for AFrame)


Old Application architecture - kept here for reference
The new emoncms documentation location is here http://emoncms.org/
Emoncms follows the commonly used Model-View-Controller (MVC) architecture, which provides a clear way of separating the functions of the application in to different parts.
At the heart of the application is the database, which stores the application data, such as user information and feeds data. Everything else is there to essentially be the bridge between the user and the database. Providing the user with the data requested in the requested format from a http web accessible API.
The following diagram shows the distinct stages in this process and the flow of data between them using the example of creating a list of a users feeds:
Application flow diagram
Whats going on here?
1) index.php
As in the diagram above everything is directed through index.php, this is achieved using mod_rewrite
The purpose of mod_rewrite is to convert
emoncms/user/login
to:
emoncms/index.php?q=user/login
index.php then fetches the property q with $_GET['q'] and builds the page accordingly.
Anther name for index.php it is the main controller. The controller that decides which sub-controllers are to be called and can put the result of many controllers together. When the themed HTML format is requested it wraps the output of the sub-controllers (including sub-views) in the main application theme.
1) The models
The model translates the mysql database API into a php methods which define the internal API of the application. The internal API can be seen an includable software library that makes the main application scripting a bit neater.
With database requests such as the following:
$feed_result = db_query("SELECT * FROM feeds WHERE id = '$feedid'");
$feed_row = db_fetch_array($feed_result);
if ($feed_row['status'] != 1) { // if feed is not deleted
$feed = array($feed_row['id'],$feed_row['name'],$feed_row['tag'],$feed_row['time'],$feed_row['value']);
}
Being turned into nice one-line commands like this:
$feed = get_feed($feedid);
2) The controllers
The controllers are the bridge between the internal php API and the external HTTP API.
Converting a request such as:
http://yoursite/emoncms3/feed/list.html
into:
$feeds = get_user_feeds($userid);
and returning the output in the required format. The controllers also handle access privileges and input validation, which are discussed here on the
security page.
A typical controller action might look like this: (the following action is within the function feed_controller() )
if ($action == 'list' && $_SESSION['read'])
{
$feeds = get_user_feeds($_SESSION['userid']);
if ($format == 'json') $output = json_encode($feeds);
if ($format == 'html') $output = view("feed/list_view.php", array('feeds' => $feeds));
}
Access privileges
The line $_SESSION['read'] determines the access privileges of the controller action. In this case the user must have at least read access privileges, which can be granted with a read access apikey. $_SESSION['write'] would require the user to have write access privileges. No $_SESSION read or write check would make the action public.
Format control
The output can currently be themed as a nice HTML page for viewing in a browser or as a JSON string for use in 3rd party applications, AJAX requests or datalogging equipment.
The format is set by adding a .html or .json to the end of the HTTP API. If no format is given it will assume that the format desired is a themed html page.
JSON format output: emoncms/feed/list.json
HTML themed output: emoncms/feed/list.html
For most controller actions you will see the following format control lines:
if ($format == 'json') $output = json_encode($feeds);
if ($format == 'html') $output = view("feed/list_view.php", array('feeds' => $feeds));
For the json option the output is just json encoded and then returned.
For the html option the output is passed to a view which handles the wrapping in a themed html output. The output of the view is passed back to the controller for returning to the main controller: index.php
3) Views
The views wrap the variables and objects passed from the controllers in a presentation layer, such as html and css. A simple view outputting a table with a list of feeds might look like this:

<?php foreach ($feeds as $feed) { ?>

<?php echo $feed['name']; ?>
<?php echo $feed['value']; ?>

<?php } ?>


Why sign up
It's not forever
Each challenge will run for just 30 days. Long enough to make a difference. Not so long that you'll want to give up.
You won't be alone
Others will be doing the same - feeling tempted, winning small victories. Succeed together for the greatest results.
Celebrate what you DON'T do
The hardest thing is saying no. We'll acknowledge that you turned down the doughnuts your colleague brought in.
It's fun!
See where you are on the leaderboard! Earn badges and achievements for every little success. You'll feel amazing!


|
Hi {{name}}!
This is {{from.name}} from Trustroots Foundation. You signed up a little while ago, and I just wanted to check back.
{% if topic === 'fill-profile' %}
Your profile seems to be quite empty.
It would be very easy to take a short time to fill it out, and it would make it easier for you to connect with people on Trustroots.
{% else %}
How has your experience with Trustroots been for you so far? I would be happy to hear any feedback. You can simply reply to this email.
{% endif %}
{% include "./partials/socialmedia.server.view.html" %}
Happy traveling!
{{from.name}}
|


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.ApplicationConfig @import models.FormBundleStatus._ @import play.twirl.api.Html @import views.html.helpers._ @import views.html.includes.awrs_main @this(awrsMain: awrs_main) @(resubmissionDate: String, isNewBusiness : Boolean, printFriendly: Boolean = false)(implicit request: Request[AnyContent], messages: Messages, applicationConfig: ApplicationConfig) @awrsRef = @{sessionUtil(request).getSessionAwrsRefNo.fold("")(x => x)} @govWrapper = { @awrsMain(title = messages("awrs.generic.tab.title", messages("awrs.confirmation.title")), userLoggedIn = true){
@Html(messages("awrs.confirmation.detailed_information","",""))
@messages("awrs.index_page.changes.answer.para_3_link")


Tower of Guinness books at Victoria store sets record at six metres high
by Jeff BellThe new location for Russell Books was the scene of a world record Thursday as about a thousand books were used to make a tower six metres high (19 feet, eight inches) in front of a cheering crowd.
All were copies of Guinness World Records books dating back as far as 1962.
“That was a big challenge, a huge challenge,” said Russell Books co-owner Andrea Minter. She said was kicking the idea around for a couple of years before going ahead with the feat.
Guinness World Records adjudicator Philip Robertson was there to present Minter with an official certificate minutes after the five-hour effort was complete.
He called the record-setting result “remarkable.”
“There’s no adhesive, there’s no glue, there’s no paint, there’s no internal structure, there’s no wooden structure.”
Robertson said he was pulling for the stackers.
“I’m not allowed to coach or assist in any way but I was willing you to success,” he said.
The official record is for the tallest tower of Guinness World Records books, something that had never been tried before. The Guinness people decided that the Russell Books tower would have to be at least six metres high to establish a benchmark.
The other book-stacking marks that have been set are the tallest pyramid of books, made up of 144,000 books, and the tallest stack of books at 12 metres.
Robertson explained that a stack is a single column of books while a tower has to have a single base layer that can then be extended into tiers and into a single column. In this case, multiple piles set up on the base provide some stability before they are joined into a carefully shaped top portion.
Minter said the books used in setting the world record came from generous community donations. She said the Times Colonist Book Sale provided a trove of material.
The record-setting event was part of opening activities for the new location of Russell Books at 747 Fort St.
Work on taking the tower down started almost right away because of concerns about its stability. An official count of the books used could follow.


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Half and half barista in et cinnamon macchiato rich flavour. Cultivar robust redeye roast viennese wings extra whipped cultivar organic medium frappuccino. Affogato, and aroma fair trade milk cream beans decaffeinated americano.

