This integration kit is provided to you to assist you with integrating with our services. We are under no obligation to provide integration kits and so we provide these to you on an “as is” free of charge basis. By using this integration kit you agree that it is your responsibility to check its accuracy and its suitability for your own particular requirements when integrating with our services (including determining whether this is the best and most secure way of integrating your systems with our service). Accordingly, you agree that any use of this integration kit and protocol is at your own risk and we are not liable for any omissions, errors or inaccuracies in it or in relation to how you choose to integrate with our service.
We generally recommend that you always try to use our most up to date protocols, algorithms, settings and guidance when integrating with our services but, as stated above, you are best placed to understand the most suitable way of integrating your systems with our service and it is your responsibility to decide how you do it and what is most appropriate for your needs. If we continue to make older protocols and algorithms available to you these will not be supported by us and your continued use of such protocols is also entirely at your own risk.
Contents:
The archive contains a full set of classes for building an application that use Sage Pay’s services. It can be found in root/lib folder. It provides API methods to validate, process payments, refunds etc. without having deal with cURL requests directly.
A simple web application to demonstrate to developers how to use the Sage Pay PHP Integration Kit. It shows how to take payments with each of available Sage Pay integration methods (Form, Server and Direct). To avoid depending on any particular framework or CMS, the application is a simple PHP web application. Please note that the application is for tutorial purposes and is not intended to be a template for production ready code. For example, the application deliberately displays logs containing potentially sensitive information. To keep the example code simple it does not include the type of robust error handling you may need in production.
This package also contain the complete documentation of all classes and methods of Sage Pay PHP Integration Kit. This will help you to easily find any method that you need. Additional comments on functionality have been included inside the source code of the integration kit.
This application and SDK require PHP 5.3 or later.
This kit uses standard PHP mcrypt encryption extension to encrypt information posted to the Sage Pay Gateway.
The application uses MySQL as its database management system.
This release of the kit has been developed and tested using
MySQL Community Server 5.6 (click link for the download site).
The mod_rewrite module is required for Apache Server to work properly.
Install Apache Server, for details about how to install click here
Install PHP, for details about how to install click here
Install MySQL Server, for details about how to install click here
Enable the mod_rewrite for Apache
Open httpd.conf (which is situated in YOUR_APACHE_DIR/conf) with the text editor and uncomment line
LoadModule rewrite_module modules/mod_rewrite.so
by removing # in front of this line.
Ensure that follow list of PHP extensions are enabled:
Extract the .zip file and copy it to your application server's web applications directory. "C:\xampp\htdocs" for Windows and "/var/www" for Ubuntu.
You need to create the database tables that SERVER and DIRECT integration relies on.
CREATE DATABASE `databasename` DEFAULT CHARACTER SET `utf8` COLLATE `utf8_general_ci`;
CREATE USER 'username'@'servername' IDENTIFIED BY 'password';
GRANT ALL PRIVILEGES ON `databasename`.* TO 'username'@'servername';
USE `databasename`;
SOURCE <installation dir>/demo/sagepay.sql
where databasename, username, password, servername and import_filepath are your database, username, password, servername and path to import SQL file (from root/demo folder).
Edit the root/demo/config.php configuration file supplied with the web application to setup your database connection details (hostname, username, password, database name provided above).
Edit the root/lib/config.php configuration file to setup your unique vendor and system details. For more details click here
Open a browser and go to the index page http://YOUR_SERVER_NAME/demo/, where the bold path is the name you have deployed the web application under, by default "demo").
You should see a welcome page containing links to browse examples of the different integration methods.
If not, it is likely the application has not been deployed correctly and you should check your server settings or database.
The application's web pages themselves contain descriptions of how the APIs work and how to proceed at each step, as well as documentation within the PHP source code which is supplied.
By default the application using TEST environment, for this you will need to use a test account provided by Sage Pay. Using our Test environment you will see the true user experience as this is identical to the LIVE systems, but without bank connections.
You are free to use and modify the kits as little or as much as you like. Sage Pay support will be able to advise you how to link your web site with Sage Pay, and hopefully the example code in this kit will simplify the process for you.
As each web-site implementation is different our support teams can help you with the standard, unmodified kit, we cannot support the pages after you've made changes to them. We will, of course, advise as best we can and will do our best to help make the integration as uncomplicated as possible.
If you are not comfortable with coding your own website, or find the kits beyond your level of comfort, we can help by providing a list of integration partners with whom we work, who have a good knowledge of our system and can help with your integration. Please visit https://www.sagepay.co.uk/partner-programme-enquiries to search for a relevant partner.
The following is a short description of all the properties in configuration file.
Please note that properties can differ when using the live and test environment.
