Is Your Brain Trying to Hold You Back?
Only you an stop yourself
Zenpra 11th March 2015 0 Comments
Learn to Push Through Limits and Develop Grit
How do athletes cultivate the mental toughness to achieve greatness? How are they able to dig deep inside themselves and find the energy when most people would just give up? Research has shown that this mental toughness can be learned through practices such as meditation and neruofeedback.
More people are becoming interested in how we can tap into the power of our brain to improve performance. It can not only help in sports but also at work or in school. Meditation and contemplation can give you the tools to understand your metal frameworks and how they limit your actions and thoughts.
I will also be showing you how to develop grit and remove negative limitations. When you are feeling down and winded learn how to reach into your untapped potential. We might feel depleted of energy but there are times when we can dial in to push through to achieve our goals.
Meditation to overcome your limits
I regularly go to the gym and lift weights ( I should say try lol). I use weightlifting as a workout not just for my muscles but also for my mind. When I feel that I do not have enough energy to perform that extra rep I try to vision energy deep within me to give me the power to push through. Overtime it becomes a lot easier to get access to this mental thought process . It may be all in my head but it does produce a noticeable physical response.
Here is a simple meditation that you can use to help dig deep within you to gain more energy and overcome mental roadblocks.
- Close your eyes and witness your breath.
- When you inhale imagine the air being a bright energetic light
- When the air reaches your lungs imagine the bright light build up in your naval.
- When you exhale imagine the light traveling through your whole body and invigorating you.

- Get back to your work with a new found sense of energy You will feel invigorated and energized to tackle your problems.
This visualization technique can help you muster up the remaining energy that you have. Also by increasing your breathing rate you will stimulate your body. You will be amazed that you are able to achieve something that a few minutes ago thought was impossible.
Digging deeper into ourselves
While I was writing this post I happened to came across this video below that shows some of the research being done on human potential and surpassing limits. There is growing interest and research in the field of human performance and cognitive improvement. I was really surprised to hear Red bull is researching human performance they even have a “Human Performance Lab”!
Companies such as GE and Red Bull are researching the mind’s capabilities and how technology can enhance it. They explore the physiological changes after people go through grueling experiences. They want to explore how does the mind and body adapt to these demanding situations where we past our “limits”. They want to use these findings to come up with methods that can enhance your performance scientifically.
GE is developing state of the art brain sensors to allow for better understanding of cognitive processes. It also opens up the ability to control technology such as robotic arms and computers. There is even a guy controlling a drone with his mind in the video!
 Biofeedback has actually been shown to improve performance and companies such as Red Bull are using it to test human limitations. Biofeedback allows you link your brain state and how you can actively control it. So you will be able to learn over time how to reach an optimal brain state without the need of a senor.
 What are the physiological changes that are occurring that give her that strength? What if we can learn how to consciously harness just 1/10
th of this power?
Enhancing our lives not just healing our lives
Most of the time we are just trying to fix a current problem we have . However, meditation can also help enhance our lives. We can be able to perform at our peak performance to bring more into our lives.
Meditation can be viewed as us not doing anything. In a society where we frown upon idle time it is important to realize that by taking a break and making time for your self is actually more beneficial. A key thing is to actually be aware that your limits. You should reflect on why you feel this way and what is actually stopping you from achieving what you have to do or your goals. We sometimes do not want to be mindful of our inadequacies and our limitations. However, we should not view are failure as a negative instead as a gateway to learn what we can do to improve ourselves.
Developing Mental Grit
Mental grit is your ability to not give up when the cards are not in your favor. When we are faced with challenges in life it is easy to just give up. How do we learn how to handle these challenges and overcome them? If you ever watched Marshawn Lynch(Seahawks running back BEAST MODE) or any running back you can see they have grit. They keep on running no matter how many people are trying to tackle them down. It is that focus and determination which we can learn how to cultivate in our own lives so we can never give up and keep pushing through with whatever life throws at us.
If there is an obstacle in our life that we know we have to face then use that as a starting point for meditation. First just be present with what you are facing such as I need to finish this report by tonight. When you are at the point where you have no energy and just feel like giving up then there is your opportunity to witness that and then dive deep within yourself and try to muster up enough energy to push through 10 more minutes. By you recognizing that point as your limit is your first step to overcome it. Overtime you will be able to increase your capacity to handle tough situations. It is this incremental process that will increase your girt and ability to handle demanding situations.
Also, always remember to relax and wind down. It is not always good to be going non stop with no break. Learn to relax more efficiently. I will discuss how to take more effective breaks in a future posting.
Removing Mental Limitations
At my job I have to make a lot of cold calls. At first I had a lot of mental limits that I set for myself. I was afraid what people would say or if I would say something stupid. However, one day I just decided you know what it is actually my mind that is creating this problem. I imagined almost a cage that was surrounding my brain. I then wrote down what I really felt and what I thought was limiting me. As I got the words on the paper I pictured dismantling the cage. Now if I have any negative thoughts when cold calling I would catch myself and say that this is my mind limiting itself, I just need to just act. I would invite you to try writing down what you think is limiting you and see for yourself how you can change your mindset.
The Bigger Picture of Using Meditation for Self Improvement
Life has become very challenging from school, work, and even what food to eat. How do we get that edge, what can we do be more in control of our situation? Just like how we learn a new skill or language to enhance our career we can use meditation as a method to give us more energy and grit. It also gives us time to relax and decompress so we are recharged for the next challenge. I find the key to self improvement is to accept the way you are now and any improvement is just exploring more of yourself. A very important insight is that we learn not to fight against a situation but flow with it and see the opportunity in a perceived challenge. I find that we can always improve ourselves but we shouldn’t feel that we are inadequate with how we currently are. If we accept who we presently are we don’t look at self improvement as a need to overcome our inadequacies but instead as a game to see what we can achieve and how we can improve.
What challenging situations have you overcome?
Tell us your story of any challenges you overcame and how it changed your view of the world. Also, what do you think is possible in the future when we combine tech and the power of the mind?


GPU Genomics is an organisation that aims to provide scalable analytics tool for large scale Genomics.
The tools are Python-first.
Projects:
If you want to use or help with these tools drop a mail to<EMAIL_ADDRESS>The softwares are distributed under the BSD-3 license.
Copyright © 2019, Prasun Anand


{% extends 'base.html' %} {% block nav %}{{ nav('about') }}{% endblock %} {% block content %}
We hate complicated terms of service too, so let's keep this simple, shall we? If anything seems out of place, you don't have to sign up, but please tell us what you don't like, because we want to know if you think we're doing something sleazy.
In addition to the terms below, foauth.org is also subject to the Runscope Terms of Service.
These aren't part of the official terms of service, so you don't have to agree to do anything on this final list. But we're working hard to bring you a service you'll enjoy, and it'd be awesome if you like it so much that you'd want to do something more.


Add BBLog User in Battlelog - Better Battlelog Forums #110883
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10.  November 2013 11:15
Topicstarter
I_like_Chocolate
I would like to add some of you in Battlelog.
So please say: "Yes!", when i can add YOU!
-Chocolate
;)
11. November 2013 18:41
Meaurk
Global Moderator
Well, why not. :-)


The parable of Ox
Author starts with an interesting parable not just about financial market, but actually related to many aspects of economics. This parable is worth remembering for every economist and business man.
In 1906, Francis Galton observed a weight guessing competition at a country fair. 800 people guessed the weight of an ox. He discovered that average guess was very closed to the true weight of the ox. (LLN)
What follows is cleverly made by the author. * The organizer started to use the average weight as the true weight. * Some guesser tried to cheat based on the privileged information from the farmer who raised the ox. Others would be reluctant to participate in the competition since some insiders had an edge. With few participants, the LLN fails. (Lemon Market - Asymmetric Information Problem) - Regulation was introduced to save the weight discovery mechansim. The farmer was asked to reveal all the relevant information to the public, and was forbidden to provide insider information to anyone, or else he would be expelled from the market. - Some smarter guy started to realized that since the award was not based on the true weight of the ox, but the average of the guessing weights. What mattered were the guesses of the other participants. - The key lay not in correctly assessing the weight of the ox, but in correctly assessing what others would guess. - When more and more people started to think that way. A rational participants would start to guess what other people would guess others would guess, and so on. - The whole weight guessing competition became the “guess” guessing competition. In order to have a valid guess (LLN needs to work), a large number of guesses were required. That comes the financial economist and econometricians who developed models to estimate what, if there had actually been many guesses, the average of the guesses would have been. - No one cared about the true weight of the ox. One one cared whether the ox was alive or dead.


About me...
I'm a prolific forger of impressive brand identities, a creative Web/Software Developer with more than 3 years experience in delivering result-oriented brand and creative solutions to meet business needs.
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--- layout: post title: When I was a boy, if you went to the harbour to fish and you got wet, you could not fish again until the next day, because you had only one pair of trousers. Today people have too many trousers. date: '2008-10-17T08:53:41+01:00' tags: [] tumblr_url: http://backlog.robbevan.com/post/54977872/when-i-was-a-boy-if-you-went-to-the-harbour-to ---
When I was a boy, if you went to the harbour to fish and you got wet, you could not fish again until the next day, because you had only one pair of trousers. Today people have too many trousers.— Iceland, fishing for answers | World news | The Guardian


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I'd like to tell you a little bit about me
Here are some fun facts about my life:
- I have never learned to play guitar, even though I've taken lessons.
- I brew beer at home (don't worry, sterilization is part of the brewing process).
- I own an extremely rare breed of dog. He is absolutely my spoiled baby.
- I play Dungeons&Dragons unironically, like the huge geek I am.
- I love to visit haunted places, and recently went to a haunted lighthouse (Heceta Head in Oregon).
These are my top ten favorite books & series (Graphic Novels are included)
- Harry Potter
- Game of Thrones
- Hunger Games
- Watchmen
- Rat Queens
- Howl's Moving Castle
- Mortal Instruments
- Lord of the Rings
- Enter's Game
- Sabriel


JavaScript Re-Declaring JavaScript Variables
If you re-declare a JavaScript variable, it will not lose its value.