Mug darjeeling hot antioxidant scented blend ceylon mint shrub blend orange pekoe. White turmeric zen gyokuro turmeric rooibos teahouse turmeric nilgiri genmai loose leaf white hojicha london fog. Jasmine camellia sinensis gaiwan pu’erh assam irish breakfast sachet tea kettle bai mudan green latte. Strainer gaiwan shrub nilgiri latte theanine ginger camellia sinensis fermentation infuser infuser scented latte shoumei darjeeling. Guayusa yerba maté genmai energy rose hips kombucha turmeric matcha nokcha focus brew orange pekoe turmeric.
| Drinks | Price | ||
|---|---|---|---|
| Latte | 100% Arabica, farm milk or oat/almond milk | $6.00 | |
| Espresso | 100% Arabica, or 100% Robusta, or mixed | $6.00 | |
| Cappuccino | 100% Arabica, farm milk or oat/almond milk | $6.00 | |
| Ice Coffee | Variety: Jasmin, Oolong, Ginger, Earl Gray, Chamomile | $5.00 | |
| Hot Tea | 100% Arabica, farm milk or oat/almond milk, syrup | $5.00 | |
| Ice Tea | Variety: Jasmin, Oolong, Ginger, Earl Gray, Chamomile | $5.00 | |
| Hot Lemon Toddy | Lemon, lemon juice, honey, cinnamon stick | $5.00 | |
| Hot Chocolate | Cocoa powder, farm milk or oat/almond milk | $5.00 |


Week no. 1
Building my personal site
To document my works during Fab Academy 2017, I created this html site with the help of Dreamweaver.
I've never done something like this before, but i had my colleagues help and whatched some tutorials.
I've downloaded a template from http://www.free-css.com and now i'm trying to customize it.
Dreamweaver is very intuitive because you can choose the splited view, here you can see the design and the code
at the same time and you start to understand the code very naturaly.
The main page will have the weekly assignments with a link to a page with each assignment,
where i'm going to upload information and pictures about the assignments every week.
The main page will have, as well, a link to a page called "about me" that will have my personal information
and another link for the final project where I intent to show my project development.
Once I’ve organized my information and determined a structure, I started creating my site:
-
I used the Dreamweaver Files panel to set up my site’s organizational structure: in the Files panel,
you can easily add, delete, and rename files and folders to change the organization as needed. -
You can use the Assets panel to easily organize the assets in the site; you can then drag most assets
directly from the Assets panel into a Dreamweaver document. -
You can easily add a variety of content to web pages. Add assets and design elements, such as text,
images, colors, movies, sound, and other forms of media.
This is what I',ve learned so far, but with the weeks passing by i will have time to explore and learn more about Dreamweaver.
The portuguese students group are uploading weekly his page to a Git repository, for our instructor reviews,
and only after validating the pages are uploaded do Fabacademy repository.
To do that i've followed a tutorial made by our instructor and very clear to understand.
Fisrt i created an account, to create a repository for portuguese fablabs where each student has his own folder to upload the files.
When i complete each week imput or change anything i use the git shell in windows OS,
and the git mantra:
cd ~/Desktop/FabAcademy/FabAcademyPortugal...
git pull
git add .
git status
git commit -m "just a smal comment"
git push;
Planing the semester
In order to plan my semesters work i did a Gantt Chart, I'll try to keep up with my work schedule. =P
My Final Project
To my Fab Academy 2017 final project, I’m thinking about something related to bike ridding that could be useful for me.
Some years ago i rebember to see this stop light for the bike that was activated as you brake and i
thought this was a nice opportunity to try to do it myself because with this project i think that a could integrate
a good part of the fab academy modules.
This type of device is already found on the web, here you can see some examples:
https://www.kickstarter.com/projects/lucidbrake/lucidbrake-motion-sensing-press-on-bicycle-brake-l
https://www.bikerumor.com/2013/05/14/more-wireless-brake-lights-velodrooms-adaptive-bike-light/
I started by drawing by what i thought I want my brake light to look like:


About
Concentration Profile
Side View
Top View
Concentration of Contaminate (μg/m^3)
5+
μg/m
3
10+
μg/m
3
20+
μg/m
3
30+
μg/m
3
50+
μg/m
3
100+
μg/m
3
Concentration range:
-
Concentration range:
-
+
sc: Stability Class
Map Location of Stack


the and for []. with COVID- are SARS-CoV that viral from {background cell infection may patients virus treatment antibodies margin was vaccine {border-style The [,]. neutralizing px;} #ffffff;} can efficacy have receptor vaccines CoV Coronavirus binding disease has this clinical coronavirus human immune other severe were This against host novel such RNA Respiratory cells ACE These inhibitors its Severe activity also antibody antiviral been early effective not plasma potential responses Acute Angiotensin SARS Syndrome [,] already based currently development drug mAbs monoclonal pneumonia protein replication respiratory specific their therapy trials IFN S-protein drugs especially functional fusion hydroxychloroquine into low px; response safety studies thus use {font-family Chinese DNA Patients SARS-CoV- With administration animal control cytokine different entry font-size infection. infections molecules proteins since spike through under used viruses COVID-: Convalescent Microbes Phase RBD Treatment affect affected after assessed associated blood convalescent could days detected double; effects emerging humoral immunity increased infected most recent reduce results target therapeutic trial Engl. Further, IL- Infect../.. Rev. Wuhan, al. and/or border-color border-width causing combination critical cytokines domain highly including infectious outbreak preclinical produced rate recently regard, should solid; strategies study symptoms syndrome system targeting there time undertaken vector {font-style {font-weight (NCT) China Chloroquine Disease Furthermore, HCQ Indeed, June KRA; Lab. Patents Review Type University Vaccines [], accessed acid activities administered all aminotransferase amplification anti-CoV anti-inflammatory antibodies. antigen being bold but courier; demonstrated drugs, due during em; epitopes first generate generated glycoprotein group high however, identification identify inflammatory interferons investigation isothermal leading levels lopinavir, lower management measures mechanisms ongoing particles patient peptide peripheral present preventive production pt;} receptors reduced risk ritonavir, several similar stage started storm than those titers tract using vaccination virus, virus. when which will within


I am currently a freshman at the University of Illinois at Urbana-Champaign, studying Computer Engineering. I am seeking an internship for Summer 2019.
Current Technical Coursework (Spring 2019)
ECE 220 - Computer Systems & Programming
CS 173 - Discrete Structures
ECE 110 - Introduction to Electronics
Planned Coursework:
CS 225 - Data Structures (Fall 2019)
ECE 391 - Computer Systems Engineering (Spring 2020)
Resume


Enterprise
The Enterprise plan is the Mac-Daddy of plans at Linkstr. With it, you get up to 50 users, unlimited domains, unlimited history, custom external 404 pages, custom in-between pages, brandable reports, and an awesome dashboard. You are only one click away from having a state of the art, enterprise grade link shortening service at your organizations fingertips.
{% endblock %}


Temporarily escalate privilege for user:
{{$ctrl.token.user.name}}
- on cluster:
{{$ctrl.token.cluster.name}}
clear
No privileged RBAC groups have been allocated to the project (or any of it's services), so escalation of privileges for this token is not possible just yet.