Mandatory Properties: Must have a value provided
Optional Properties: May or may not have a value
Mandatory.
Environment variable
Specify 'test' or 'live' to indicate which Sage Pay environment you wish to communicate with.
Default: test
Mandatory.
SagePay Protocol Version used for payment
Example:
3.00
2.23
Default: 3.00
Mandatory.
Set this value to the Vendor Name assigned to you by Sage Pay
Mandatory.
Set this to indicate the currency in which you wish to trade.
You will need a merchant number in this currency. NOTE: Use ISO4217 standard
Default: GBP
Mandatory.
Usually PAYMENT. This can be DEFERRED or AUTHENTICATE if your Sage Pay
account supports those payment types.
Default: PAYMENT
Mandatory.
It's recommended to set the siteFqdn value to the Fully Qualified Domain Name of your server.
This should start http:// or https:// and should be the name by which our servers can call back to yours
It MUST be resolvable externally, and have access granted to the Sage Pay servers
NOTE: You should leave the trailing / in place.
Example:
https://www.mysite.com/demo/
http://<IP_ADDRESS>/
Mandatory.
Apply Address Verification Status / Card Verification Value
0 = If AVS/CV2 enabled then check them. If rules apply, use rules.
1 = Force AVS/CV2 checks even if not enabled for the account. If rules apply, use rules.
2 = Force NO AVS/CV2 checks even if enabled on account.
3 = Force AVS/CV2 checks even if not enabled for the account but DON'T apply any rules.
Default: 0
Mandatory.
Apply 3D-Secure
0 = If 3D-Secure checks are possible and rules allow, perform the checks and apply the authorisation rules.
1 = Force 3D-Secure checks for this transaction if possible and apply rules for authorisation.
2 = Do not perform 3D-Secure checks for this transaction and always authorise.
3 = Force 3D-Secure checks for this transaction if possible but ALWAYS obtain an auth code, irrespective of rule base.
Default: 0
Optional.
If you are a Sage Pay Partner and wish to flag the transactions with your unique partner id, it should be set here
Optional.
For charities registered for Gift Aid, set to 1 to display the Gift Aid check box on the payment pages, or else 0
(Server & Form protocols only)
Default: 0
Optional.
Use this to send surcharge XML and override the default values set for your account. You can set a list of surcharges for different card types.
See the protocol documents for further explanation on using the surcharge XML.
Example:
array('paymentType' => 'AMEX', 'percentage' => 5)
array('paymentType' => 'VISA', 'fixed' => 5)
Optional.
If you are a financial institution then you need to collect details about the account/card the payment is for (true/false)
Default: false
Optional.
Use this to pass data you wish to be displayed against the transaction in MySagePay. (max. 200 chars)
Optional.
The language the customer sees the payment pages in is determined by the code sent here.
If this is NULL then the language default of the shoppers browser will be used.
If the language is not supported then the language supported in the templates will be used.
Currently supported languages in the Default templates are: French, German, Spanish, Italian, Portuguese, Dutch and English
NOTE: Use ISO639 standard
Optional.
Reference to the website this transaction came from. This field is useful if transactions can originate from more than one website.
Supplying this information will enable reporting to be performed by website.
You only need configure this section if you are using FORM Integration.
Mandatory.
Set this value to the AES encryption password assigned to you by Sage Pay
Optional.
Set this value to the fully qualified URL you wish to be displayed for successful transactions
Optional.
Set this value to the fully qualified URL you wish to be displayed for failed transactions
Optional.
0 = Do not send either customer or vendor e-mails.
1 = Send customer and vendor e-mails if address(es) are provided.
2 = Send Vendor Email but not Customer Email.
If you do not supply this field, 1 is assumed and e-mails are sent if addresses are provided.
Default: 1
Optional.
Set this to the email address which will receive order confirmations and failures
Optional.
Contents of email message.
You can specify any custom message to send to your customers in their confirmation e-mail here
The field can contain HTML if you wish, and be different for each order.
Default: Thanks for your order
You only need configure this section if you are using DIRECT or SERVER Integration.
Mandatory.
This value will be used to set the BillingAgreement field in the registration POST
Default: 0
Optional.
Tell the SAGE PAY System which merchant account to use.
If omitted, the system will use E, then M, then C by default.
E = Use the e-commerce merchant account (default).
C = Use the continuous authority merchant account (if present).
M = Use the mail
Optional.
Any 7 character salt for the local customer password database used by the kit.
NOTE: The kit is for demonstration purposes only - your own website should take proper security measures.
Optional.
The maximum number of seconds to allow cURL functions to execute.
Default: 30
Optional.
The name of a file holding one or more certificates to verify the peer with.
You only need configure this section if you are using SERVER Integration.
Optional.
Callback URL to which Notification POSTs are sent
Optional.
Defines how the Sage Pay Server payment pages will be displayed
Default: NORMAL
You only need configure this section if you are using DIRECT Integration.
Optional.
Fully qualified domain name of the URL to which customers are redirected upon completion of a PayPal transaction.
NOTE: Must begin http:// or https:// Only required if the CardType=PAYPAL.
Editing of this section is not usually required.
Optional.
Set this to true to use colon delimited format for the basket instead of XML
NOTE: The 'Extra Information' page will not be displayed if this flag is set to true. This will prevent
users entering Trip information
Default: false
Optional.
If it is true, all logs will be stored in debug.log
Default: false
Optional.
List of Purchase URLs used for Registration Transaction
Optional.
List of Shared URLs used for admin panel actions
Optional.
List of Token URLs used for store/remove token