- NAME
- Tcl_LimitAddHandler, Tcl_LimitCheck, Tcl_LimitExceeded, Tcl_LimitGetCommands, Tcl_LimitGetGranularity, Tcl_LimitGetTime, Tcl_LimitReady, Tcl_LimitRemoveHandler, Tcl_LimitSetCommands, Tcl_LimitSetGranularity, Tcl_LimitSetTime, Tcl_LimitTypeEnabled, Tcl_LimitTypeExceeded, Tcl_LimitTypeReset, Tcl_LimitTypeSet - manage and check resource limits on interpreters
- SYNOPSIS
- #include <tcl.h>
- int
- Tcl_LimitCheck(interp)
- int
- Tcl_LimitReady(interp)
- int
- Tcl_LimitExceeded(interp)
- int
- Tcl_LimitTypeExceeded(interp, type)
- int
- Tcl_LimitTypeEnabled(interp, type)
- void
- Tcl_LimitTypeSet(interp, type)
- void
- Tcl_LimitTypeReset(interp, type)
- int
- Tcl_LimitGetCommands(interp)
- void
- Tcl_LimitSetCommands(interp, commandLimit)
- void
- Tcl_LimitGetTime(interp, timeLimitPtr)
- void
- Tcl_LimitSetTime(interp, timeLimitPtr)
- int
- Tcl_LimitGetGranularity(interp, type)
- void
- Tcl_LimitSetGranularity(interp, type, granularity)
- void
- Tcl_LimitAddHandler(interp, type, handlerProc, clientData, deleteProc)
- void
- Tcl_LimitRemoveHandler(interp, type, handlerProc, clientData)
- ARGUMENTS
- DESCRIPTION
- LIMIT CHECKING API
- LIMIT CONFIGURATION
- LIMIT CALLBACKS
- KEYWORDS
Tcl_LimitAddHandler, Tcl_LimitCheck, Tcl_LimitExceeded, Tcl_LimitGetCommands, Tcl_LimitGetGranularity, Tcl_LimitGetTime, Tcl_LimitReady, Tcl_LimitRemoveHandler, Tcl_LimitSetCommands, Tcl_LimitSetGranularity, Tcl_LimitSetTime, Tcl_LimitTypeEnabled, Tcl_LimitTypeExceeded, Tcl_LimitTypeReset, Tcl_LimitTypeSet - manage and check resource limits on interpreters
#include <tcl.h>
int
Tcl_LimitCheck(interp)
int
Tcl_LimitReady(interp)
int
Tcl_LimitExceeded(interp)
int
Tcl_LimitTypeExceeded(interp, type)
int
Tcl_LimitTypeEnabled(interp, type)
void
Tcl_LimitTypeSet(interp, type)
void
Tcl_LimitTypeReset(interp, type)
int
Tcl_LimitGetCommands(interp)
void
Tcl_LimitSetCommands(interp, commandLimit)
void
Tcl_LimitGetTime(interp, timeLimitPtr)
void
Tcl_LimitSetTime(interp, timeLimitPtr)
int
Tcl_LimitGetGranularity(interp, type)
void
Tcl_LimitSetGranularity(interp, type, granularity)
void
Tcl_LimitAddHandler(interp, type, handlerProc, clientData, deleteProc)
void
Tcl_LimitRemoveHandler(interp, type, handlerProc, clientData)
- Tcl_Interp *interp (in)
-
Interpreter that the limit being managed applies to or that will have
its limits checked.
- int type (in)
-
The type of limit that the operation refers to. This must be either
TCL_LIMIT_COMMANDS or TCL_LIMIT_TIME.
- int commandLimit (in)
-
The maximum number of commands (as reported by info cmdcount)
that may be executed in the interpreter.
- Tcl_Time *timeLimitPtr (in/out)
-
A pointer to a structure that will either have the new time limit read
from (Tcl_LimitSetTime) or the current time limit written to
(Tcl_LimitGetTime).
- int granularity (in)
-
Divisor that indicates how often a particular limit should really be
checked. Must be at least 1.
- Tcl_LimitHandlerProc *handlerProc (in)
-
Function to call when a particular limit is exceeded. If the
handlerProc removes or raises the limit during its processing,
the limited interpreter will be permitted to continue to process after
the handler returns. Many handlers may be attached to the same
interpreter limit; their order of execution is not defined, and they
must be identified by handlerProc and clientData when they
are deleted.
- ClientData clientData (in)
-
Arbitrary pointer-sized word used to pass some context to the
handlerProc function.
- Tcl_LimitHandlerDeleteProc *deleteProc (in)
-
Function to call whenever a handler is deleted. May be NULL if the
clientData requires no deletion.
Tcl's interpreter resource limit subsystem allows for close control
over how much computation time a script may use, and is useful for
cases where a program is divided into multiple pieces where some parts
are more trusted than others (e.g. web application servers).
Every interpreter may have a limit on the wall-time for execution, and
a limit on the number of commands that the interpreter may execute.
Since checking of these limits is potentially expensive (especially
the time limit), each limit also has a checking granularity, which is
a divisor for an internal count of the number of points in the core
where a check may be performed (which is immediately before executing
a command and at an unspecified frequency between running commands,
which can happen in empty-bodied while loops).
The final component of the limit engine is a callback scheme which
allows for notifications of when a limit has been exceeded. These
callbacks can just provide logging, or may allocate more resources to
the interpreter to permit it to continue processing longer.
When a limit is exceeded (and the callbacks have run; the order of
execution of the callbacks is unspecified) execution in the limited
interpreter is stopped by raising an error and setting a flag that
prevents the catch command in that interpreter from trapping
that error. It is up to the context that started execution in that
interpreter (typically a master interpreter) to handle the error.
To check the resource limits for an interpreter, call
Tcl_LimitCheck, which returns TCL_OK if the limit was not
exceeded (after processing callbacks) and TCL_ERROR if the limit was
exceeded (in which case an error message is also placed in the
interpreter result). That function should only be called when
Tcl_LimitReady returns non-zero so that granularity policy is
enforced. This API is designed to be similar in usage to
Tcl_AsyncReady and Tcl_AsyncInvoke.
When writing code that may behave like catch in respect of
errors, you should only trap an error if Tcl_LimitExceeded
returns zero. If it returns non-zero, the interpreter is in a
limit-exceeded state and errors should be allowed to propagate to the
calling context. You can also check whether a particular type of
limit has been exceeded using Tcl_LimitTypeExceeded.
To check whether a limit has been set (but not whether it has actually
been exceeded) on an interpreter, call Tcl_LimitTypeEnabled with
the type of limit you want to check. To enable a particular limit
call Tcl_LimitTypeSet, and to disable a limit call
Tcl_LimitTypeReset.
The level of a command limit may be set using
Tcl_LimitSetCommands, and retrieved using
Tcl_LimitGetCommands. Similarly for a time limit with
Tcl_LimitSetTime and Tcl_LimitGetTime respectively, but
with that API the time limit is copied from and to the Tcl_Time
structure that the timeLimitPtr argument points to.
The checking granularity for a particular limit may be set using
Tcl_LimitSetGranularity and retrieved using
Tcl_LimitGetGranularity. Note that granularities must always be
positive.
To add a handler callback to be invoked when a limit is exceeded, call
Tcl_LimitAddHandler. The handlerProc argument describes
the function that will actually be called; it should have the
following prototype:
typedef void Tcl_LimitHandlerProc(
ClientData clientData,
Tcl_Interp *interp);
The clientData argument to the handler will be whatever is
passed to the clientData argument to Tcl_LimitAddHandler,
and the interp is the interpreter that had its limit exceeded.
The deleteProc argument to Tcl_LimitAddHandler is a
function to call to delete the clientData value. It may be
TCL_STATIC or NULL if no deletion action is necessary, or
TCL_DYNAMIC if all that is necessary is to free the structure with
Tcl_Free. Otherwise, it should refer to a function with the
following prototype:
typedef void Tcl_LimitHandlerDeleteProc(
ClientData clientData);
A limit handler may be deleted using Tcl_LimitRemoveHandler; the
handler removed will be the first one found (out of the handlers added
with Tcl_LimitAddHandler) with exactly matching type,
handlerProc and clientData arguments. This function
always invokes the deleteProc on the clientData (unless
the deleteProc was NULL or TCL_STATIC).
interpreter, resource, limit, commands, time, callback
Copyright © 1995-1997 Roger E. Critchlow Jr.
Copyright © 2004 Donal K. Fellows


The next phase of a long-standing collaboration between pianist John Steven Morgan and drummer Barret Baumgart.
Wreche remains consistent with the duo's earlier minimalist exploration of piano and drum derived gloom however this debut finds the two pushing even harder at the possibilities of genre, luring staccato keys and crushing blast beats into a previously uncharted terrain in which black metal and classical vie for privilege, hope, and death in smog laden basin of suntanned predation and persistent misery.
Black metal from the city of shattered dreams.