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.medcare.igtl.util.BytesArray
public class BytesArray
For reading/writing binary fields
| Field Summary | |
|---|---|
static long |
MAX_UINT
|
| Constructor Summary | |
|---|---|
BytesArray()
Destination Constructor |
| Method Summary | |
|---|---|
long |
crc64(byte[] buffer,
int len,
long crc)
Calculates CRC from a byte buffer based on the polynom specified in ECMA-182. |
long |
crc64(byte[] buf,
long crc)
Calculate a crc value Adds from a complete byte array to the
data checksum. |
double |
decodeDouble(byte[] data,
int ofs)
Decodes a double value from bytes, using IEEE 754 format |
long |
decodeLong(byte[] data,
int ofs,
boolean signed)
Decodes a long value from bytes |
long |
decodeTimeStamp(byte[] array)
Will read a 8 bytes array and return it as a millisec timestamp. |
byte[] |
encodeDouble(int len,
double val)
Encodes a double value into bytes |
byte[] |
encodeLong(int len,
long val,
boolean signed)
Encodes a long value into bytes |
byte[] |
encodeTimeStamp(long t)
Encodes a time in millisec in a 8 bytes array |
byte[] |
encodeTimeStamp(long s,
long f)
Encodes a time in millisec in a 8 bytes array |
byte[] |
getBytes()
Return a byte array representing the data |
byte[] |
getBytes(int length)
Read length
|
double |
getDouble(int length)
Read a double value from bytesArray, using IEEE 754 format |
int |
getIndex()
Gets the current read/write index return@ The index |
long |
getLong(int length)
Read a long value from bytesArray |
int |
getSize()
For an output/write bytesArray, returns the number of bytes written. |
java.lang.String |
getString(int len)
Converts the byte array to a String based on the US-ASCII character set. |
java.lang.String |
getString(int length,
boolean varLength)
Read a string from the bytesArray. |
long |
getULong(int length)
Read an unsigned long value from bytesArray |
long |
next_crc(long crc,
byte ch)
Calculates the next crc value. |
int |
putBytes(byte[] n)
Write an array of bytes to the bytesArray |
int |
putBytes(byte[] n,
int nLen)
Write an array of bytes to the bytesArray |
int |
putBytes(byte[] n,
int nOfs,
int nLen)
Write an array of bytes to the bytesArray |
long |
putCrc(byte[] buf,
int len,
long crc)
Calculate and Add a crc value to the bytesArray |
int |
putDouble(double val,
int wrtLen)
Add a double value to the bytesArray |
int |
putLong(long val,
int wrtLen)
Add a long value to the bytesArray |
int |
putString(java.lang.String s)
Add a string to the bytesArray. |
long |
putTimeStamp()
Calculate Add a timestamp value to the bytesArray |
int |
putTimeStamp(float t)
Add a timestamp value to the bytesArray |
int |
putULong(long val,
int wrtLen)
Add a unsigned long value to the bytesArray. |
void |
setIndex(int ndx)
Resets the read/write index to the specified value |
short |
unsignedByteToShort(byte b)
Converts an unsigned byte to a short. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final long MAX_UINT
| Constructor Detail |
|---|
public BytesArray()
| Method Detail |
|---|
public int putBytes(byte[] n, int nOfs, int nLen)
n
- The bytes to write to the bytesArraynOfs
- The offset into n
to start reading fromnLen
- The number of bytes to write from n
public int putBytes(byte[] n, int nLen)
n
- The bytes to write to the bytesArraynLen
- The number of bytes to write from n
public int putBytes(byte[] n)
n
- The bytes to write to the bytesArray
public int getSize()
public int getIndex()
public void setIndex(int ndx)
ndx
- The value to set the indexpublic byte[] getBytes()
public byte[] getBytes(int length)
length
length
- The number fo bytes to read from the bytesArray
public long getLong(int length)
long
value from bytesArray
length
- The number of bytes to decode the value from
public long getULong(int length)
long
value from bytesArray
length
- The number of bytes to decode the value from
public double getDouble(int length)
double
value from bytesArray, using IEEE 754 format
length
- The number of bytes from which the value is decoded
public java.lang.String getString(int length, boolean varLength)
length
bytes have been readvarLength==true
)
length
- The maximum length to readvarLength
- Boolean telling if the string can be variable in length (stop
on a null)
public java.lang.String getString(int len)
len
- The number of bytes to convert to a String
public int putLong(long val, int wrtLen)
long
value to the bytesArray
val
- The value to writewrtLen
- The number of bytes to write the value into
public int putULong(long val, int wrtLen)
long
value to the bytesArray. Same as
writeLong()
val
- The value to writewrtLen
- The number of bytes to write the value into
public long putTimeStamp()
timestamp
value to the bytesArray
public int putTimeStamp(float t)
timestamp
value to the bytesArray
t
- float timestamp n
public long putCrc(byte[] buf, int len, long crc)
crc
value to the bytesArray
buf
- The bytes to callculate the crc fromlen
- The number of bytes to calaculate fromcrc
- actual crc value n
public int putDouble(double val, int wrtLen)
double
value to the bytesArray
val
- The value to writewrtLen
- The number of bytes to write the value into
public int putString(java.lang.String s)
wrtLen
bytes are written or the string terminates
s
- The string to write
public byte[] encodeLong(int len, long val, boolean signed)
long
value into bytes
len
- The number of bytes to encode the value toval
- The value to encodesigned
- true if signed false if unsigned
public long decodeLong(byte[] data, int ofs, boolean signed)
long
value from bytes
data
- The byte array to decode the value fromofs
- The offset into data
to start decoding fromsigned
- If the encoded bytes represent a signed value
public byte[] encodeDouble(int len, double val)
double
value into bytes
len
- The number of bytes to encode the value toval
- The value to encode
public double decodeDouble(byte[] data, int ofs)
double
value from bytes, using IEEE 754 format
data
- The byte array from which to decode the double
valueofs
- The offset into data
to start decoding
public byte[] encodeTimeStamp(long t)
t
- the time in millis from 00:00:00 January 1, 1970, UTC value
public byte[] encodeTimeStamp(long s, long f)
s
- the seconds from time in millis from 00:00:00 January 1, 1970,
UTC valuef
- the fraction of time in millis from 00:00:00 January 1, 1970,
UTC value
public long decodeTimeStamp(byte[] array)
array
- the array to decode TimeStamp from
public short unsignedByteToShort(byte b)
b
- the byte to get short from
public long crc64(byte[] buffer, int len, long crc)
crc
value Adds from a partial byte array to the
data checksum.
buffer
- The bytes to callculate the crc fromlen
- The number of bytes to calaculate fromcrc
- actual crc value n
public long crc64(byte[] buf, long crc)
crc
value Adds from a complete byte array to the
data checksum.
buf
- The bytes to callculate the crc fromcrc
- actual crc value n
public long next_crc(long crc, byte ch)
crc
- actual crc value n
ch
- The next byte
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I am honored and humbled to assume the Office of President of the Bioengineering Honor Society. For myself and on behalf of this club, I want to thank my predecessor for all she has done.
I want to recognize Anita for her role in the leadership, because she, not I, should have been the one becoming president today. Anita, you’ve given so much to this club - joy in a year as Social Chair and direction in a semester as EVP. We relied on you for each and every important project - two DWPs, the high school competition, a plethora of workshops, and countless socials. Your enthusiasm, your dependability, your natural and proven leadership - all these qualities made you the rock and backbone of BioEHS in this difficult time. I hope to be a worthy replacement of the great president you surely would have been.
For all of BioEHS - alumni, officers, members old and new - I want to apologize if this club has not lived up to your expectations. BioEHS needs to change after a year of stagnation, and it is the responsibility of this officer core to give the club the activation energy needed to revive it.
For we must make candidacy worthwhile to those who wish to join us, we must make membership meaningful for those who are a part of us, and we must make our actions useful for those around us.
My fellow bioengineers, fellow members of the Bioengineering Honor Society - the era of stagnation is over. Because today, the work to build a better BioEHS begins, and I hope to see all of you on the journey ahead.
Thank you, and good night!