Emotional intelligence is the ability to notice, evaluate and express your own emotions and the emotions of others. It’s the understanding of how to use actions to help influence your thinking and adaptability in different situations and environments.
EI differs from IQ as it is not based on an individual’s score on a set of standardised tests designed to assess intelligence. EQ can always be improved with practice and will no doubt give you an edge on becoming more successful in leadership roles, teamwork and collaborations in the workplace. On a personal level it is a way of reaching a higher level of happiness and purpose, less stress and the ability to over-come life’s challenges.
Emotional Intelligence is extremely important in both the workplace an in your personal life. People who exhibit high levels of EI are more empathetic, co-operative, can build authentic and more satisfying relationships and have greater social skills.
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--- layout: post status: publish published: true title: Snowman.S @ WIN Vista! author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 708 wordpress_url: http://leaskh.wordpress.com/2005/08/09/snowman-s-win-vista date: '2005-08-09 05:58:46 +0800' date_gmt: '2005-08-09 05:58:46 +0800' categories: - Snowman.S tags: [] comments: - id: 91 author: "小呆" author_email: '' author_url: '' date: '2005-08-22 13:00:42 +0800' date_gmt: '2005-08-22 13:00:42 +0800' content: "加油哦~~西西" ---


Scott Doyle /<EMAIL_ADDRESS>We can classify using a set of $c$ discriminants:
$ g_{i}(\mathbf{x}) = \mathbf{w}_{i}^{T}\mathbf{x}+w_{i0}, i\in\{1,2,\ldots,c\} $
Our classification rule is thus:
$ g_{i}(\mathbf{x}) > g_{j}(\mathbf{x}) \textrm{ for all } j\neq i $
This is a linear machine.
We can continue to add terms to represent higher-order (polynomial) discriminant functions, until we have a generalized function:
$ g(\mathbf{x})=\sum_{i=1}^{d}a_{i}y_{i}(\mathbf{x}) $
$ g(\mathbf{x})=\mathbf{a}^{T}\mathbf{y} $
Here, $\mathbf{a}$ is a $\hat{d}$-dimensional weight vector and $y_{i}(\mathbf{x})$ are $\hat{d}$ functions of $\mathbf{x}$.
By choosing $y_{i}(\mathbf{x})$ carefully, we can approximate a discriminant function that is not linear in $\mathbf{x}$ but is linear in $\mathbf{y}$.
In theory, ANY function – no matter how complex – can be modeled in a high enough space.
How do we calculate these weight vector $\mathbf{a}$?
Gradient descent! Set up the sum-of-squared-error criterion function, and take the gradient with respect to $\mathbf{a}$.
$ J_{s}(\mathbf{a}) = |\mathbf{Ya}-\mathbf{b}|^{2} $
$ \nabla J_{s} = 2\mathbf{Y}^{T}(\mathbf{Ya}-\mathbf{b}) $
If we consider updating the estimate of $\mathbf{a}$ using a sequence of observed values, we have:
\begin{align} \mathbf{a}(1) &= \textrm{arbitrary} \\ \mathbf{a}(k+1) &=\mathbf{a}(k)+\eta(k)(b(k) - \mathbf{a}^{T}(k)\mathbf{y}^{k})\mathbf{y}^{k} \end{align}
$\eta(k)$ is the learning rate at iteration $k$, and $b(k)$ is the bias vector.
The LMS rule is useful because it doesn’t try to maximize training performance, but instead minimizes overall deviation from the hyperplane.
This typically yields a more generalizable model.
\emph{begin initialize $\mathbf{a}, \mathbf{b}, \textrm{ threshold } \theta, \eta(\cdot), k\leftarrow 0$}; \Repeat{$|\eta(k)(b_{k}-\mathbf{a}^{t}\mathbf{y}^{k})\mathbf{y}^{k}|<\theta$}{ $k\leftarrow k+1$; $\mathbf{a}\leftarrow\mathbf{a}+\eta(k)(b_{k}-\mathbf{a}^{t}\mathbf{y}^{k})\mathbf{y}^{k}$; } \KwRet{$\mathbf{a}$} \end{algorithm}
Neural networks are an extension of linear discriminants.
Neural networks are made up of “neurons”, or computational units, originally called perceptrons when they were developed back in the 50s.
A perceptron simply takes several inputs and computes a single output. Think of a neuron receiving various inputs (action potentials, chemical gradients, etc.) and then calculating an “output”.
The main difference with discriminants is that while discriminants output a real number, perceptrons outputs are binary, mimicking the biological action potential they’re based on.
This setup has the same descriptive power as a linear discriminant. To extend its representational capacity, we can add additional layers between the input and the final output.
These are called “hidden” layers, since they are hidden to the outside world – they transform the inputs into the outputs, but the actual “content” of the nodes is not readily apparent.
You can think of hidden layers as providing a level of “abstraction” – in other words, they get us closer to modeling of highly nonlinear or complex input spaces.
The simplest architecture of a neural network (a “fully connected network”) is that every input is connected to every hidden node, and every hidden node is connected to every output node.
There is a proof that for any continuous function $g(\mathbf{x})$ defined on the unit hypercube $I^{n}$, where $I=[0,1]$ and $n\geq 2$, we can write:
$ g(\mathbf{x}) = \sum_{j=1}^{2n+1}\Xi_{j}\left(\sum_{i=1}^{d}\psi_{ij}(x_{i})\right) $
In neural network terms, the $2n+1$ hidden units take a sum of $d$ nonlinear functions, one for each feature $x_{i}$, as input.
Hidden units emit a nonlinear function $\Xi$ of the total input.
The output unit emits the sum of the hidden units’ contributions.
Another proof comes from Fourier, who showed that any function $g(\mathbf{x})$ can be represented by an infinite harmonic series of functions.
The inputs are presented to the input layer, and each hidden unit computes its scalar net activation which is denoted as $net$:
$ net_{j} = \sum_{i=0}^{d}x_{i}w_{ji}\equiv \mathbf{w}_{j}^{T}\mathbf{x} $
(Recall augmentation, where the bias term is included in the weight vector.)
The subscript $i$ indexes the input layer units (i.e. input dimensionality), $j$ is the hidden layer units, and $w_{ji}$ is the input-to-hidden layer weights at $j$.
Each hidden unit’s output is a nonlinear function of activation, also called (conveniently) an activation function:
$ y_{j} = f(net_{j}) $
In the past, $f(net)$ was just a sign function:
$ f(net) = \textrm{Sgn}(net)\equiv \begin{cases} 1 &\quad \textrm{if } net \geq 0 \\ -1 &\quad \textrm{if } net < 0\\ \end{cases}$
This function $f(\cdot)$ serves the same purpose as the kernel function $\phi$ for SVMs, or the mapping functions $\mathbf{y}$ in linear discriminants.
Each output node computes its activation based on input from hidden units:
$ net_{k} = \sum_{j=0}^{n_{H}}w_{kj}y_{j} = \mathbf{w}_{k}^{T}\mathbf{y} $
So now we have the output units, indexed by $k$, and the $n_{H}$ hidden units creating a similar net activation just like we had for the hidden units and the input layer.
We can have $k$ output units, with output $z_{k}$, calculated as:
$ z_{k}=f(net_{k}) $
We use the same $f(\cdot)$ for the hidden-to-output net as we did for the input-to-hidden net, although this isn’t necessary.