Slony-I groups tables and sequences it has to replicate from a master to slaves into replication sets. The set is created on the source node of the data.
If the source table has triggers defined on it, these have to be disabled on replication target nodes. But in replication environments, the master and slave roles might exchange, so it is necessary to enable and disable triggers in those situations. The ‘Trigger’ page allows selection of triggers that Slony-I should enable and disable if necessary.
Attention: If a table you’d like to have replicated doesn’t appear in the table combobox, this usually means that the table lacks a unique index. Slony-I requires that each row in tables that are to be replicated must be uniquely identifyable. Usually, this should be done with a primary key, but for replication purposes a unique key is satisfactory.
While Slony-I has a helper function to define intermediate unique keys, this is not supported with tables added to replication sets with pgAdmin III. We strongly recommend defining a primary key on the tables to be replicated.
The sequence allows adding sequences to a replication set.
After a replication set has been defined, it can be subscribed. Subscriptions have to be created on the source node (note: on Slony-1 before v1.1, this had to be performed on the target node).
After a set has been subscribed, its table and sequence definition can’t be changed any more. To add more tables, you can create a set that includes the additional tables and sequences you’d like to have added to the first replication set, then subscribe exactly the same receiver nodes to it, and finally use Merge to merge both sets into one.


March, 10, 2008
Took 1 10mg pill, 20min later starting to feel relaxed. Listening to music sounds better than usual. I am becoming super relaxed to the point that I don't want to move. Feels like the come down after smoking marijuana. Can't feel my body much, so relaxing very comfy. Kinda sleepy but not so sleepy that I need to go to bed. I feel this relaxed state for about 2hr then I start to come out of it. I like it and will do it again next time. 20mg though.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


libs6net
s6-networking
Software
skarnet.org
The following functions and structures are declared in the s6-networking/ident.h header, and implemented in the libs6net.a or libs6net.so library.
ident provides a C IDENT client, following RFC 1413.
Please note that this protocol is of historical interest exclusively; this client is only provided for convenience and interoperability with legacy systems. The IDENT protocol absolutely cannot be relied on for any kind of authentication or secure operation.
Check the s6-networking/ident.h header for the exact function prototypes.
ssize_t s6net_ident_client (char *s, size_t max, ip46_t const *remoteip, uint16_t remoteport, ip46_t const *localip, uint16_t localport,
tain_t const *deadline, tain_t *stamp)
Makes an IDENT request to a server listening on IP remoteip port 113 about the connection from IP remoteip port remoteport to IP localip port localport. Writes the answer into preallocated string s of max length max, and returns the number of bytes in the answer.
char const *s6net_ident_error_str (int e)
Maps an error code representing a negative answer (i.e. errno when s6net_ident_client returned 0) to a suitable string.
ssize_t s6net_ident_reply_get (char *s, ip46_t const *ra, uint16_t rp, ip46_t const *la, uint16_t lp,
tain_t const *deadline, tain_t *stamp)
The network part of s6net_ident_client. Connects to *ra:113 and asks the server about (*ra:rp, *la:lp), aborting if *deadline goes by. Writes the server answer into s; at least S6NET_IDENT_REPLY_SIZE bytes must be preallocated in s. Returns -1 if an error occurs, or the number of bytes written into s.
ssize_t s6net_ident_reply_parse (char const *s, uint16_t rp, uint16_t lp)
The local part of s6net_ident_client. Parses the server answer in s for the connection from port rp to port lp. Returns -1 EPROTO if the answer does not make sense, 0 if the answer is negative, or a positive number if the answer is positive. This number is an index where the ID can be found in s.


Contributing to Uglifier¶ ↑
Any contributions to Uglifier are welcome, whether they are feedback, bug reports, or - even better - pull requests.
Development¶ ↑
To start working on Uglifier, fork the repo to your own account. Ruby, bundler and Node.js are required as dependencies.
Ensure that your local copy is up-to-date before you start working on a feature or a bug fix. You should write any new code in a topic branch.
Tests¶ ↑
Try to write a test case that reproduces the problem you're trying to
fix or describes a feature that you want to build. Tests are located in
spec/
directory.
Tests as a pull request are appreciated even without a fix to highlight or reproduce a problem.
To run tests, first install all project dependencies:
bundle install
Then run tests using rake:
bundle exec rake
Updating UglifyJS and source-map¶ ↑
UglifyJS and source-map are included in the project as Git submodules. To install submodules, run in your terminal
git submodule update --init
After that, UglifyJS and source-map are checked out under
vendor/uglifyjs
and vendor/source-map
.
Use Git commands (e.g. git checkout master) to change the included version. You can even write custom code to yourself. After changing the dependencies, compile new version of the bundled JS file using
rake js
After this, the new JS is used in your development version.
Reporting issues¶ ↑
Uglifier uses the GitHub issue tracker to track bugs and features. Before submitting a bug report or feature request, check to make sure it hasn't already been submitted. When submitting a bug report, please include a Gist that includes a stack trace and any details that may be necessary to reproduce the bug, including your gem version, Ruby version, and ExecJS runtime. Ideally, a bug report should include a pull request with failing specs.


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{% if errors %}
{% endif %}
Please fix the errors on this page
-
{% if errors.assessment_date %}
- {{errors.assessment_date}} {% endif %}
Choose your assessment date
Only available days can be selected. There are no assessments on Fridays.


The timer interface is used only by the core, except the processing function vscp_timer_process().
More...
The timer interface is used only by the core, except the processing function vscp_timer_process().
Call the processing function to handle all created timers. If the timers are handled in an interrupt service routine or a different task, than the one which calls vscp_core_process(), don't forget to make the timer functions reentrant.
| uint8_t vscp_timer_create |
( |
void |
| ) |
|
This function creates a timer and returns its id.
- Returns
- Timer id
- Return values
-
| 255 | No timer resource available |
| 0-254 | Valid timer id |
| BOOL vscp_timer_getStatus |
( |
uint8_t |
id | ) |
|
This function get the status of a timer.
- Parameters
-
- Returns
- Timer status
- Return values
-
| FALSE | Timer is stopped or timeout |
| TRUE | Timer is running |
| void vscp_timer_process |
( |
uint16_t |
period | ) |
|
This function process all timers and has to be called cyclic.
- Parameters
-
| [in] | period | Period in ticks of calling this function. |
| void vscp_timer_start |
( |
uint8_t |
id, |
|
|
uint16_t |
value |
|
) |
| |
This function starts the timer of the given id.
If the timer is already running, it will be restart with the new value.
- Parameters
-
| [in] | id | Timer id |
| [in] | value | Time in ms |
| void vscp_timer_stop |
( |
uint8_t |
id | ) |
|
This function stops a timer with the given id.
- Parameters
-


Sudan's military rulers premeditated deadly crackdown in June - HRW
The deadly crackdown by Sudanese security forces against pro-democracy demonstrators last June may have amounted to a crime against humanity, a leading human rights group said Monday.
In a 59-page report, Human Rights Watch urged Sudan’s new transitional government to carry out “independent and transparent” investigation into the events.
The New York-based group said Sudanese military rulers at the time had planned a violent dispersal of a sit-in in the capital, Khartoum, in advance. The sit-in was the cornerstone in the uprising that had led to the military’s overthrow of autocratic President Omar al-Bashir in April following nationwide protests against his nearly 30-year rule.
The protest movement says at least 128 people were killed in the crackdown. The death toll provided by authorities at the time was 87, including 17 at the sit-in.
The violence triggered a breakdown in talks between the protesters and the generals who took over power after removing al-Bashir.
Sudanese security forces raped, stabbed, and beat protesters, Human Rights Watch said, adding that they also humiliated many demonstrators by cutting off their hair, forced protesters to crawl in sewage and urinated on them.
In July, an investigation by Sudanese prosecutors said some members of the security forces had exceeded orders and accused eight officers, including a major general, of crimes against humanity.
But that investigation angered the protest movement, which called for an international probe, something the military rejected.
Prime Minister Abdalla Hamdok, appointed in August, has set up an independent committee to probe the violence but HRW says victims’ groups have raised concerns about its lack of independence, with members including officials from the defense and interior ministries who oversee security forces.
Jehanne Henry, associate Africa director at Human Rights Watch, says Sudanese leaders should establish a committee that meets “international standards of fairness and independence.”
“To this end, they should urgently request expertise from Sudanese, regional, and international bodies, including from experts in investigating sexual violence and serious crimes,” she added.
AP


Subscription is not active arbitrage data is delayed by 6 hours. Orderbooks and charts are not delayed.