Gregory Way, 2019
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(cowplot))
suppressPackageStartupMessages(library(ggrepel))
consensus <- "modz"
coef_dir <- file.path("figures", "coefficients")
figure_dir <- file.path(coef_dir, consensus)
results_dir <- "results"
empty_grob <- cowplot::ggdraw()
dir.create(figure_dir, recursive = TRUE, showWarnings = FALSE)
coef_plot <- function(
df, target_name, compartment_features, coef_theme, top_plot_num = 15
) {
# Compile a series of plots that describe model coefficients
#
# Arguments:
# df - coefficient dataframe with feature metadata and weights
# target_name - a string of a specific cell health model
# compartment_features - a vector of which features to plot
# coef_theme - a ggplot theme object to apply to all plots
# top_plot_num - the number of top individual features to visualize
# Subset the input dataframe to focus on the specific target
subset_coef_df <- coef_df %>% dplyr::filter(target == !!target_name)
# Extract and process specific feature sets
area_df <- subset_coef_df %>%
dplyr::filter(feature_group %in% c("AreaShape", "Neighbors")) %>%
dplyr::group_by(shuffle, feature_group) %>%
dplyr::mutate(feature_count = dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::group_by(shuffle, compartment, feature_group) %>%
dplyr::top_n(n = 1, wt = abs_weight) %>%
dplyr::group_by(compartment, feature_group, shuffle) %>%
dplyr::mutate(abs_max_weight = max(abs_weight)) %>%
dplyr::select(compartment, feature_group, shuffle, abs_max_weight, feature_count) %>%
dplyr::distinct() %>%
dplyr::ungroup() %>%
dplyr::mutate(feature_group = paste0(feature_group, " (n = ", feature_count, ")"),
weight_text = round(abs_max_weight, 2))
# Merge compartments with correlation data summary
compartment_df <- subset_coef_df %>%
dplyr::filter(feature_group %in% !!compartment_features) %>%
dplyr::group_by(shuffle, feature_group) %>%
dplyr::mutate(feature_count = dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::group_by(shuffle, compartment, feature_group, channel) %>%
dplyr::top_n(n = 1, wt = abs_weight) %>%
dplyr::group_by(compartment, feature_group, channel, shuffle) %>%
dplyr::mutate(abs_max_weight = max(abs_weight)) %>%
dplyr::select(compartment, feature_group, channel, shuffle, abs_max_weight, feature_count) %>%
dplyr::distinct() %>%
dplyr::ungroup() %>%
dplyr::mutate(feature_group = paste0(feature_group, " (n = ", feature_count, ")"))
corr_feature_count <- dim(subset_coef_df %>%
dplyr::filter(feature_group == "Correlation", shuffle == "Real"))[1]
correlation_df <- subset_coef_df %>%
dplyr::filter(feature_group == "Correlation") %>%
dplyr::group_by(channel, compartment, shuffle) %>%
dplyr::mutate(abs_max_weight = max(abs_weight)) %>%
dplyr::select(channel, compartment, shuffle, abs_max_weight) %>%
dplyr::distinct() %>%
dplyr::ungroup() %>%
dplyr::mutate(feature_count = corr_feature_count,
feature_group = paste0("Colocalization (n = ", corr_feature_count, ")"))
compartment_df <- dplyr::bind_rows(compartment_df, correlation_df)
# Process individual feature name info
total_features <- length(unique(subset_coef_df$feature))
total_non_zero_features <- nrow(
subset_coef_df %>%
dplyr::filter(shuffle == "Real") %>%
dplyr::filter(abs_weight > 0)
)
top_plot_num <- ifelse(total_non_zero_features < top_plot_num,
total_non_zero_features,
top_plot_num)
feature_title <- paste0(
round((total_non_zero_features / total_features) * 100, 2),
"% Non-Zero"
)
top_n_features <- subset_coef_df %>%
dplyr::filter(shuffle == "Real") %>%
dplyr::top_n(n = top_plot_num, wt = abs_weight) %>%
dplyr::pull(feature)
feature_order <- subset_coef_df %>%
dplyr::filter(shuffle == "Real") %>%
dplyr::arrange(weight) %>%
dplyr::pull(feature)
subset_coef_features_df <- subset_coef_df %>%
dplyr::filter(shuffle == "Real") %>%
dplyr::filter(feature %in% !!top_n_features)
subset_coef_features_df$feature <- factor(
subset_coef_features_df$feature, levels = feature_order
)
min_gradient <- min(subset_coef_df$abs_weight)
max_gradient <- max(subset_coef_df$abs_weight)
# Draw an empty plot for later
empty_grob <- ggdraw()
# 1st Plot - Area Features
# First, create an area background to fill missing elements
area_comparments <- unique(area_df$compartment)
area_feature_groups <- unique(area_df$feature_group)
area_shuffle <- unique(area_df$shuffle)
area_background <- tidyr::crossing(area_comparments, area_feature_groups, area_shuffle) %>%
dplyr::mutate(weight_text = "N/A")
colnames(area_background) <- c("compartment", "feature_group", "shuffle", "weight_text")
# Now plot
area_gg <- ggplot(area_df, aes(x = compartment, y = feature_group)) +
geom_point(data = area_background, fill="grey", size = 5, pch = 22) +
facet_wrap(~shuffle) +
scale_fill_gradient2(
name = "Max\nAbs. Weight",
low = "white",
high = "blue",
limits = c(min_gradient, max_gradient)
) +
ylab("Feature Group") +
xlab("Compartment") +
theme(axis.text.y = element_text(angle = 90, hjust = 0.5)) +
theme_bw() +
coord_fixed() +
geom_text(aes(label = weight_text), size = 1.5, data = area_background) +
geom_point(aes(fill = abs_max_weight), size = 5, pch = 22) +
geom_text(aes(label = weight_text), size = 1.5) +
coef_theme +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# 2nd Plot - Other Compartment Features
# First, create a background to fill missing elements
comp_compartments <- unique(compartment_df$compartment)
comp_channels <- unique(compartment_df$channel)
comp_feature_groups <- unique(compartment_df$feature_group)
comp_shuffle <- unique(compartment_df$shuffle)
comp_background <- tidyr::crossing(comp_compartments, comp_channels, comp_feature_groups, comp_shuffle) %>%
dplyr::mutate(weight_text = "N/A")
colnames(comp_background) <- c("compartment", "channel", "feature_group", "shuffle", "weight_text")
# Now, plot
compartment_gg <- ggplot(compartment_df,
aes(x = channel, y = feature_group)) +
geom_point(data = comp_background, fill="grey", size = 5, pch = 22) +
facet_grid(compartment~shuffle) +
scale_fill_gradient2(
name = "Weighted\nCoeffients\nAbs. Max",
low = "white",
high = "blue",
limits = c(min_gradient, max_gradient)
) +
ylab("Feature Group") +
xlab("Channel") +
coord_fixed() +
theme_bw() +
geom_text(aes(label = weight_text), size = 1.5, data = comp_background) +
geom_point(aes(fill = abs_max_weight), size = 5, pch = 22) +
geom_text(aes(label = round(abs_max_weight, 2)), size = 1.5) +
coef_theme +
theme(axis.text.x = element_text(angle = 90))
# 3th Plot - Individual Feature Names
feature_name_gg <- ggplot(subset_coef_features_df,
aes(x = feature, y = weight, fill = abs(weight))) +
geom_bar(color = "black", stat = "identity") +
scale_fill_gradient2(
name = "Abs. Weight",
low = "white",
high = "blue",
limits = c(min_gradient, max_gradient)
) +
ggtitle(feature_title) +
coord_flip() +
xlab("") +
ylab("Model Coefficient") +
theme_bw() +
coef_theme +
theme(legend.position = "none", axis.text.x = element_text(angle = 90))
# Get cowplot title
use_title <- label_df %>%
dplyr::filter(id == !!target) %>%
dplyr::pull(readable_name)
full_title <- ggdraw() +
draw_label(
use_title,
fontface = 'bold',
x = 0,
hjust = -0.1
)
coef_legend <- cowplot::get_legend(compartment_gg)
right_panel <- cowplot::plot_grid(
area_gg + theme(legend.position = "none",
plot.margin = margin(1.3, 1, 1, 1)),
compartment_gg + theme(legend.position = "none",
plot.margin = margin(1.3, 1, 1, 1)),
rel_heights = c(0.5, 1),
nrow = 2,
align = "h",
axis = "l"
)
full_panel <- cowplot::plot_grid(
feature_name_gg + theme(plot.margin = margin(1, 1, 1, 1)),
right_panel,
align = "none",
rel_widths = c(1, 0.9)
)
coef_full_gg <- cowplot::plot_grid(
full_panel,
coef_legend,
rel_widths = c(1, 0.1)
)
coef_gg <- cowplot::plot_grid(
full_title,
coef_full_gg,
nrow = 2,
rel_heights = c(0.1, 1)
)
return(coef_gg)
}
# Annotated Cell Health Features
feat_file <- file.path(
"..",
"1.generate-profiles",
"data",
"labels",
"feature_mapping_annotated.csv"
)
label_df <- readr::read_csv(feat_file, col_types = readr::cols())
# Load Regression Results
regression_file <- file.path(
results_dir,
paste0("full_cell_health_regression_", consensus, ".tsv.gz")
)
regression_metrics_df <- readr::read_tsv(regression_file, col_types = readr::cols()) %>%
dplyr::filter(cell_line == "all", metric == "r_two", data_fit == "test")
regression_metrics_df$shuffle <- dplyr::recode(
regression_metrics_df$shuffle,
"shuffle_true" = "Permuted",
"shuffle_false" = "Real"
)
regression_metrics_df$shuffle <- factor(
regression_metrics_df$shuffle,
levels = c("Real", "Permuted")
)
print(dim(regression_metrics_df))
head(regression_metrics_df)
coef_file <- file.path(
"results", paste0("full_cell_health_coefficients_", consensus, ".tsv.gz")
)
coef_df <- readr::read_tsv(
coef_file,
col_types = readr::cols(
feature = readr::col_character(),
weight = readr::col_double(),
abs_weight = readr::col_double(),
target = readr::col_character(),
y_transform = readr::col_character(),
shuffle = readr::col_character()
)
) %>%
dplyr::filter(y_transform == "raw") %>%
tidyr::separate(
feature,
into = c(
"compartment",
"feature_group",
"measurement",
"channel",
"parameter1",
"parameter2"
),
sep = "_",
remove = FALSE
)
coef_df$shuffle <- dplyr::recode(
coef_df$shuffle,
"shuffle_true" = "Permuted",
"shuffle_false" = "Real"
)
coef_df$shuffle <- factor(