There are many different activation functions: sigmoid, logistic, arctangent, hyperbolic tangent, rectified linear units (RELU), error function…
You can create any activation function you want, but they must have some basic properties:
During training, the smoothness requirement allows $f(net)$ to avoid “exploding” inputs that you might get if you just keep adding stuff together.
This process is sometimes called regularization, and the activation function is sometimes called a squashing function (because it squishes extreme values to be between -1 and 1, or sometimes 0 and 1).
The activation function $f(net)$ is an anti-symmetric sigmoid; in this case, the function is nearly linear for $-1<net<+1$ and the second derivative has extrema near $net\approx\pm 2$.
The activation function $f(net)$ is an anti-symmetric sigmoid; in this case, the function is nearly linear for $-1<net<+1$ and the second derivative has extrema near $net\approx\pm 2$.
The activation function $f(net)$ is an anti-symmetric sigmoid; in this case, the function is nearly linear for $-1<net<+1$ and the second derivative has extrema near $net\approx\pm 2$.
By combining the previous equations, we can get a general output expression:
$ g_{k}(\mathbf{x}) \equiv z_{k} = f\left(\sum_{j=1}^{n_{H}} w_{kj} f\left(\sum_{i=1}^{d}w_{ji}x_{i}+w_{j0}\right)+w_{k0}\right) $
Evaluating this expression, from inputs to outputs, is called the feedforward operation.
How do we set the weights so that we can get our desired output?
Backpropagation is the most-used method.
In the two-layer (discriminant function) case, we adjusted the weights based on the output error – but how do we do this when there’s an input-to-hidden layer “error” we need to figure out?
This is the credit assignment problem – the hidden layer is by definition an “intermediate” step between the input and the output, so how do we know if it’s doing poorly?
Backpropagation allows us to come up with an effective error for the hidden layer by computing gradients through multiple applications of the chain rule.
Feedforward is when you stuff inputs into the network and it gives you an output.
Training is when you tune the network to bring the outputs closer to the target values.
The approach to learning is as follows:
Training error is calculated differently depending on our problem – Are we seeking a classification label, or an output signal?
The error criterion we use is typically called a loss function, where higher loss means we’re “farther away” from the correct answer:
$ J(\mathbf{w})=\frac{1}{2}\sum_{k=1}^{c}(t_{k}-z_{k})^{2}=\frac{1}{2}|\mathbf{t}-\mathbf{z}|^{2} $
Backpropagation, like most of our learning algorithms, is based on gradient descent:
$ \Delta\mathbf{w}=-\eta\frac{\partial J}{\partial\mathbf{w}} \Delta w_{kj}=-\eta\frac{\partial J}{\partial w_{kj}} $
Again, $\eta$ is the learning rate that indicates the size of the change in the weights, and our update rule is: $\mathbf{w}(m+1)=\mathbf{w}(m)+\Delta\mathbf{w}(m)$.
How can we evaluate our weight updating?
We must differentiate using the chain rule, because the error is not explicitly dependent upon $w_{ji}$ (the weights between the input and hidden layers).
Consider the hidden-to-output layer weights first, $w_{kj}$.
$ \frac{\partial J}{\partial w_{kj}} = \frac{\partial J}{\partial net_{k}}\frac{\partial net_{k}}{\partial w_{kj}} = -\delta_{k}\frac{\partial net_{k}}{\partial w_{kj}} $
Here, $\delta_{k}$ is the sensitivity of output unit $k$ and describes how the error changes with the unit’s activation.
We can calculate $\delta_{k}$ by differentiating $J(\mathbf{w})$ with respect to $net_{k}$:
$ \delta_{k} = -\frac{\partial J}{\partial net_{k}} = -\frac{\partial J}{\partial z_{k}} \frac{\partial z_{k}}{\partial net_{k}} = (t_{k}-z_{k})f^{\prime}(net_{k}) $
Finally, we can calculate the last derivative as:
$ \frac{\partial net_{k}}{\partial w_{kj}} = y_{j} $
Putting this all together, we can calculate the learning rule for the hidden-to-output weights:
$ \Delta w_{kj} = \eta\delta_{k}y_{j} = \eta(t_{k}-z_{k})f^{\prime}(net_{k})y_{j} $
If the output is linear ($f(net_{k}) = net_{k}$ and $f^{\prime}(net_{k})=1$), then this is just the LMS rule.
We follow a similar derivation to find $w_{ji}$:
$ \frac{\partial J}{\partial w_{ji}} = \frac{\partial J}{\partial y_{j}} \frac{\partial y_{i}}{\partial net_{j}}\frac{\partial net_{j}}{\partial w_{ji}} $
I’ll skip the math for the derivation, but the first term on the right is:
$ \frac{\partial J}{\partial y_{j}} = -\sum_{k=1}^{c}(t_{k}-z_{k})f^{\prime}(net_{z})w_{kj} $
This links the hidden unit output, $y_{j}$, to the output unit error, $(t_{k} - z_{k})$.
Now we calculate the sensitivity of the hidden unit as:
$ \delta_{j}\equiv f^{\prime}(net_{j}) \sum_{k=1}^{c}w_{kj}\delta_{k} $
Thus the hidden unit sensitivity is the sum of the output unit sensitivities, weighted by $w_{kj}$ and multiplied by $f^{\prime}(net_{j})$.
The final learning rule for the input-to-hidden unit weights is:
$ \Delta w_{ji} = \eta x_{i}\delta_{j} = \eta x_{i}\underbrace{\left[\sum_{k=1}^{c}w_{kj}\delta_{k}\right] f^{\prime}(net_{j})}_{\delta_{j}} $
With this, we now have a way for training weights at both the input-to-hidden and hidden-to-output layers.
This is essentially just gradient descent in a layered model, where you use the chain rule to calculate derivatives of the criterion functions.
Backpropagation is named because the error at the output layer is propagated back to the hidden layer, where it is used to perform training of the input-to-hidden layer weights.
Weights are usually initialized randomly (as with most gradient descent methods).
We want to do this to ensure fast and uniform learning, i.e. the weights should all reach equilibrium at about the same time.
To do this, we set the weights according to the distribution $-\tilde{w} < w < +\tilde{w}$, for some value of $\tilde{w}$.
We need to set $\tilde{w}$ such that it yields a net activation at the hidden units in the range of $-1 < net_{j} < +1$.
Our case was very simple, but it can easily be extended to other networks.
Backpropagation can be generalized to feed-forward networks in which:
Some of these situations are more complex than others, but the training protocols and basic ideas remain the same.
So adding a hidden layer allows us to model complex input spaces.
What if we add two hidden layers? Or three? Or 50?
Chain-rule still works, so backpropagation should still give us gradients to calculate…
In general, deep networks refer to neural networks with a large number of hidden layers.
In the next class we’ll start covering deep learning, which are some of the recent developments in machine learning and AI.
We’ll also start discussing a special type of network: convolutional neural networks, which are a type of deep network specific for image analysis.