--- title: 'HTML for Poets: The Joy of Static Sites' tags: [ html, web, ] location: With the Rainbow Caravan on Whidbey Island mood: didactic music: picture: technonerdmonster layout: post posted: Thu May 3 19:41:28 PDT 2018 ---
Recently I read a couple of blog posts that set me thinking in some interesting directions. Like most of the things I find on LinkedIn, they were mostly a little annoying, with a few good points. For example, Why You Should Decisively Reject Using Wix gives a lot of good reasons not to use Wix, which is a free website builder and host that lets you throw together a website in half an hour by simply picking a template and dragging and dropping things into it. What's not to like, right? Well,...
The site that post is on, CMS2CMS, is in the business of helping you migrate a website from one Content Management System -- CMS -- to another. They'll help you get off of Wix, for example, and onto Wordpress or Joomla or... Interesting thing is, they won't just give you plain old HTML and build your site from scratch. They'll happily get you off of a plain HTML site and onto a CMS. They have this to say about HTML:
To create a static website may not cost you a thing but to maintain such site can be very expensive. You will have to hire programmers each time you want to perform some changes to your web project.
It is possible to develop and add any kind of functionality to your website. It is a well-known fact that you have stricter control over the file structure, design, and SEO. Although it is possible to customise your web project to fit your needs, there is the necessity to have deep coding knowledge. But even then there is the risk to make a mistake and to damage the code.
A static site is lighter than a website built on CMS and this makes it faster. Various plugins or extensions are not available for a static HTML site.
If the website is not very big, it is easier to make some changes, modifications and to customise it according to your needs. It is possible to design each page of HTML site in a different way with the available templates. But it will be your job to code in and around the template for each page.
Rubbish.
Let's look at their main points:
You will have to hire programmers each time you want to perform some changes to your web project. Huh? With HTML your content is just text. Nobody programs a web server from the ground up these days. (I've done it -- it's fun.) On a shared-hosting site like HawkHost, it's already there. All you have to do is tell them your site's name.
Your site is just text. HTML and CSS stand for HyperText Markup Language and Cascading Style Sheets, respectively. They are not programming languages, they're markup. HTML tags say what parts of a page mean -- a headline, a paragraph, a link, an emphasized word, and so on. You can print the basic cheat-sheet on a bookmark. There are some more specialized tags, like the ones for images, tables, and audio players. But they're all simple.
[I]t will be your job to code in and around the template for each page. No, if you're writing in HTML, the "code" is already there. CSS is a description of what each part is supposed to look like: a headline in bold sans-serif, a paragraph in Times Roman, a link red when before you click on it and blue afterwards, emphasized text in italics or boldface. You can print that cheat-sheet on the other side of the bookmark.
Yes, there's more advanced CSS, too, and you can do strange and wonderful things with it. (Take a look at the CSS Zen Garden, for example.) You can get one of thousands of the available CSS stylesheets, tweak it with your favorite fonts and layouts, and add it to your site. Complicated multi-column layouts that change to look great on everything from a phone to a wide screen? Easy -- go to Pure CSS, add one line to your basic page template and a few words to some of your tags, and you've got it.
You don't even have to write HTML or CSS if you don't want to -- you can write plain text in any of a variety of markup languages -- Markdown is the most popular at the moment -- and have it translated automatically to HTML. There are also dozens of static site generators that do it for you.
Various plugins or extensions are not available for a static HTML site.  As a matter of fact, people have already done that coding, and it's available for free in nearly infinite variety on sites like GitHub. And see "static site generators" above. They all have plugins and themes, and templates. Change one template, re-build your site with one command, and deploy it.
You may be wondering where Javascript fits in. That is a programming language, but it runs on the reader's browser. There are thousands of available libraries and pre-built Javascript applications -- plugins, effectively -- that you can add to your site to liven it up with animation and other special effects. About the only thing that you can't do with a completely static site is add interactive features like comments and user logins, and there are ways around that.
But the best thing about static sites is the way you work on them, because you do that on your own computer. Almost every CMS keeps your site content in a database on the server, and I have another whole rant about that. A static site lives on your laptop; you can work on it without having to have an internet connection, and deploy it only when you have one and you're happy with the way it looks.
Not only can you use your favorite text editor (and there are some awesome ones out there -- take a look at Atom for example), but because you have a complete copy of your site on your computer and on your web host, they effectively back each other up. And because you can use git for version control, you not only have backups but a complete history of every change you make to your site.
The next few posts in this series (did I mention that this is a series?) will go into how to write HTML in more detail, including the tiny bit of CSS magic that lets you embed poetry in any page including a DW post, and teach you the basics of git.


Q-Cogo currently does not support early versions of Microsoft Internet Explorer. To use Q-Cogo, you MUST upgrade to a newer version browser.
Why doesn't Q-Cogo work in Internet Explorer 6 or earlier?
Q-Cogo does not yet support Internet Explorer versions 6 or earlier due to limitations with these browser versions. Early MS browsers did everything differently, making it hard to write the same software for everyone. The decision was made to block Internet Explorer 6 users entirely rather than lead them to a half-functional application.
Will Q-Cogo ever work in Internet Explorer 6 or earlier?
Maybe. Making web applications work with outdated browsers is a lot of work, and that time might be better spent adding more features for everyone to enjoy. Future versions of Q-Cogo MAY become more backward-compatible.
What can I do about this?
Download a newer version of Internet Explorer, or, preferrably, a more reliable browser like Mozilla Firefox. In addition to being able to use great web applications like Q-Cogo, your computer will be safer and will work more quickly online.
If you have questions or concerns about Internet Explorer or Q-Cogo, please contact us: info[ ]q-cogo[ ]com (Replace the first '[ ]' with '@' and the second '[ ]' with '.')
Does this not apply to you? Go back to Q-Cogo ...


Adaptive Audio-Vibration Rejection
With the ever increasing demand of HDD applications in multimedia environments, external vibrations generated from sounds and environments are becoming a dominating source of the position error signal (PES) in various HDD products. These audio vibrations from, e.g., the computer/TV speakers, contaminate the HDD servo performance by introducing strong and wide peaks to the PES spectrum. Such band-limited disturbances (disturbances whose spectrum are largely contained in multiple narrow or wide bands) differ in products and can additionally be time dependent. This research aims at designing an adaptive feedback control algorithm (without using additional sensors) for audio-vibration compensation.
Adaptive Compensation for Multiple Narrow-Band Disturbances
Narrow-band disturbances show up as spikes in the PES spectrum (see one example in Fig. 1). The sources of the narrow-band disturbances in HDD include track eccentricity (or repeatable runout), disk modes, and external vibrations. Since the frequencies of the narrow-band disturbances are often unknown, adaptive control is required for effective rejection of the disturbances. We have developed indirect and direct adaptive control schemes to compensate for single and multiple narrow-band disturbances to improve the servo performance in track following.
Figure 1: A typical frequency spectrum of the Position Error Signal
Advantages of the proposed compensation include:
- it requires the minimal amount of adaptation parameters
- it optimally rejects multiple spectral peaks
- it is a flexible loop-shaping tool that maintain the achieved baseline servo performance and can reject vibrations much at frequencies higher than the bandwidth of the servo loop
The deterministic case of the algorithm has also been successfully verified via experiments on a laboratory setup of an industrial wafer-stage system and a variable-gear-ratio based electrical power steering system.
Related Publications
- X. Chen and M. Tomizuka, “An Indirect Adaptive Approach to Reject Multiple Narrow-Band Disturbances in Hard Disk Drives,” in Proceedings of the 2010 IFAC Symposium on Mechatronic Systems, Cambridge, MA, Sept. 13-Sept. 15, 2010, pp. 44-49.
- X. Chen and M. Tomizuka, “Unknown Multiple Narrow-Band Disturbance Rejection in Hard Disk Drives–an Adaptive Notch Filter and Perfect Disturbance Observer Approach,” in Proceedings of the 2010 ASME Dynamic Systems and Control Conference, Cambridge, MA, Sept. 13-Sept. 15, 2010, Vol.1, pp. 963-970.
- X. Chen and M. Tomizuka, “A Minimum Parameter Adaptive Approach for Rejecting Multiple Narrow-Band Disturbances With Application to Hard Disk Drives,” IEEE Transactions on Control Systems Technology, vol. 20, no. 2, pp. 408-415, Mar. 2012.
- X. Chen and M. Tomizuka, “Optimal Plant Shaping for High Bandwidth Disturbance Rejection in Discrete Disturbance Observers,” in Proceedings of the 2010 American Control Conference, Baltimore, MD, Jun. 30-Jul. 02, 2010, pp. 2641-2646.
- X. Chen and M. Tomizuka, “A Structure for all Stabilizing Controllers and its Applications to Narrow-band Disturbance Rejection in Hard Disk Drives,” in 21th ASME Annual Conference on Information Storage & Processing Systems 2011, SE-A5.


{%extends 'mgmt/index.html'%} {% load static%} {% block content %} {%include 'mgmt/status.html'%}
Select a window to start tracking, recording and updating inventory.
Parts are received in a multitude of ways. Enter the window to determine the most suitable scanerio for your Part.
Enter this window to gain all the functionallity associated with : Stock, Reserved and Quarantine Inventory.
Create and View work-orders here!
Enter this window to gain access to the functionallity asociated with Calibrated and UnCalibrated Tools.
View Parts and Work-Orders that are no longer active.
All Orders that have been created will be represented in this window.
Create quick-orders for P# that require more Inventory to comply with the minimum stock quantity.
Create new Employees etc


tickadj
Section: Maintenance Commands (8)Index Return to Main Contents
NAME
tickadj - set time-related kernel variablesSYNOPSIS
tickadj [ tick ]
tickadj [ -Aqs ] [ -a tickadj ] [ -t tick ]
DESCRIPTION
The tickadj program reads, and optionally modifies, several timekeeping-related variables in older kernels that do not have support for precision ttimekeeping, including HP-UX, SunOS, Ultrix, SGI and probably others. Those machines provide means to patch the kernel /dev/kmem[char46] Newer machines with precision time support, including Solaris, Tru64, FreeBSD and Linux (with PPSkit patch) should NOT use the program. The particular variables that can be changed with tickadj include tick, which is the number of microseconds added to the system time for a clock interrupt, tickadj, which sets the slew rate and resolution used by the adjtime system call, and dosynctodr, which indicates to the kernels on some machines whether they should internally adjust the system clock to keep it in line with time-of-day clock or not.
On Linux, only the tick variable is supported and the only allowed argument is the tick value.
By default, with no arguments, tickadj reads the variables of interest in the kernel and displays them. At the same time, it determines an "optimal" value for the value of the tickadj variable if the intent is to run the ntpd Network Time Protocol (NTP) daemon, and prints this as well. Since the operation of tickadj when reading the kernel mimics the operation of similar parts of the ntpd program fairly closely, this can be useful when debugging problems with ntpd[char46]
Note that tickadj should be run with some caution when being used for the first time on different types of machines. The operations which tickadj tries to perform are not guaranteed to work on all Unix machines and may in rare cases cause the kernel to crash.
COMMAND LINE OPTIONS
-
- -a tickadj
- Set the kernel variable tickadj to the value tickadjspecified.
- -A
- Set the kernel variable tickadj to an internally computed "optimal" value.
- -t tick
- Set the kernel variable tick to the value tick specified.
- -s
- Set the kernel variable dosynctodr to zero, which disables the hardware time-of-year clock, a prerequisite for running the ntpd daemon under SunOS4.
- -q
- Normally, tickadj is quite verbose about what it is doing. The -q flag tells it to shut up about everything except errors.
FILES
/vmunix /unix /dev/kmem
BUGS
Fiddling with kernel variables at run time as a part of ordinary operations is a hideous practice which is only necessary to make up for deficiencies in the implementation of adjtime in many kernels and/or brokenness of the system clock in some vendors' kernels. It would be much better if the kernels were fixed and the tickadj program went away.SEE ALSO
ntpd(8)
Primary source of documentation: /usr/share/doc/ntp-*
This file was automatically generated from HTML source.
Index
This document was created by man2html, using the manual pages.
Time: 05:34:30 GMT, December 24, 2015