coef_df$shuffle,
levels = c("Real", "Permuted")
)
print(dim(coef_df))
head(coef_df, 5)
compartment_features <- c(
"Texture",
"Intensity",
"RadialDistribution",
"Granularity"
)
top_plot_num <- 15
coef_theme <- theme(
strip.text = element_text(size = 7,
color = "black",
margin = margin(1, 1, 1, 1)),
strip.background = element_rect(colour = "black",
fill = "#fdfff4"),
axis.text = element_text(size = 7),
axis.title = element_text(size = 8),
plot.title = element_text(size = 8),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7),
legend.key.width = unit(0.5, "cm"),
legend.key.size = unit(0.3, "cm")
)
summary_coef_theme <- theme(
strip.text = element_text(size = 9,
color = "black",
margin = margin(1, 1, 1, 1)),
strip.background = element_rect(colour = "black",
fill = "#fdfff4"),
axis.text = element_text(size = 7),
axis.title = element_text(size = 9),
plot.title = element_text(size = 9),
legend.title = element_text(size = 8),
legend.text = element_text(size = 7),
legend.key.width = unit(0.5, "cm"),
legend.key.size = unit(0.3, "cm")
)
point_size <- 10
text_label_size <- 3
pdf_file <- file.path(
coef_dir,
paste0("all_model_coefficients_", consensus, ".pdf")
)
pdf(pdf_file, width = 7, height = 8, onefile = TRUE)
for (target in unique(coef_df$target)) {
coef_gg <- coef_plot(
df = coef_df,
target_name = target,
compartment_features = compartment_features,
coef_theme = coef_theme,
top_plot_num = top_plot_num
)
output_file <- file.path(
figure_dir,
paste0("model_", consensus, "_", target, ".png")
)
cowplot::save_plot(output_file, coef_gg, base_height = 5.5, base_width = 7.5)
print(coef_gg)
}
dev.off()
First, merge Regression Performance with Coefficients
coef_summary_df <- coef_df %>%
dplyr::left_join(regression_metrics_df, by = c("target", "y_transform", "shuffle")) %>%
dplyr::mutate(weighted_coef = weight * value, abs_weighted_coef = abs(weight * value)) %>%
dplyr::group_by(feature, shuffle) %>%
dplyr::mutate(
abs_max_weight = max(abs_weight),
abs_mean_weight = mean(abs_weight),
abs_95percentile = quantile(abs_weight, 0.95)
) %>%
dplyr::select(
feature,
abs_max_weight,
abs_mean_weight,
abs_95percentile,
compartment,
feature_group,
measurement,
channel,
parameter1,
shuffle
) %>%
dplyr::distinct()
head(coef_summary_df)
min_gradient <- 0
point_size <- 9
summary_metrics <- c("abs_mean", "abs_max", "abs_95th")
for (shuffle_option in c("Permuted", "Real")) {
summary_subset_df <- coef_summary_df %>% dplyr::filter(shuffle == !!shuffle_option)
for (summary_metric in summary_metrics) {
if (summary_metric == "abs_mean") {
column_choice <- "abs_mean_weight"
stat_string <- "aggregated_mean"
legend_name <- "Weighted Coeffients\nAbs. Mean (x100)"
} else if (summary_metric == "abs_max") {
column_choice <- "abs_max_weight"
stat_string <- "aggregated_max"
legend_name <- "Weighted Coeffients\nAbs. Max"
} else if (summary_metric == "abs_95th") {
column_choice <- "abs_95percentile"
stat_string <- "aggregated_95th"
legend_name <- "Weighted Coeffients\nAbs. 95th Percent"
}
# Process area different from other features
area_df <- summary_subset_df %>%
dplyr::filter(feature_group %in% c("AreaShape", "Neighbors")) %>%
dplyr::group_by(compartment, feature_group, shuffle) %>%
dplyr::mutate(
aggregated_max = max(abs_max_weight),
aggregated_mean = mean(abs_mean_weight) * 100,
aggregated_95th = quantile(abs_95percentile, 0.95),
feature_count = dplyr::n()
) %>%
dplyr::select(
compartment, feature_group, shuffle, aggregated_max, aggregated_mean, aggregated_95th, feature_count
) %>%
dplyr::distinct() %>%
dplyr::ungroup() %>%
dplyr::mutate(
aggregated_max_round = round(aggregated_max, 2),
aggregated_mean_round = round(aggregated_mean, 2),
aggregated_95th_round = round(aggregated_95th, 2)
) %>%
dplyr::group_by(feature_group) %>%
dplyr::mutate(
feature_group_count = sum(feature_count)
) %>%
dplyr::ungroup() %>%
dplyr::mutate(feature_group = paste0(feature_group, " (n = ", feature_group_count, ")"))
area_df <- area_df %>%
dplyr::mutate(weight_text = round(area_df[, stat_string], 2) %>% dplyr::pull(stat_string))
area_comparments <- unique(area_df$compartment)
area_feature_groups <- unique(area_df$feature_group)
area_shuffle <- unique(area_df$shuffle)
area_background <- tidyr::crossing(area_comparments, area_feature_groups, area_shuffle) %>%
dplyr::mutate(weight_text = "N/A")
colnames(area_background) <- c("compartment", "feature_group", "shuffle", "weight_text")
# Process correlation different from other features
corr_feature_group <- summary_subset_df %>%
dplyr::filter(feature_group == "Correlation")
corr_feature_count <- dim(corr_feature_group)[1]
correlation_df <- corr_feature_group %>%
dplyr::group_by(channel, compartment, shuffle) %>%
dplyr::mutate(
aggregated_max = max(abs_max_weight),
aggregated_mean = mean(abs_mean_weight) * 100,
aggregated_95th = quantile(abs_95percentile, 0.95),
) %>%
dplyr::select(
channel, compartment, shuffle, aggregated_max, aggregated_mean, aggregated_95th
) %>%
dplyr::distinct() %>%
dplyr::ungroup() %>%
dplyr::mutate(
aggregated_max_round = round(aggregated_max, 2),
aggregated_mean_round = round(aggregated_mean, 2),
aggregated_95th_round = round(aggregated_95th, 2),
) %>%
dplyr::mutate(feature_group_count = corr_feature_count,
feature_group = paste0("Colocalization (n = ", corr_feature_count, ")"))
correlation_df <- correlation_df %>%
dplyr::mutate(weight_text = round(correlation_df[, stat_string], 2) %>% dplyr::pull(stat_string))
# Process compartment features different from other features
compartment_df <- summary_subset_df %>%
dplyr::filter(feature_group %in% !!compartment_features) %>%
dplyr::group_by(compartment, channel, feature_group, shuffle) %>%
dplyr::mutate(
aggregated_max = max(abs_max_weight),
aggregated_mean = mean(abs_mean_weight) * 100,
aggregated_95th = quantile(abs_95percentile, 0.95),
feature_count = dplyr::n()
) %>%
dplyr::select(
compartment,
channel,
feature_group,
shuffle,
aggregated_max,
aggregated_mean,
aggregated_95th,
feature_count
) %>%
dplyr::distinct() %>%
dplyr::ungroup() %>%
dplyr::mutate(
aggregated_max_round = round(aggregated_max, 2),
aggregated_mean_round = round(aggregated_mean, 2),
aggregated_95th_round = round(aggregated_95th, 2)
) %>%
dplyr::group_by(feature_group) %>%
dplyr::mutate(
feature_group_count = sum(feature_count)
) %>%
dplyr::ungroup() %>%
dplyr::mutate(feature_group = paste0(feature_group, " (n = ", feature_group_count, ")"))
compartment_df <- compartment_df %>%
dplyr::mutate(weight_text = round(compartment_df[, stat_string], 2) %>% dplyr::pull(stat_string))
# Merge compartments with correlation data summary
compartment_df <- dplyr::bind_rows(compartment_df, correlation_df)
comp_comparments <- unique(compartment_df$compartment)
comp_channels <- unique(compartment_df$channel)
comp_feature_groups <- unique(compartment_df$feature_group)
comp_shuffle <- unique(compartment_df$shuffle)
comp_background <- tidyr::crossing(comp_comparments, comp_channels, comp_feature_groups, comp_shuffle) %>%
dplyr::mutate(weight_text = "N/A")
colnames(comp_background) <- c("compartment", "channel", "feature_group", "shuffle", "weight_text")
max_gradient <- max(
max(compartment_df %>% dplyr::pull(stat_string)),
max(area_df %>% dplyr::pull(stat_string))
)
area_gg <- ggplot(area_df, aes(x = compartment, y = feature_group)) +
geom_point(data = area_background, fill = "grey", size = point_size, pch = 22) +
scale_fill_gradient2(
name = legend_name,
low = "white",
high = "blue",
limits = c(min_gradient, max_gradient)
) +
ylab("Feature Group") +
xlab("Compartment") +
geom_text(aes(label = weight_text), size = text_label_size, data = area_background) +
geom_point(aes_string(fill = stat_string), size = point_size, pch = 22) +
geom_text(aes(label = weight_text), size = text_label_size) +
theme(axis.text.y = element_text(angle = 90, hjust = 0.5)) +
theme_bw() +
coord_fixed() +
summary_coef_theme
compartment_gg <- ggplot(compartment_df,
aes(x = channel, y = feature_group)) +
geom_point(data = comp_background, fill = "grey", size = point_size, pch = 22) +
geom_text(aes(label = weight_text), size = text_label_size, data = comp_background) +
geom_point(aes_string(fill = stat_string), size = point_size, pch = 22) +
geom_text(aes(label = weight_text), size = text_label_size) +
facet_grid(~compartment) +
scale_fill_gradient2(
name = legend_name,
low = "white",
high = "blue",
limits = c(min_gradient, max_gradient)
) +
ylab("Feature Group") +
xlab("Channel") +
coord_fixed() +
theme_bw() +
summary_coef_theme +
theme(axis.text.x = element_text(angle = 90))
# Compile full panel plot
bottom_panel <- cowplot::plot_grid(
compartment_gg + theme(legend.position = "none",
plot.margin = margin(1.3, 1.3, 1.3, 1.3))
)
main_legend_gg <- cowplot::get_legend(compartment_gg)
top_panel <- cowplot::plot_grid(
area_gg + theme(legend.position = "none"),
empty_grob,
main_legend_gg,
empty_grob,
ncol = 4,
rel_widths = c(1, 0.1, 0.4, 0.3)
)
big_fig <- cowplot::plot_grid(
top_panel,
bottom_panel,
nrow = 2,
rel_heights = c(0.7, 1),
align = "v",
axis = "l"
)
print(big_fig)
output_file <- file.path(
coef_dir,
paste0(
"coefficient_summary_", shuffle_option, "_", consensus, "_", summary_metric, ".png"
)
)
cowplot::save_plot(output_file, big_fig, base_height = 4, base_width = 6, dpi = 500)
}
}