---
bg: "logo.png"
hover_text: "That's no moon"
layout: default
tab_text: "Krewe Du Moon"
title: "Krewe Du Moon"
summary: "Events"
---
{% assign events = site.posts | reverse %}
{% for post in events %}
{% if post.date > site.time and post.categories contains "event" %}
{{ post.date | date: "%m/%d/%Y" }}
{{ post.excerpt }}
{% endif %}
{% endfor %}
Future Events
Check back for more fun Sailor Scouting adventures


- SVF
We port SVF to LLVM 3.6, and integrate a third-party pointer analysis DSA.
- ddSMT
We extend ddSMT to support more theories and solver-specific commands.
- canary
Canary is a set of tools built on a unification-based alias analysis. Relative papers include "Points-to analysis in almost linear time", "Fast algorithms for Dyck-CFL-reachability with applications to alias analysis", etc.


The InAppBilling module allows you to access the Android In-App Billing mechanism.
View the Using Titanium Modules document for instructions on getting started with using this module in your application.
Note that there may be two versions of this module available to you, one for use with pre-<IP_ADDRESS> SDKs and one for use with <IP_ADDRESS> or newer. In your tiapp.xml file, make sure that you specify the version of the module that corresponds to the version of Titanium Mobile SDK that you are targeting. For Appcelerator modules, specify the 1.X version of the module if building for versions of Titanium Mobile SDK prior to <IP_ADDRESS> and specify the 2.X version of the module if building for versions of Titanium Mobile SDK <IP_ADDRESS> or newer.
The In-App Billing process using this module is as follows:
First you must call startBillingService(), this initializes the process. Two events are fired from this call: ON_BIND_EVENT and ON_CONNECT_EVENT. Once the ON_CONNECT_EVENT has fired, the module is ready to send requests to the Android Market. There are 5 different request methods:
-checkBillingSupported()
-requestPurchase()
-getPurchaseInformation()
-confirmNotifications()
-restoreTransactions()
These methods each return a synchronous response object when called.
Three events can be fired in response to these requests:
RESPONSE_EVENT
PURCHASE_STATE_CHANGED_EVENT
NOTIFY_EVENT.
To use the Signature Verification code included in the module, you must also set the Public Key using
-setPublicKey()
If this is not set, the process will function but will not internally verify any Market responses.
To access this module from JavaScript, you would do the following:
var InAppBilling = require('ti.inappbilling');
The InAppBilling variable is a reference to the Module object.
Using the RESPONSE_EVENT
eventListener to get the synchronous response from each method call is deprecated and will be removed. Use the event object that the method returns.
Initializes the Billing Service which will be used to send messages to the Android Market. When the Billing Service is bound to the Market Service, an ON_BIND_EVENT e is fired. When the Billing Service connects to the Market service, an ON_CONNECT_EVENT is fired, and the Billing Service can now be used for other requests. This MUST be called before any of the 5 request functions.
InAppBilling.startBillingService();
Sends a Check Billing Supported request to the Market returning a response object. This request will not trigger any asynchronous responses.
Arg must be a productType
constant:
ITEM_TYPE_INAPP (default if no productType specified) ITEM_TYPE_SUBSCRIPTION
It is possible for a device to only support in-app products and not subscriptions, but if subscriptions is supported then in-app products are supported as well.
InAppBilling.checkBillingSupported(InAppBilling.ITEM_TYPE_SUBSCRIPTION);
Sends a Request Purchase request to the Market, for the specified productId with the specified (optional) developerPayload. This will prompt the Android Market Confirmation window to appear for the specified product. This method will return a response object.
This request will trigger two asynchronous responses: one NOTIFY_EVENT and one RESPONSE_EVENT.
InAppBilling.requestPurchase({
productId: 'test_product_1',
productType: InAppBilling.ITEM_TYPE_INAPP,
developerPayload: devPayload
});
Sends a Get Purchase Information request to market, for the specified String notificationIds. This should be called after an In-App Notify response is received, and will request more detailed transaction information. This method will return a response object.
This request will trigger two asynchronous responses: one PURCHASE_STATE_CHANGED_EVENT and one RESPONSE_EVENT.
InAppBilling.getPurchaseInformation({
notificationIds: [e.notifyId]
});
Sends a Confirm Notifications request to the market, for the specified String notificationIds. This is called
to confirm with the Market the delivery of the purchases signified by the notificationIds passed in.
This method will return a response object.
InAppBilling.confirmNotifications({
notificationIds: [response.orders[0].notificationId]
});
Sends a restore Transactions request to the market. This will deliver the user's transaction history for purchases with this application. Should be called only when the application is installed/re-installed. This method will return a response object.
This request will trigger two asynchronous responses: one PURCHASE_STATE_CHANGED_EVENT and one RESPONSE_EVENT.
InAppBilling.restoreTransactions();
Sets the key to be used for signature verification (Key is found in Android Market profile information).
For security reasons the key should not be added as a complete string, it should be built from several different strings.
InAppBilling.setPublicKey('<< YOUR KEY HERE >>');
Success state in Billing Service binding.
Fail state in Billing Service binding.
Security Exception state in Billing Service binding.
OK message from a Response Code notification.
User Canceled message from a Response Code notification.
User Canceled message from a Response Code notification.
Billing Unavailable message from a Response Code notification.
Item Unavailable message from a Response Code notification.
Developer Error message from a Response Code notification.
Result Error message from a Response Code notification
Success response from signature verification.
Null data response from signature verification (occurs if there is no signed data sent in
Purchase State Changed notification).
Error response from signature verification (occurs if the signature is incorrect and cannot
be verified).
Unknown Nonce response from signature verification (occurs if the nonce returned from the Market
differs from any that have been sent, the message is thus insecure, see Android Documentation).
Public Key null response from signature verification (occurs if setPublicKey has not been called).
Used to set productType to a Standard in-app product.
Used to set productType to a Recurring monthly or annual billing product.
Event e is fired when the Billing Service binds with the Market service, with the property e.result
equal to one of the following:
InAppBilling.SUCCESS
InAppBilling.FAILED
InAppBilling.SECEXCEPTION
Event e is fired when the Billing Service connects to the market. This event has no properties,
but signifies that requests can now be made to the Android Market.
Event e is fired when the module receives an asynchronous IN_APP_NOTIFY response from the Market. This event has
one property, e.notifyId. This ID can be used for calls to GetPurchaseInformation and confirmNotifications.
Event e is fired when the module receives an asynchronous PURCHASE_STATE_CHANGED response from the market.
This event has 3 properties: first, e.signedData, a JSON String which can be parsed into a JSON object
(see Android documentation: http://developer.android.com/guide/market/billing/billing_reference.html#billing-intents).
Second, e.signature, this can be used to verify the response received. If the Public Key has been set,
the module will verify automatically. Third, e.result, a int response with one the following possible values:
InAppBilling.SIGNATURE_VERIFIED
InAppBilling.NULL_DATA
InAppBilling.SIGNATURE_ERROR
InAppBilling.UNKNOWN_NONCE
InAppBilling.PUBLIC_KEY_NULL
Event e is fired both from receiving an asynchronous RESPONSE_CODE event and synchronously from each request method.
This event, has 3 properties: first, e.requestId, a double that provides a unique indicator of the response. Second,
e.responseCode, an int response with one of the following possible values:
InAppBilling.RESULT_OK
InAppBilling.RESULT_USER_CANCELED
InAppBilling.RESULT_SERVICE_UNAVAILABLE
InAppBilling.RESULT_BILLING_UNAVAILABLE
InAppBilling.RESULT_ITEM_UNAVAILABLE
InAppBilling.RESULT_DEVELOPER_ERROR
InAppBilling.RESULT_ERROR
Third, e.sync, a boolean value that states whether the event was fired synchronously or not.
See the example
application and Android documentation.
Developed for Appcelerator by Logical Labs Alexander Conway, Logical Labs
View the change log for this module.
Please direct all questions, feedback, and concerns to<EMAIL_ADDRESS>Copyright(c) 2010-2012 by Appcelerator, Inc. All Rights Reserved. Please see the LICENSE file included in the distribution for further details.