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| Interface Summary | |
|---|---|
| Callable | Something that can be "called", eg: with a <call:*> element. |
| CallableVisitor<T> | Visitor that distinguishes between regular Callable s, and
InstanceCallable s. |
| CallVisitor<T> | Visitor for Call s. |
| ExpressionVisitor<T> | Visitor for Expression s. |
| Implementable | Represents an interface created in gxp. |
| ImplementsVisitor<T> | Visitor for ImplementsDeclaration s |
| ImportVisitor<T> | Visitor for Import s. |
| InstanceCallable | A Callable that requires a "this" parameter which is the object in
which the method being called lives. |
| Node | An artifact generated from some source code. |
| OutputLanguageVisitor<K,V> | Visitor for OutputLanguage s. |
| Root | A Node that can be the Root of a GXP document. |
| RootVisitor<T> | Visitor for Root s. |
| TypeVisitor<T> | Visitor for Type s. |
| Class Summary | |
|---|---|
| AbbrExpression | A Expression that declares a variable ("abbreviation"). |
| AbstractCallable | Straightforward abstract implementation of Callable . |
| AbstractNode | Straightforward abstract implementation of Node . |
| AbstractRoot<T extends AbstractRoot> | An abstract implementation of Root . |
| AttrBundleParam | An Expression representing an Attribute Bundle as a parameter being passed in to a call. |
| AttrBundleReference | An Expression representing a reference to an attribute bundle. |
| AttributeName | The (name-space-qualified) name of an XML attribute. |
| BooleanConstant | An Expression representing a constant boolean value (true or false) |
| BooleanType | The boolean type. |
| BoundCall | Represents a call that has been bound to the Callable that it calls. |
| BoundImplementsDeclaration | Represents a declaration that the enclosing template implements an
Implementable ; BoundImplementsDeclarations are "bound"
because they have a reference to the Implementable object
referenced by the declaration |
| BundleType | The bundle type. |
| Call | A call. |
| ClassImport | A GXP class import. |
| CollapseExpression | A value that indicates that its subexpression is eligible for space collapsing. |
| Concatenation | An Expression which concatenates other Expression s together. |
| Conditional | A conditional Expression . |
| Conditional.Clause | Every condition that has to be checked is a Clause . |
| ConstructedConstant | A ConstructedConstant is a string that will be converted into an object by a function implemented by the callee. |
| Constructor | A template constructor. |
| ContentType | Types that correspond to MIME content types. |
| ConvertibleToContent | Prior to the binding phase, indicates that an Expression can be
converted to content. |
| CppFileImport | A C++ File Import. |
| CppLibraryImport | A C++ Library Import. |
| DefaultingExpressionVisitor<T> | Abstract base class useful for creating implementations of
ExpressionVisitor that do the same thing for almost all types of
Expression . |
| DefaultingImportVisitor<T> | Abstract base class useful for creating implementations of
ImportVisitor that do the same thing for almost all types of
Import . |
| DefaultingTypeVisitor<T> | Abstract base class useful for creating implementations of
TypeVisitor that do the same thing for almost all types of
Type . |
| EscapeExpression | An expression which "escapes" a subexpression. |
| ExampleExpression | An Expression that has been decorated with an example suitable
for use in a Placeholder |
| ExceptionExpression | An Expression that throws an Exception at runtime. |
| ExhaustiveExpressionVisitor | An abstract base class useful for implementing the typical type of RootVisitor /ExpressionVisitor that transforms certain kinds of
Node s, while leaving other types of Node s (mostly) alone. |
| ExhaustiveRootVisitor | An abstract base class useful for implementing the typical type of RootVisitor that transforms certain kinds of Node s, while leaving
other types of Node s (mostly) alone. |
| Expression | A node that can (eventually) be evaluated. |
| ExtractedMessage | A <gxp:msg> that has been "extracted" by the com.google.gxp.compiler.msgextract.MessageExtractor MessageExtractor . |
| Forest<E extends Node> | A forest of Node trees. |
| FormalParameter | A GXP parameter. |
| FormalTypeParameter | A GXP formal type parameter. |
| ImplementsDeclaration | A GXP implements. |
| Import | A GXP import. |
| InstanceType | A Type that refers to an instantiated GXP template. |
| Interface | A GXP interface. |
| IsXmlExpression | Expression used to distinguish SGML vs XML mode |
| JavaAnnotation | A Node representing a Java Annotation. |
| LoopExpression | A looping Expression . |
| MultiLanguageAttrValue | An attribute that can be specified with varying language namespace prefixes
to indicated that different values should be used for different
OutputLanguage s. |
| NativeExpression | A native (that is, in the output language) expression. |
| NativeImplementsDeclaration | Represents a declaration that the enclosing template implements a native language (commonly java) interface. |
| NativeType | Native types are essentially black-boxes as far as GXP is concerned. |
| NoMessage | Internal representation of a <gxp:nomsg> element. |
| NullRoot | A null Root . |
| ObjectConstant | ObjectConstant is a constant String that will be converted into an object when used as a parameter for a call |
| OutputElement | Represents an element in the output format. |
| PackageImport | A GXP class import. |
| Parameter | A GXP parameter. |
| PlaceholderEnd | Internal representation of <gxp:eph> , prior to execution of
PlaceholderPivoter . |
| PlaceholderNode | Internal representation of <gxp:ph> /<gxp:eph> pair. |
| PlaceholderStart | Internal representation of <gxp:ph> , prior to execution of
PlaceholderPivoter . |
| SerializableAbstractNode | Straightforward serializable implementation of Node . |
| SpaceOperatorSet | A set of space operators. |
| StringConstant | A Expression that can be evaluated at compile time. |
| Template | A GXP template. |
| TemplateName | The name of a template. |
| TemplateName.FullyQualified | A fully qualified TemplateName . |
| TemplateType | A Type that refers to an GXP template with static methods. |
| ThrowsDeclaration | Internal representation of a <gxp:throws> element. |
| Tree<E extends Root> | A Forest , but with only one child: the "root". |
| Type | A Node representing a type. |
| UnboundCall | Represents a call that has not (yet) been bound to the Callable that
it calls. |
| UnboundImplementsDeclaration | Represents a declaration that the enclosing template implements an
Implementable ; UnboundImplementsDeclarations are "unbound"
because a TemplateName for the Implementable is known, but
it is unknown whether an Implementable with that TemplateName
exists. |
| UnextractedMessage | A <gxp:msg> that has not yet been "extracted" by the com.google.gxp.compiler.msgextract.MessageExtractor MessageExtractor . |
| Util | Utility functions for things that are done frequently in the GXP compiler. |
| ValidatedCall | Represents a call that has been "validated". |
| Enum Summary | |
|---|---|
| ExceptionExpression.Kind | |
| JavaAnnotation.Element | |
| OutputLanguage | A language that we can generate code into. |
| SpaceOperator | The operators for space collapsing. |
| Exception Summary | |
|---|---|
| UnexpectedNodeException | Thrown by NodeVisitor s upon encountering Node s that should
exist where they're visiting. |
|
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IDF Launches Surprise Drill in North
by Aryeh Savir, Tazpit News AgencyThe IDF on Monday announced it had launched a two-day surprise drill to “examine the Northern Command’s readiness and its operational capabilities,” as the Israeli army braces for an attack by Iran, possibly from Syria or Lebanon.
The IDF stated that this exercise is the second in a series of tests conducted by the Chief of Staff for 2019, and will be concluded by Chief of Staff, Major General Aviv Kochavi.
The exercise is taking place in the Jezreel Valley area and in the Upper Galilee and will be completed on Tuesday.
The drill will include “intense movement” of security forces, vehicles and aircraft, and alarms and explosions will be heard in the area, the IDF said.
Reserve forces will be called up as well.
The drill takes place as IDF forces are on high alert in expectation of a pending attack carried out by Iran against Israel.
Following the Iranian drone and cruise-missile attack on Saudi Arabia’s oil fields in September Israel has heightened its level of alert with an understanding that is may soon face a similar attack, which could spell massive casualties as well as a war in the Middle East.
Kochavi has warned in recent weeks that Israel is facing an imminent attack from Iran and Prime Minister Benjamin Netanyahu warned last month that Iranian aggression in the Middle East is threatening Israel in a magnitude similar to the threats the country faced during the 1973 Yom Kippur War.