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Sat Mar 7, 2015
At the end of this year, I’ll finally be upgrading to a new phone. After three years with the HTC One S, I’ve realized that… three years is a long time. So, I’ll be skipping the entry-level and mid-tier phones and just shell out for a flagship. For a device you use everyday, it’s worth getting the best.
I’ll be blogging about all the flagship contenders that come out this year in a series of blog posts, then decide on the winner by December. In the meantime, here’s a fun comparison between my three-year old mid-tier phone and the latest iPhone. The results are actually quite surprising:
Now, I’ll freely admit that I’ve cherry picked the specs listed here, but most of these are essential hardware requirements.
It is obvious that the latest iPhone has a far better screen and a bigger battery.
However, the HTC One S and the iPhone 6 are actually on par in a few areas: The same 3G network speed (I’m not bringing LTE into this) and the same RAM. Both have an eight-megapixel camera. Both record 1080p video. All the standard smartphone stuff like GPS and Bluetooth is all on equal ground.
But wait a minute. My HTC One S actually has a faster CPU…sure, it’s only 0.1 GHz faster, but still, for a three-year old mid-tier device, that’s pretty amazing. Not only that, but the One S is lighter, and has extras such as the FM Radio and ability to use Flash in the browser.
Even design-wise, both these phones have sleek curved lines that make for a pleasant tactile experience - but the One S came first. So, on paper at least, the HTC One S seems to hold its own against what is ostensibly the best smartphone in the world.
If only that were true.
My HTC One S has become slow and lags in a number of areas. Heck, even the ringtone stutters and crackles when the phone rings. The undeniable truth is that specs don’t matter. By integrating both the hardware and software, Apple can focus on the user experience from the ground up and is utterly aloof from the spec war. Android phones must have killer specs because only overkill will guarantee a great user experience - like hitting a nail with Mjölnir. By flashing a custom ROM onto my phone, I’ve further divorced the software from the hardware. The camera, in particular, has gone from not-bad to atrocious.
Allow me to emphasize: I’m no fanboy of either Android or Apple and am a user of both platforms. Each have their merits and drawbacks. And I don’t regret going the custom ROM route. But in the year to come, when I’m taking a cold, hard look at the flagships - I’m not going to take the specs that seriously. A 20MP camera is useless without proper image processing. More RAM is useless if it drains the battery. When you design a phone, there are many tough choices to be made, but if you’re not putting the user experience first, then you’re making the wrong ones.