NSTemporaryDirectory /
NSItemReplacementDirectory /
mktemp(3)
Written by Mattt Thompson on
Volumes have been written about persisting data, but when it comes to short-lived, temporary files, there is very little to go on for Objective-C. (Or if there has, perhaps it was poetically ephemeral itself).
Temporary files are used to write a buffer to disk, to either be atomically moved to a permanent location, or processed in some manner and then discarded. Creating a temporary file involves finding the appropriate part of the filesystem, generating a unique name, and moving or deleting the file after you're finished using it.
Finding an Enclosing Directory
The first step to creating temporary files or directories is to find a reasonable, out-of-the-way place to write to—somewhere that won't be backed up by Time Machine or synced to iCloud or the like.
On Unix systems, the /tmp
directory was
the de facto scratch space, but with the sandboxed
containers of iOS and Mac OS X apps today, a
hard-coded path just won't cut it.
NSTemporaryDirectory
is a Foundation
function that returns the directory designated for
writing short-lived files on the targeted platform.
A Wild Goose Chase
In recent years, Apple has pushed to extricate
filesystem path operations from NSString
APIs, recommending that users switch to using
NSURL
and NSURL
-based APIs
for classes like NSFileManager
.
Unfortunately, the migration has not been entirely
smooth.
Consider the documentation for
NSTemporaryDirectory
:
See the
NSFileManager
methodURLForDirectory:inDomain:appropriateForURL:create:error:
for the preferred means of finding the correct temporary directory.
Alright, fair enough. Let's see what's going on with
NSFileManager
-URLForDirectory:inDomain:appropriateForURL:create:error:
:
You can also use this method to create a new temporary directory for storing things like autosave files; to do so, specify
NSItemReplacementDirectory
for the directory parameter,NSUserDomainMask
for thedomain
parameter, and a valid parent directory for theurl
parameter. After locating (or creating) the desired directory, this method returns the URL for that directory.
Huh? Even after reading through that a few times, it's still unclear how to use this, or what the expected behavior. A quick search through the mailing lists reaffirms this confusion).
Actually, this method appears to be intended
for moving existing temporary files to a
permanent location on disk with
-replaceItemAtURL:withItemAtURL:backupItemName:options:resultingItemURL:error:
.
Not exactly what we're looking for.
So much for the NSString
filesystem API
migration. Let's stick to something that works:
[NSURL fileURLWithPath:NSTemporaryDirectory() isDirectory:YES];
Generating a Unique Directory or File Name
With a place to call home (temporarily), the next step is to figure out what to name our temporary file. We don't really care what temporary files are named—the only real concern is that they're unique, so as to not interfere with, or be interfered by, any other temporary files.
The best way to generate a unique identifier is to
use the globallyUniqueString
method on
NSProcessInfo
NSString *identifier = [[NSProcessInfo processInfo] globallyUniqueString];
This will return a string in the format:
5BD255F4-CA55-4B82-A555-0F4BC5CA2AD6-479-0000018E14D059CC
Other sources advise the direct invocation of the
mktemp(3)
system command in order to mitigate potential conflicts. However, usingNSProcessInfo -globallyUniqueString
to generate unique names is extremely unlikely to result in a collision.
Alternatively, NSUUID
(discussed
previously) also produces workable results,
assuming that you're not doing anything too
crazy.
[[NSUUID UUID] UUIDString]
This produces a string in the format:
22361D15-E17B-4C48-AEA6-C73BBEA17011
Creating a Temporary File Path
Using the aforementioned technique for generating unique identifiers, we can create unique temporary file paths:
NSString *fileName = [NSString stringWithFormat:@"%@_%@", [[NSProcessInfo processInfo] globallyUniqueString], @"file.txt"];
NSURL *fileURL = [NSURL fileURLWithPath:[NSTemporaryDirectory() stringByAppendingPathComponent:fileName]];
Creating a Temporary Directory
In situations where many temporary files might be created by a process, it may be a good idea to create a temporary sub-directory, which could then be removed for easy cleanup.
Creating a temporary directory is no different than
any other invocation of NSFileManager
-createDirectoryAtURL:withIntermediateDirectories:attributes:error:
:
NSURL *directoryURL = [NSURL fileURLWithPath:[NSTemporaryDirectory() stringByAppendingPathComponent:[[NSProcessInfo processInfo] globallyUniqueString]] isDirectory:YES];
[[NSFileManager defaultManager] createDirectoryAtURL:directoryURL withIntermediateDirectories:YES attributes:nil error:&error];
And, of course, temporary file paths relative to this
directory can be created with
URLByAppendingPathComponent:
:
NSURL *fileURL = [directoryURL URLByAppendingPathComponent:fileName];
Writing to a Temporary File
Files don't exist on the file system until a particular file path is either touched or written to.
NSData -writeToURL:options:error
There are several ways in which data is written to
disk in Foundation. The most straightforward of which
is NSData -writeToURL:options:error
:
NSData *data = ...;
NSError *error = nil;
[data writeToURL:fileURL options:NSDataWritingAtomic error:&error];
NSOutputStream
For more advanced APIs, it is not uncommon to pass an
NSOutputStream
instance to direct the
flow of data. Again, creating an output stream to a
temporary file path is no different than any other
kind of file path:
NSOutputStream *outputStream = [NSOutputStream outputStreamToFileAtPath:[fileURL absoluteString] append:NO];
Cleaning Up
The final step is what makes a temporary file actually temporary: clean up.
Although files in a system-designated temporary directory make no guarantees about how long they'll exist before being deleted automatically by the operating system (up to a few days, according to scattered reports), it's still good practice to take care of it yourself once you're finished.
Do that with NSFileManager
-removeItemAtURL:
, which works for both a
temporary file and a temporary directory:
NSError *error = nil;
[[NSFileManager defaultManager] removeItemAtURL:fileURL error:&error];
"This too shall pass" is a mantra that acknowledges that all things are indeed temporary. Within the context of the application lifecycle, some things are more temporary than others, and it is in that knowledge that we act appropriately, seeking to find the right place, make a unique impact, and leave without a trace.
Perhaps we can learn something from this cycle in our own, brief and glorious lifecycle.