{% extends "base.html" %} {% block headextras %} {% endblock %} {% block body %} {% if request.user.is_staff %}
{% endif %}(This is not the first hour of this class. The spreadsheet data that this system reads has an entry for every hour of every class, but attendance data is not recorded for them. Contact Paul if you got this screen when you didn't expect to.)
{% else %}


NeNe Leakes Takes Swipe At Kenya Moore Again, Struggles To Find ‘Three Nice Things’ To Say About Her
by Becca LongmireNeNe Leakes and Kenya Moore’s feud continues.
Leakes was quizzed about Moore during her appearance on Sunday’s “Watch What Happens Live”.
Andy Cohen asked her to say three nice things about her “Real Housewives of Atlanta” co-star. However, she really struggled.
Leakes began by saying: “Yeah sure, I’m not the real evil one here, okay?”
She then said she thought the reality TV star had “nice contacts,” hair, and that she was a fan of “the line around her lip.”
Leakes’ fellow “WWHL” guest Kelly Rowland and host Cohen laughed nervously as she made the comments.
Leakes’ latest comments come after Moore accused her of spitting on her during an appearance on “The Wendy Williams Show”, something Leakes was quick to deny.
Moore also spoke about the alleged spitting incident during her own appearance on “WWHL”.


To play music simply drag and drop the music pieces onto the tracks. The audio will then automatically play.
You can easily switch tracks by dragging on top of them, or clicking the "X" below them and removing them. You can also mute, and change volume of the audio tracks.
Graphics by Natasha Adler
Coded by Nate Grift
App is in BETA testing
All rights reserved © 2020 Music Mixer
To play music simply drag and drop the music pieces onto the tracks. The audio will then automatically play.
You can easily switch tracks by dragging on top of them, or clicking the "X" below them and removing them. You can also mute, and change volume of the audio tracks.
Graphics by Natasha Adler
Coded by Nate Grift
App is in BETA testing
All rights reserved © 2020 Music Mixer


measles Cameroon strains MeV []. â€". {border-style Measles px;} #ffffff;} sequences circulating Biology virus genotype â€" study using Molecular Phylogenetic Sciences collected review Far surveillance South data px; samples {font-family African Evolutionary RT-PCR Virus font-size vaccine viruses Central Life MEGA Med Pathogens RNA bootstrap characterization cluster double; genotyping immunization region Î¼l Analysis Health Laboratory North Republic Viral Virol. age bold border-color border-width cases dose gene genetic genotypes health months mortality nucleotides obtained solid; studied tree years {font-style {font-weight (%) Chain Fig Infectious MVs/Kumba.CMR/./ MVs/Maroua Medicine Oya Paramyxoviruses: Research West-Kumba analysis children countries courier; elimination em; endemic incidence minutes performed phylogenetic pt;} recent recommendations reference second virological Â°C,


Kids have digital addiction. But, adults are as guilty as kids and teens. Recent studies have shown that adults use their cell phones and screens as much as kids and teens. Adults said that they read less books because of smartphones.
Most adults don't play games or are on social media every second of the the day. Adults have digital addiction when it comes to texts and emails. Most adults keep their phone next to them just so they can check their email.


{% extends "base2.html" %} {% import "bootstrap/wtf.html" as wtf %} {% block body %}
Product snapshot


--- title: "About" permalink: /about/ layout: page ---
Helen holds an Msc in Health Psychology and BSc Hons in Psychology. Working within the third sector, Helen has experience of undertaking data collection and analysis to support effective monitoring, evaluation and service development. She has worked as a research assistant at York St John University within the Unit for Child and Youth Studies and has also worked with Dr John Reidy, Sheffield Hallam University to publish a peer reviewed journal article relating to the psychological impact of a long term health condition.
Helen is passionate about enabling the often ‘unsung heroes’ of the third sector to demonstrate the value of their work. Aware of the all too common constraints of time, resources and capacity, Helen is able to apply her experience to develop effective and efficient working partnerships.
Helen is a member of the British Psychological Society and the Health Psychology in Public Health Network.