The United States of America has a well regulated militia, guaranteed by the second amendment to the Constitution.
Devin Patrick Kelley shot 46 people (including an unborn child).
The United States does not need more regulation of guns. It does not need regulation requiring a mental health assessment of a gun purchaser. It has a Well Regulated Militia. Learn more about Devin Patrick Kelley and other members of the Well Regulated Militia of the United States of America.


--- title: The Best Matzo Ball Soup layout: post date: '2015-12-07 02:19:56 -0500' type: post parent_id: 0 published: true image: assets/images/post-img/20150331-matzo-balls-daniel-gritzer-24-thumb-625xauto-421450.jpg status: publish categories: - Soups-Stews-Chilis tags: - passover meta: _thumbnail_id: 273 _edit_last: 1 _posts_by_tag_page_fields: a:2:{s:12:"widget_title";s:0:"";s:11:"widget_tags";s:0:"";} _wpas_done_all: 1 _wpghs_github_path: _posts/2015-12-07-the-best-matzo-ball-soup.md _sha: 2fdc3feefd9d4b622285039634753b333b3ae1bb author: login: mkelley email<EMAIL_ADDRESS>display_name: Mike first_name: Mike last_name: Kelley ---
Some people like sinkers, some people like floaters. Here at Serious Eats, we're equal opportunity matzo-ballers, so we're giving a recipe that lets you choose the matzo balls of your dreams. Best part, it's ridiculously easy.


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--- title: Maintenance body_class: page-maintenance layout-full --- {{#extend "layout-blank"}} {{#content "css" mode="prepend"}} {{/content}} {{#content "body"}}
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Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Makes 8 biscuits
Dietary
These easy biscuits can be made by kids with only a little grown-up help, and they're deliciously crunchy.
Before cooking, wash your hands and put on an apron. Weigh or measure all the ingredients. Ask a grown-up to preheat the oven to 180C/350/Gas 4. Place a sheet of baking paper onto a baking tray.
Put the flour and butter into a mixing bowl and rub the two together to make a crumbly mixture.
Snip the stalk off the dried fig then cut the fruit into small pieces using the scissors (ask a grown-up to help you). Add to the bowl with the sugar and the fudge pieces.
Use your hands to start to squeeze it all together – you can push down with your knuckles into the bowl. As it starts to make a lump, turn the bowl out onto the work surface and continue squeezing to make a long, fat sausage shape.
Tear the mixture in half. Pull each piece into half again then each smaller piece into two, trying to keep them about the same size. This makes eight pieces of biscuit dough.
Roll each piece into a round ball then put onto the lined tray and flatten a little with your hand – repeat with the other seven biscuits.
Ask a grown-up to put the baking tray in a preheated oven for 12-15 minutes or until the biscuits are light golden-brown. Leave to cool on the paper. Serve with some red grapes or more dried fig.