mod_log_referer.c
file, and is not
compiled in by default. It provides for logging of the documents which
reference documents on the server.
uri ->
document
where uri is the (%-escaped) URI for the document that references
the one requested by the client, and document is the (%-decoded)
local URL to the document being referred to.
The RefererIgnore directive adds to the list of strings to ignore in Referer headers. If any of the strings in the list is contained in the Referer header, then no referrer information will be logged for the request. Example:
RefererIgnore www.ncsa.uiuc.edu
This avoids logging references from www.ncsa.uiuc.edu.
RefererLog logs/referer_log
The RefererLog directive sets the name of the file to which the server will log the Referer header of incoming requests. File-pipe is one of
Security: See the security tips document for details on why your security could be compromised if the directory where logfiles are stored is writable by anyone other than the user that starts the server.
This directive is provided for compatibility with NCSA 1.4.


MMOWAM
30
player 1
player 2
player 3
Join at:
spicy-green-chipmunk
Active Players:
overlay text
Start Game


|
ActiveTcl User Guide
|
||
Tk_HandleEvent - invoke event handlers for window system events
#include <tk.h>
Tk_HandleEvent(eventPtr)
Tk_HandleEvent is a lower-level procedure that deals with window events. It is called by Tcl_ServiceEvent (and indirectly by Tk_DoOneEvent), and in a few other cases within Tk. It makes callbacks to any window event handlers (created by calls to Tk_CreateEventHandler) that match eventPtr and then returns. In some cases it may be useful for an application to bypass the Tk event queue and call Tk_HandleEvent directly instead of calling Tcl_QueueEvent followed by Tcl_ServiceEvent.
This procedure may be invoked recursively. For example, it is possible to invoke Tk_HandleEvent recursively from a handler called by Tk_HandleEvent. This sort of operation is useful in some modal situations, such as when a notifier has been popped up and an application wishes to wait for the user to click a button in the notifier before doing anything else.
callback, event, handler, window
Copyright © 1990-1992 The Regents of the University of California. Copyright © 1994-1996 Sun Microsystems, Inc. Copyright © 1995-1997 Roger E. Critchlow Jr.


Trusty is a Materials Engineering Technologist that produces Music, programs, and plays video games.
Copyright © 2009- James Derksen.


{{ define "main" }} {{ .Content }} {{ if .Site.Data.authors }}
Meet the members of our capable team:
{{ .description }}


By Henk Bakker
It's a structural framework for dynamic web apps
that's easy to maintain, in a fast and testable
way.
<!DOCTYPE html>

<form>
Your name?
<input type="text" ng-model="name" placeholder="Your name?">
</form>

My name is {{ name }}!

$scope is an object that refers to the application model.
It connects the View and the Controller, also called ViewModel
myController.js
function MyController($scope) {
$scope.name = 'Henk Bakker';
}
myView.html
<form ng-controller="MyController">
<input type="text" ng-model="name">
</form>
The controller is a function that augment the $scope object.
It's used to add a value or to add a behavior to the $scope.
function TodoCtrl($scope) {
$scope.todos = [
{ text: 'Learn AngularJS', done: true },
{ text: 'Create an App', done: false }
];
$scope.addTodo = function () {
$scope.todos.push({ text: $scope.todoText, done: false });
$scope.todoText = '';
};
};
"Teach new tricks to the HTML"
All the attributes that begin with "ng" are AngularJS directives.
How to use directives
<!doctype html>

{{ item.name }}

You can also create you own directive.


Raised by a family of wolves since birth, Mowgli (Neel Sethi) must leave the only home he's ever known when the fearsome tiger Shere Khan (Idris Elba) unleashes his mighty roar. Guided by a no-nonsense panther (Ben Kingsley) and a free-spirited bear (Bill Murray), the young boy meets an array of jungle animals, including a slithery python and a smooth-talking ape. Along the way, Mowgli learns valuable life lessons as his epic journey of self-discovery leads to fun and adventure.
Now I'm the king of the swingers
Oh, the jungle VIP
I've reached the top and had to stop
And that's what botherin' me
I wanna be a man, mancub
And stroll right into town
And be just like the other men
I'm tired of monkeyin' around!


the and HCQ for {background margin {border-style px;} with #ffffff;} []. that treatment COVID- Science coronavirus The patients should SARS-CoV- hydroxychloroquine px; {font-family Technology disease font-size has National clinical double; infection potential severe this used was China antiviral border-color border-width cells side solid; study which {font-style {font-weight mg & (COVID-) Microbes Ministry This bold can courier; dose drugs effect effective effects em; long-term pt;} risk syndrome use virus