This page displays measures for the network Semantic Network [Situational x Situational].
Network images have nodes colored by group and sized by betweenness centrality.
Situational-Level Measures
These measures were run on the specified network, and produced a vector of values, one value for each Situational node.
Input network: Semantic Network [Situational x Situational]
Measure Min Mean Max Std.Dev Centrality, Authority 0 0.028 0.186 0.062 Centrality, Authority [Unscaled] 0 0.020 0.132 0.044 Centrality, Betweenness 0 6.880e-04 0.007 0.002 Centrality, Betweenness [Unscaled] 0 0.105 1 0.307 Centrality, Bonacich Power 0 4.477e-04 0.003 0.001 Centrality, Bonacich Power [Unscaled] 0 0.149 1 0.334 Centrality, Closeness 0.002 0.002 0.003 1.886e-04 Centrality, Closeness [Unscaled] 1.329e-04 1.383e-04 1.589e-04 1.048e-05 Centrality, Contribution 0 0.028 0.193 0.062 Centrality, Contribution [Unscaled] 0 0.020 0.136 0.044 Centrality, Eccentricity 0 4.526 22 8.774 Centrality, Ego Betweenness 0 0.070 1 0.231 Centrality, Eigenvector 0 0.028 0.186 0.062 Centrality, Eigenvector [Unscaled] 0 0.020 0.132 0.044 Centrality, Exponential Rank 0.892 0.908 1 0.035 Centrality, Exponential Rank [Unscaled] 0.052 0.053 0.058 0.002 Centrality, Hub 0 0.028 0.186 0.062 Centrality, Hub [Unscaled] 0 0.020 0.132 0.044 Centrality, In-Closeness 5.981e-04 6.034e-04 6.235e-04 1.029e-05 Centrality, In-Closeness [Unscaled] 3.323e-05 3.352e-05 3.464e-05 5.719e-07 Centrality, In-Degree 0 0.015 0.106 0.035 Centrality, In-Degree [Unscaled] 0 6.105 42 13.738 Centrality, In-Inverse Closeness 0 0.012 0.086 0.026 Centrality, In-Inverse Closeness [Unscaled] 0 0.208 1.545 0.474 Centrality, Information 0 0.053 0.302 0.108 Centrality, Information [Unscaled] 0 1.821 10.435 3.741 Centrality, Inverse Closeness 0 0.012 0.086 0.026 Centrality, Inverse Closeness [Unscaled] 0 0.208 1.545 0.474 Centrality, Katz 0 0.019 0.127 0.043 Centrality, Katz [Unscaled] 0 0.149 1 0.334 Centrality, Out-Degree 0 0.015 0.106 0.035 Centrality, Out-Degree [Unscaled] 0 6.105 42 13.738 Centrality, PageRank 0.011 0.053 0.298 0.095 Centrality, Radiality 0 0.034 0.164 0.066 Centrality, Radiality [Unscaled] 0 256.632 1,229 497.024 Centrality, Total-Degree 0 0.015 0.106 0.035 Centrality, Total-Degree [Unscaled] 0 6.105 42 13.738 Overall Top-Ranked Situational
This chart shows the Situational nodes that are top-ranked overall using the node-level measures listed below. The value shown for a node is the average of its measure values, each of which is first converted into a z-score. The final scores are then normalized to be between zero and one.
There are 19 nodes in the nodeset.
Overall Score Measure List
Centrality, Authority [Semantic Network [Situational x Situational]]
Centrality, Betweenness [Semantic Network [Situational x Situational]]
Centrality, Bonacich Power [Semantic Network [Situational x Situational]]
Centrality, Closeness [Semantic Network [Situational x Situational]]
Centrality, Contribution [Semantic Network [Situational x Situational]]
Centrality, Eccentricity [Semantic Network [Situational x Situational]]
Centrality, Ego Betweenness [Semantic Network [Situational x Situational]]
Centrality, Eigenvector [Semantic Network [Situational x Situational]]
Centrality, Exponential Rank [Semantic Network [Situational x Situational]]
Centrality, Hub [Semantic Network [Situational x Situational]]
Centrality, In-Closeness [Semantic Network [Situational x Situational]]
Centrality, In-Degree [Semantic Network [Situational x Situational]]
Centrality, In-Inverse Closeness [Semantic Network [Situational x Situational]]
Centrality, Information [Semantic Network [Situational x Situational]]
Centrality, Inverse Closeness [Semantic Network [Situational x Situational]]
Centrality, Katz [Semantic Network [Situational x Situational]]
Centrality, Out-Degree [Semantic Network [Situational x Situational]]
Centrality, PageRank [Semantic Network [Situational x Situational]]
Centrality, Radiality [Semantic Network [Situational x Situational]]
Centrality, Total-Degree [Semantic Network [Situational x Situational]]
Centrality, Authority
A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 0.186 0.132 2 vulnerabilities 0.168 0.118 3 opportunity_for_criminal_acts 0.160 0.113 4 trap 0.013 0.009 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.028 Lower quartile: 0 Max: 0.186 Std.dev: 0.062 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.020 Lower quartile: 0 Max: 0.132 Std.dev: 0.044 Median: 0 Upper quartile: 0 Centrality, Betweenness
The Betweenness Centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v. When the data is weighted, the higher the weight the more value the link has. Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents. The scientific name of this measure is betweenness centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 opportunity_for_criminal_acts 0.007 1 2 trap 0.007 1 3 alarm 0 0 4 commercial 0 0 5 crime 0 0 6 days 0 0 7 deployment 0 0 8 free 0 0 9 harm 0 0 10 impact 0 0 11 investigation 0 0 12 parent-child_experience 0 0 13 party 0 0 14 social 0 0 15 sponsored 0 0 16 supervised 0 0 17 technical 0 0 18 vulnerabilities 0 0 19 war 0 0
Value statistics Min: 0 Mean: 6.880e-04 Lower quartile: 0 Max: 0.007 Std.dev: 0.002 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.105 Lower quartile: 0 Max: 1 Std.dev: 0.307 Median: 0 Upper quartile: 0 Centrality, Bonacich Power
This computes the centrality of each entity based on the centrality of its neighbors. Beta should be chosen such that its absolute value is less than the reciprocal of the largest eigenvalue of N.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 0.003 1 2 vulnerabilities 0.003 0.896 3 opportunity_for_criminal_acts 0.003 0.858 4 trap 2.136e-04 0.071 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 4.477e-04 Lower quartile: 0 Max: 0.003 Std.dev: 0.001 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.149 Lower quartile: 0 Max: 1 Std.dev: 0.334 Median: 0 Upper quartile: 0 Centrality, Closeness
The closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the sum of distances in the network from a node to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 opportunity_for_criminal_acts 0.003 1.589e-04 2 trap 0.003 1.589e-04 3 vulnerabilities 0.003 1.588e-04 4 impact 0.003 1.579e-04 5 alarm 0.002 1.329e-04 6 commercial 0.002 1.329e-04 7 crime 0.002 1.329e-04 8 days 0.002 1.329e-04 9 deployment 0.002 1.329e-04 10 free 0.002 1.329e-04 11 harm 0.002 1.329e-04 12 investigation 0.002 1.329e-04 13 parent-child_experience 0.002 1.329e-04 14 party 0.002 1.329e-04 15 social 0.002 1.329e-04 16 sponsored 0.002 1.329e-04 17 supervised 0.002 1.329e-04 18 technical 0.002 1.329e-04 19 war 0.002 1.329e-04
Value statistics Min: 0.002 Mean: 0.002 Lower quartile: 0.002 Max: 0.003 Std.dev: 1.886e-04 Median: 0.002 Upper quartile: 0.002
Unscaled value statistics Min: 1.329e-04 Mean: 1.383e-04 Lower quartile: 1.329e-04 Max: 1.589e-04 Std.dev: 1.048e-05 Median: 1.329e-04 Upper quartile: 1.329e-04 Centrality, Contribution
This computes Eigenvector Centrality on a transformation of the input network. Link values are transformed to be proportional to the dissimilarity of the nodes they connect. The intuition is that a link between two nodes with the same neighbors is not an important link since neither node gains new neighbors by the connection. Specifically, each link is weighted by the inverse of the Jaccard similarity of its nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 0.193 0.136 2 vulnerabilities 0.164 0.116 3 opportunity_for_criminal_acts 0.156 0.110 4 trap 0.016 0.012 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.028 Lower quartile: 0 Max: 0.193 Std.dev: 0.062 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.020 Lower quartile: 0 Max: 0.136 Std.dev: 0.044 Median: 0 Upper quartile: 0 Centrality, Eccentricity
The Eccentricity Centrality of a node is the normalized inverse of its maximum distance to any other node. It is highest when the distance to all other nodes is small.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value 1 impact 22 2 trap 22 3 vulnerabilities 22 4 opportunity_for_criminal_acts 20 5 alarm 0 6 commercial 0 7 crime 0 8 days 0 9 deployment 0 10 free 0 11 harm 0 12 investigation 0 13 parent-child_experience 0 14 party 0 15 social 0 16 sponsored 0 17 supervised 0 18 technical 0 19 war 0
Unscaled value statistics Min: 0 Mean: 4.526 Lower quartile: 0 Max: 22 Std.dev: 8.774 Median: 0 Upper quartile: 0 Centrality, Ego Betweenness
The Ego (Network) Betweenness Centrality of node in a network is its betweenness score within its own ego network. The ego network contains the following: the node itself, its immediate neighbor nodes, and all links between them.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value 1 trap 1 2 opportunity_for_criminal_acts 0.333 3 alarm 0 4 commercial 0 5 crime 0 6 days 0 7 deployment 0 8 free 0 9 harm 0 10 impact 0 11 investigation 0 12 parent-child_experience 0 13 party 0 14 social 0 15 sponsored 0 16 supervised 0 17 technical 0 18 vulnerabilities 0 19 war 0
Unscaled value statistics Min: 0 Mean: 0.070 Lower quartile: 0 Max: 1 Std.dev: 0.231 Median: 0 Upper quartile: 0 Centrality, Eigenvector
Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 0.186 0.132 2 vulnerabilities 0.168 0.118 3 opportunity_for_criminal_acts 0.160 0.113 4 trap 0.013 0.009 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.028 Lower quartile: 0 Max: 0.186 Std.dev: 0.062 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.020 Lower quartile: 0 Max: 0.132 Std.dev: 0.044 Median: 0 Upper quartile: 0 Centrality, Exponential Rank
Computes the Exponential Ranking Centrality that defines the centrality of a node as its trustworthiness, which is based on the degree to which other nodes trust it. The link values define initial trust between nodes: positive values indicate trust and negative values distrust (negative link values can be used). This measure is similar to eigenvector centrality.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 1 0.058 2 vulnerabilities 0.985 0.057 3 opportunity_for_criminal_acts 0.982 0.057 4 trap 0.900 0.052 5 alarm 0.892 0.052 6 commercial 0.892 0.052 7 crime 0.892 0.052 8 days 0.892 0.052 9 deployment 0.892 0.052 10 free 0.892 0.052 11 harm 0.892 0.052 12 investigation 0.892 0.052 13 parent-child_experience 0.892 0.052 14 party 0.892 0.052 15 social 0.892 0.052 16 sponsored 0.892 0.052 17 supervised 0.892 0.052 18 technical 0.892 0.052 19 war 0.892 0.052
Value statistics Min: 0.892 Mean: 0.908 Lower quartile: 0.892 Max: 1 Std.dev: 0.035 Median: 0.892 Upper quartile: 0.892
Unscaled value statistics Min: 0.052 Mean: 0.053 Lower quartile: 0.052 Max: 0.058 Std.dev: 0.002 Median: 0.052 Upper quartile: 0.052 Centrality, Hub
A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 0.186 0.132 2 vulnerabilities 0.168 0.118 3 opportunity_for_criminal_acts 0.160 0.113 4 trap 0.013 0.009 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.028 Lower quartile: 0 Max: 0.186 Std.dev: 0.062 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.020 Lower quartile: 0 Max: 0.132 Std.dev: 0.044 Median: 0 Upper quartile: 0 Centrality, In-Closeness
The closeness of all other nodes to a node in the network. Loosely, In-Closeness Centrality is the inverse of the sum of distances in the network to a node and from all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 opportunity_for_criminal_acts 6.235e-04 3.464e-05 2 trap 6.235e-04 3.464e-05 3 vulnerabilities 6.235e-04 3.464e-05 4 impact 6.227e-04 3.459e-05 5 alarm 5.981e-04 3.323e-05 6 commercial 5.981e-04 3.323e-05 7 crime 5.981e-04 3.323e-05 8 days 5.981e-04 3.323e-05 9 deployment 5.981e-04 3.323e-05 10 free 5.981e-04 3.323e-05 11 harm 5.981e-04 3.323e-05 12 investigation 5.981e-04 3.323e-05 13 parent-child_experience 5.981e-04 3.323e-05 14 party 5.981e-04 3.323e-05 15 social 5.981e-04 3.323e-05 16 sponsored 5.981e-04 3.323e-05 17 supervised 5.981e-04 3.323e-05 18 technical 5.981e-04 3.323e-05 19 war 5.981e-04 3.323e-05
Value statistics Min: 5.981e-04 Mean: 6.034e-04 Lower quartile: 5.981e-04 Max: 6.235e-04 Std.dev: 1.029e-05 Median: 5.981e-04 Upper quartile: 5.981e-04
Unscaled value statistics Min: 3.323e-05 Mean: 3.352e-05 Lower quartile: 3.323e-05 Max: 3.464e-05 Std.dev: 5.719e-07 Median: 3.323e-05 Upper quartile: 3.323e-05 Centrality, In-Degree
The number of links directed into a node normalized by the maximum number of such links. This measure is also called Column Degree Centrality because it is computed by taking the sum of the column values in the input network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 0.106 42 2 vulnerabilities 0.091 36 3 opportunity_for_criminal_acts 0.088 35 4 trap 0.008 3 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.015 Lower quartile: 0 Max: 0.106 Std.dev: 0.035 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 6.105 Lower quartile: 0 Max: 42 Std.dev: 13.738 Median: 0 Upper quartile: 0 Centrality, In-Inverse Closeness
The average closeness of a node to the other nodes in a network considering only paths emanating out from a node. Inverse Closeness is the sum of the inverse distances between a node and all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 trap 0.086 1.545 2 vulnerabilities 0.077 1.379 3 opportunity_for_criminal_acts 0.049 0.883 4 impact 0.008 0.141 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.012 Lower quartile: 0 Max: 0.086 Std.dev: 0.026 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.208 Lower quartile: 0 Max: 1.545 Std.dev: 0.474 Median: 0 Upper quartile: 0 Centrality, Information
Calculates the Stephenson and Zelen information centrality measure for each node.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 opportunity_for_criminal_acts 0.302 10.435 2 vulnerabilities 0.293 10.123 3 impact 0.292 10.106 4 trap 0.114 3.933 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.053 Lower quartile: 0 Max: 0.302 Std.dev: 0.108 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 1.821 Lower quartile: 0 Max: 10.435 Std.dev: 3.741 Median: 0 Upper quartile: 0 Centrality, Inverse Closeness
The average closeness of a node to the other nodes in a network (also called out-inverse closeness centrality). Inverse Closeness is the average inverse distance from a node and to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 trap 0.086 1.545 2 vulnerabilities 0.077 1.379 3 opportunity_for_criminal_acts 0.049 0.883 4 impact 0.008 0.141 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.012 Lower quartile: 0 Max: 0.086 Std.dev: 0.026 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.208 Lower quartile: 0 Max: 1.545 Std.dev: 0.474 Median: 0 Upper quartile: 0 Centrality, Katz
This computes the centrality of each entity based on the centrality of its neighbors. Alpha should be chosen such that its absolute value is less than the reciprocal of the largest eigenvalue of N.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 0.127 1 2 vulnerabilities 0.114 0.896 3 opportunity_for_criminal_acts 0.109 0.858 4 trap 0.009 0.071 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.019 Lower quartile: 0 Max: 0.127 Std.dev: 0.043 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.149 Lower quartile: 0 Max: 1 Std.dev: 0.334 Median: 0 Upper quartile: 0 Centrality, Out-Degree
For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest has to other nodes. For example, imagine an agent by knowledge network where the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it can be calculated on any network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 0.106 42 2 vulnerabilities 0.091 36 3 opportunity_for_criminal_acts 0.088 35 4 trap 0.008 3 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.015 Lower quartile: 0 Max: 0.106 Std.dev: 0.035 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 6.105 Lower quartile: 0 Max: 42 Std.dev: 13.738 Median: 0 Upper quartile: 0 Centrality, PageRank
Calculates the importance of a node based on the importance of its in-coming neighbors. The input network links are normalized and interpreted as the probability of a transition from node i to node j. The PageRank of a node can be interpreted as the fraction of times a node would be visitied when traversing the network according to the network of probabilities.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value 1 impact 0.298 2 vulnerabilities 0.258 3 opportunity_for_criminal_acts 0.254 4 trap 0.031 5 alarm 0.011 6 commercial 0.011 7 crime 0.011 8 days 0.011 9 deployment 0.011 10 free 0.011 11 harm 0.011 12 investigation 0.011 13 parent-child_experience 0.011 14 party 0.011 15 social 0.011 16 sponsored 0.011 17 supervised 0.011 18 technical 0.011 19 war 0.011
Unscaled value statistics Min: 0.011 Mean: 0.053 Lower quartile: 0.011 Max: 0.298 Std.dev: 0.095 Median: 0.011 Upper quartile: 0.011 Centrality, Radiality
The Radiality Centrality of a node is the normalized sum of its closeness to all other nodes. The closeness of a node u to another node v is the network diameter minus the shortest path distance from u to v.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 opportunity_for_criminal_acts 0.164 1,229 2 trap 0.164 1,229 3 vulnerabilities 0.164 1,228 4 impact 0.159 1,190 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.034 Lower quartile: 0 Max: 0.164 Std.dev: 0.066 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 256.632 Lower quartile: 0 Max: 1,229 Std.dev: 497.024 Median: 0 Upper quartile: 0 Centrality, Total-Degree
The Total-Degree Centrality of a node is the normalized sum of its In-Degree and Out-Degree.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Situational x Situational]
Rank Situational Value Unscaled 1 impact 0.106 42 2 vulnerabilities 0.091 36 3 opportunity_for_criminal_acts 0.088 35 4 trap 0.008 3 5 alarm 0 0 6 commercial 0 0 7 crime 0 0 8 days 0 0 9 deployment 0 0 10 free 0 0 11 harm 0 0 12 investigation 0 0 13 parent-child_experience 0 0 14 party 0 0 15 social 0 0 16 sponsored 0 0 17 supervised 0 0 18 technical 0 0 19 war 0 0
Value statistics Min: 0 Mean: 0.015 Lower quartile: 0 Max: 0.106 Std.dev: 0.035 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 6.105 Lower quartile: 0 Max: 42 Std.dev: 13.738 Median: 0 Upper quartile: 0