Investigation launched as man dies suddenly at care home
Police and the North West Ambulance Service were called to the property
by Luke TraynorThe death of a man at a care home is being treated as "unexplained" by police.
Police and the North West Ambulance Service were called to St Martins in Liscard, at around 4.30pm on Saturday, after a resident had become unresponsive.
The 56-year-old was taken to hospital where he was sadly pronounced dead a short time later.
The death is currently being treated as unexplained.
Merseyside Police told the ECHO the man's death is not being treated as suspicious.
He has been formally identified, next of kin have been informed and a Home Office post mortem will take place to establish the cause of death.
A manager for St Martins declined to comment.
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Screencast of Design Prototype
Examples from design process
Screencast of Design Prototype
Some Research Findings
Summary of UX Research
Problem identified from UX research
Screencast of Presentation
Top of Landing page
Example from Logo Design Process
Icon Development Process
Icons for UI
UI testing example
Example of the UI
Screencast of the Re-design Prototype
UX Research Process to reach the Problem Statement
One screen of the Re-design
Screencast of Prototype
Find your gaming partner here (Co-Op Match Screen)
Creating IA that is transparent & user-centred
Wireframes with IA incorporating the features & user flow decided on by the Gamerholics team
A closer look at a wireframe with IA
The Trello Co-Op board of our team. We used this for resource sharing, planning and communication
Logo Design Process
Developing my UI design skills


edu.um.cps2002.tile_game
Classes
Game
GameTest
HazardousMap
HazardousMapTest
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Map
MapCreator
MapCreatorTest
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PlayerTest
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Tile


load-component/load-component.directive.ts
A directive that uses the LoadComponentService to dynamically load a component.
The loaded component's lifecycle is automatically managed with changes to the directive's
options
reflected to the component by rebinding it using LoadComponentService
| Selector | [sqLoadComponent] |
Methods |
Inputs |
Outputs |
constructor(loadComponentService: LoadComponentService, viewContainerRef: ViewContainerRef)
|
|||||||||
|
Parameters :
|
| sqLoadComponent | |
Type : LoadComponentOptions
|
|
|
Options for the loading of a component |
| sqLoadComponent | |
Type : EventEmitter
|
|
|
Used to emit events when the component is created and destroyed |
| ngOnChanges |
ngOnChanges()
|
|
Handles any changes to the input The component is loaded using the
Returns :
void
|
| ngOnDestroy |
ngOnDestroy()
|
|
Unbinds the loaded component. It is not destroyed here as angular will automatically destroy
the component at the same time as this directive is destroyed as they share the same
Returns :
void
|
import {Directive, Input, Output, OnChanges, OnDestroy, ViewContainerRef, ComponentRef, EventEmitter, Type} from "@angular/core";
import {LoadComponentService, LoadComponentOptions, LoadedComponent} from "./load-component.service";
/**
* A directive that uses the {@link LoadComponentService} to dynamically load a component.
* The loaded component's lifecycle is automatically managed with changes to the directive's
* `options` reflected to the component by rebinding it using {@link LoadComponentService}
*/
@Directive({
selector: "[sqLoadComponent]"
})
export class LoadComponentDirective implements OnChanges, OnDestroy {
/**
* Options for the loading of a component
*/
@Input("sqLoadComponent") options: LoadComponentOptions;
/**
* Used to emit events when the component is created and destroyed
*/
@Output("sqLoadComponent") eventEmitter = new EventEmitter<{componentRef: ComponentRef<Type<any>> | undefined}>();
private loadedComponent: LoadedComponent;
private currentComponent: Type<any>;
constructor(
private loadComponentService: LoadComponentService,
private viewContainerRef: ViewContainerRef) {
}
/**
* Handles any changes to the input `options`. On the first call the component is loaded
* and bound. Subsequent changes to the `options` are handled by rebinding the component unless
* the component type to load changes in which case the current component is unloaded before
* loading the new component.
*
* The component is loaded using the `ViewContainerRef` associated with the directive
*/
ngOnChanges() {
if (this.loadedComponent) {
if (this.currentComponent === this.options.component) {
this.loadComponentService.bindComponent(this.options, this.loadedComponent);
return;
}
this.loadComponentService.unbindComponent(this.loadedComponent);
this.loadedComponent.componentRef.destroy();
this.eventEmitter.emit({componentRef: undefined});
}
this.loadedComponent = this.loadComponentService.loadComponent(this.options, this.viewContainerRef);
this.currentComponent = this.options.component;
this.eventEmitter.emit({componentRef: !!this.loadedComponent ? this.loadedComponent.componentRef : undefined});
}
/**
* Unbinds the loaded component. It is not destroyed here as angular will automatically destroy
* the component at the same time as this directive is destroyed as they share the same `ViewContainerRef`
*/
ngOnDestroy() {
if (this.loadedComponent) {
this.loadComponentService.unbindComponent(this.loadedComponent);
this.eventEmitter.emit({componentRef: undefined});
}
}
}


Before Change
// This verifies that the file was indeed provided as additional input to the pex call.
preamble_file = "custom_preamble.txt"
preamble = "//!CUSTOM PREAMBLE\n"
additional_inputs_content = InputFilesContent(
(FileContent(path=preamble_file, content=preamble.encode()),)
)
additional_inputs = self.request_single_product(Digest, additional_inputs_content)
additional_pex_args = (f"--preamble-file={preamble_file}",)
pex_output = self.create_pex_and_get_all_data(
additional_inputs=additional_inputs, additional_pex_args=additional_pex_args
)
After Change
// This verifies that the file was indeed provided as additional input to the pex call.
preamble_file = "custom_preamble.txt"
preamble = "//!CUSTOM PREAMBLE\n"
additional_inputs = self.request_single_product(
Digest, CreateDigest([FileContent(path=preamble_file, content=preamble.encode())])
)
additional_pex_args = (f"--preamble-file={preamble_file}",)
pex_output = self.create_pex_and_get_all_data(
additional_inputs=additional_inputs, additional_pex_args=additional_pex_args
)


Override Refund Result
Override Refund Successfully Processed.
Amount
12.00
OSC
APP
Order ID
A12345
Shipping/Contact Information
firstNamelastName
shippingAddress
addressLine1
addressLine2
city region
postalCode
country
phoneNumber
email
This is the name and address of the person who will receive the product/service (e.g., the person who will be taking a test or is requesting scores); or, if you ordered items to be shipped, they will be sent to this address.


General Election seats to watch: North-west England
The Tories are eyeing a dozen gains in this battleground region.
The choice of voters in north-west England could make the difference between a Conservative government with a comfortable majority in the next parliament, and one that has a very slim majority – or even none at all.
There are nine seats in the region that would fall to the Tories on a modest swing of 5%, ranging from the ultra-marginal Crewe & Nantwich (where Labour is defending a majority of just 48 and a 0.04% swing is needed) to Workington (4.7%).
If the Tories manage a swing of 8%, the total number of potential gains rises from nine to 15.
All of these seats bar one are places that Labour won in 2017. The exception is Westmorland & Lonsdale, constituency of former Lib Dem leader Tim Farron, which the Conservatives would take on a swing of just 0.8%.
With so many seats in play, and with polls suggesting Labour is at risk of losing ground across the region, it’s clear the party faces a real challenge on December 12.
If Labour manages to hold on to the likes of Warrington South (where the Tories need a 2.1% swing) and Blackpool South (3.6%), the losses may be kept to a minimum.
Tactical voting in some of these constituencies might also help Labour hold on.
It could even help Labour make a couple of gains in places like Bolton West and Pendle: both Conservative-held seats, and both needing only a small swing (0.9% and 1.4%) to change hands.
Ivan Lewis won Bury South for Labour in 2017, resigned from the party in 2018 and will be on the ballot paper this year as an independent candidate, though in the past few days he has publicly endorsed the Conservative candidate.
The Tories need a swing of 5.9% to take the seat.
Elsewhere the Liberal Democrats have their sights on the Tory-held seats of Cheadle and Hazel Grove.
The swings needed (4.2% and 6.3% respectively) might put them beyond the party’s reach, though again tactical voting could make a difference.