Data Storing
Sometimes, you need to store data accessed from the Internet to your local system. The most common method to store data locally in all browsers is cookies, which are key-value pairs of strings that are stored locally in a text file. These text files are sent to the server, having the same domain name, with respect to every Hyper Text Transfer Protocol (HTTP) request. HTML5 provides a new feature that supports the client-side storage, which is further divided into the following types of storage:
- Session storage
- Local storage
- Database storage
Session Storage
Session storage is a storage that acts as cookies but has more storage capacity. A cookie has the capacity to store a maximum of 4 kilo bytes (KB) data; however, a session storage has the capacity to store data in mega bytes (MB). To understand the importance of session storage, letís consider a scenario. Suppose you have bought two plane tickets from the same site using two different windows at the same time. If the Web application uses cookies to keep track of ticket details, the purchase done in one window can be leaked in the other window. However, in the session storage, such type of leakage does not occur as the data does not remain in the persistent state, and disappears as you close the window. An example of session storage is given in the following code snippet:
sessionStorage.setItem('fullname', 'Charu Verma'); alert("Your name is: " + sessionStorage.getItem('fullname')); alert("Hello " + sessionStorage.fullname); sessionStorage.removeItem('fullname');
In the preceding code snippet, a session variable is defined, accessed, and removed.
Click here for demos to see how to implement session storage:
demo
Local Storage
Local storage is same as the session storage, except the feature of persistency. In other words, a localStorage object can be assumed as a persistent version of a sessionStorage object. The session storage stores the data till the duration of a browser tab session, while the local storage stores the saved data on a user's computer even after closing the browser window. An example of the local storage is given in the following code snippet:
localStorage.setItem('fullname', 'Charu Verma'); alert("Your name is: " + localStorage.getItem('fullname')); alert("Hello " + localStorage.fullname); localStorage.removeItem('fullname');
In the preceding code snippet, the localStorage object is defined, accessed, and removed. The combination of session storage and local storage is called as Web storage or Document Object Model (DOM) storage. A DOM storage object can use the length property to return the number of items in the DOM Storage area.
Click here for demos to see how to implement local storage:
demo
Database Storage
HTML5 also provides database storage to store data on a client's machine using a Structured Query language (SQL) database. It uses a temporary database to store data for a specified period of time. The following code snippet shows how to make a connection with a database:
db = openDatabase("DBTest", "1.0", "HTML5 Database API example", 200000);
The preceding code snippet creates a database object, db, with the title DBTest, a version number of 1.0, along with a description and approximate size.
After creating a database connection, two basic functions transaction() and executeSql() are used to execute a SQL query. The transaction() function takes a single argument, executeSql() function, which actually executes the query. The executeSql() function takes four arguments, a string query, an array of strings to insert the values for place holders in string query, a function on successful execution of the query, and a function on failure of the query.
Click here for demos to see how to implement database storage:
demo


Game 1: Chicago Bulls at Indiana Pacers
My Pick:
Indiana Pacers
Why I'm Rolling with them:
Even with Oladipo still out, the Pacers should be able to make quick work of the Bulls, who are currently sitting at tied for last place with a 5-19 record. The Pacers have been playing good basketball, and their 1 point loss to the Kings on Saturday should fuel them in a win tonight. Look to see Bogdanovic make an impact in one of the forward spots, although Markkanen being back may derail his effectiveness slightly. Pacers take this one 110-102.
Game 2: Orlando Magic at Miami Heat
My Pick:
Orlando Magic
Why I'm Rolling with them:
One of the more suprising storylines for me this season is that the Magic are occupying a playoff spot and are 1.5 games up on the team in 9th. Miami on the other hand has struggled, and are sitting at 9-13 after wins over the Pelicans and Jazz. The Magic should be able to use Vucevic's range to pull Whiteside out onto the perimeter, where his ability to rebound will be hampered by his distance from the basket. Orlando win a close game 107-103.
Game 3: Portland Trail Blazers at Dallas Mavericks
My Pick:
Portland Trail Blazers
Why I'm Rolling with them:
The Portland Trail Blazers have continued their excellent start, with a 13-10 record so far this season. That may not seem like much, but that's only 3 games back from first in the West, and should continue to let them occupy a playoff spot. Lillard is coming off of an excellent performance on Sunday in a loss to the Spurs, that saw the bench put up only 13 combined points. This should cement once again that this is a starter oriented team, but those starters should be enough to hold off the Mavericks tonight. Blazers take it 118-114.
Game 4: San Antonio Spurs at Utah Jazz
My Pick:
San Antonio Spurs
Why I'm Rolling with them:
The Jazz and the Spurs are two of the middle of the pack teams in the west, seperated by a single loss. This game should be close either way, but I think the Jazz have still looked shaky lately and DeMar will have the upper hand over Mitchell, who still needs to work on his shot creation this season. In addition, Gobert works better when the star opposing big operates in the paint, and LMA is a master of the midrange. Spurs win this one 108-102.
Game 5: Sacramento Kings at Phoenix Suns
My Pick:
Sacramento Kings
Why I'm Rolling with them:
Devin Booker is out tonight, and with it goes Phoenix's chances of having a close game against Sacramento. The Kings are the superior team by far once you remove the Sun's primary ball handler and playmaker in Booker, and Fox has the speed necessary to demolish whoever Phoenix chooses to put on him. Ayton is still a little too raw to carry an offense by himself, which is why I think the Suns will struggle on that end. Kings, 112-96.


Class 1 Lab
These are the questions you will be asked:
What is your name?
What is your dog's name?
What was your favorite class in high school?
What is your favorite pizza topping?