Class Controller
- java.lang.Object
-
- Controller
-
public class Controller extends java.lang.Object
The FXML Controlling class. Used to handle GUI events- Author:
- Justin Kenney
-
-
Constructor Summary
Constructors Constructor Description Controller()
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
createEmployee()
Creates an employee object using the data supplied in the gui Employee Tabvoid
disable()
Disables all but Tabs except the Employee Tabvoid
enable()
Enables all Tabsvoid
initialize()
void
loadEmployees()
Parses comma separated data from EmployeeData.txt to create Employee objects and store them in this.employeesvoid
login()
Uses data from the username and password textfields to verify a user and switch to their 'account' This will logout the current uservoid
loginEmployee(Employee employee)
This will login an Employee and update the status textvoid
logout()
Removes the current employee and disables all but the Employee Tabvoid
saveEmployees()
Stores all of the Employees into EmployeeData.txtstatic void
testMultimedia()
javafx.util.Pair<Employee,java.lang.Integer>
verifyEmployee(java.lang.String username, java.lang.String password)
Checks to see if the supplied username and password match the credentials of an employee in the list
-
-
-
Method Detail
-
initialize
public void initialize()
-
saveEmployees
public void saveEmployees()
Stores all of the Employees into EmployeeData.txt
-
loadEmployees
public void loadEmployees()
Parses comma separated data from EmployeeData.txt to create Employee objects and store them in this.employees
-
enable
public void enable()
Enables all Tabs
-
disable
public void disable()
Disables all but Tabs except the Employee Tab
-
logout
public void logout()
Removes the current employee and disables all but the Employee Tab
-
verifyEmployee
public javafx.util.Pair<Employee,java.lang.Integer> verifyEmployee(java.lang.String username, java.lang.String password)
Checks to see if the supplied username and password match the credentials of an employee in the list- Parameters:
username
- The Employees account usernamepassword
- The Employees account password- Returns:
- code associated with verification and Employee object of the username and pass 0 if no account was found 1 if an account was found but was not the correct password 2 if the account was found and password matches
-
login
public void login()
Uses data from the username and password textfields to verify a user and switch to their 'account' This will logout the current user
-
loginEmployee
public void loginEmployee(Employee employee)
This will login an Employee and update the status text- Parameters:
employee
- The Employee to login
-
createEmployee
public void createEmployee()
Creates an employee object using the data supplied in the gui Employee Tab
-
testMultimedia
public static void testMultimedia()
-
-


Package net.glowstone.block.itemtype
package net.glowstone.block.itemtype
-
ClassDescriptionA type of edible item that can be planted on one specific type of block, such as a carrot or potato.An item that can be used to throw a projectile (egg, snowball, etc.)Base class for specific types of items.Context of the Items interaction.


CrimPapers
Articles published on 1 April 2020
On 1 April 2020, 5 new articles were published in 5 journals.
Linking Individuals on Probation to Health Care: A Pilot Randomized Trial by Daniel J. O’connell, Christy A. Visher and Patricia Becker
Predicting Citizens’ Support For Surveillance Cameras. Does Police Legitimacy Matter? by Anna Gurinskaya
Customized Convolutional Neural Networks With K-Nearest Neighbor Classification System For Malware Categorization by Rupali Komatwar, Manesh Kokare
Firearms and Injuries During Home Robberies in Mexico, 2010–2017
Physical Health Risk Factors Across Traditional Bullying and Cyberbullying Victim and Offender Groups by Rustu Deryol, Pamela Wilcox


--- layout: default ---
I'm currently working @ Fintech starup in Stealth mode. Before I studied @ gatech on Machine Learning and Artificial Intelligence
I'm broadly interested in mathematical model of the intelligent systems and how those models fit the working solutions of Natural Language Processing and Finance.
To evolve my intelligence, I prefer Explain it out and Code it up
ping me @ wenchen.li.cs@gmail.com


Papadopoulos NG et al.,
2002,
Greece
[
15]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Oct 1999–Sep 2000 |
<18 month |
118 children,
87 virus-positive cases:
Viral types
- RSV 63/87 (72.4%)
- RV 25/87 (28.7%)
- Others 18/87 (20.7%)
Co-infections
- Coinfect. 16/87 (19.5%)
- RV + RSV 10/16 (62.5%)
|
15-points CSI
based on
heart rate, respiratory rate, cyanosis,
difficult in feeding, and oxygen saturation
|
RV associated with a higher CSI on admission at the univariate analysis
(OR 5.4; 95% CI 1.7–17.2;
p = 0.004)
and at the multivariate logistic regression analysis
(OR 4.9; 95% CI, 1.2–18.7;
p = 0.022)
|
Richard N,
2008,
France
[
16]
|
Retrospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Sep 2003–Apr 2005
(2 winter seasons)
|
<1 year |
180 infants,
173 virus-positive cases:
Viral types
- RSV 130/173 (75.1%)
- RV 39/173 (22.5%)
- Others 36/173 (20.8%)
Co-infections
- Coinfect. 44/173 (25.4%)
- RV + RSV 13/44 (29.5%)
|
Admission to PICU
Hospital LOS
|
Viral co-infections associated with an increased risk of admission to PICU
at the univariate analysis (OR 2.9; 95% CI 1.4–6.4) and at the multivariate logistic regression analysis
(OR 2.7; 95% CI, 1.2–6.2;
p = 0.02)
LOS tended to be prolonged in co-infection, but no statistically significant difference
|
Marguet C et al.,
2009,
France
[
17]
|
Prospective multicenter,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Nov 2002–Mar 2004
(3 winter seasons)
|
<1 year |
209 infants,
198 virus-positive cases:
Viral types
- RSV 134/198 (67.7%)
- RV 56/198 (28.3%)
- Others 32/198 (16.2%)
Co-infections
- Coinfect. 49/198 (24.7%)
- RV + RSV 30/49 (61.2%)
|
Duration of oxygen requirement
Hospital LOS > 5 d
|
At the multivariate logistic regression, RV infection decreased the risk to LOS > 5 d compared to other viral infections, especially RSV-infection
(OR 0.11,
95% CI 0.03-0.37)
At the multivariate logistic regression, RV infection decreased the oxygen requirement compared to RSV-infections
(OR 0.29,
95% CI 0.09–0.90)
|
Calvo C et al.,
2010,
Spain
[
18]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Sep 2005–Jul 2008 |
<2 years |
318 children,
275 virus-positive cases:
Viral types
- RSV 195/275 (61.3%)
- RV 64/275 (17.4%)
- Others 109/275 (39.6%)
Co-infections
- Coinfect. 79/275 (28.7%)
N.B. frequency of RV+RSV infection not specified
|
Hospital LOS |
No significant differences between RV and RSV single infections, nor between co-infections and single infections |
Midulla F et al.,
2010,
Italy
[
19]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Sep 2004–May 2007
(3 winter seasons)
|
<1 year |
182 infants,
104 virus-positive cases:
Viral types
- RSV 75/104 (72.1%)
- RV 16/104 (15.4%)
- Others 26/104 (25%)
Co-infections
- Coinfect. 16/104 (15.4%)
- RV + RSV 0
|
8-points CSI based on respiratory rate, nasal flaring or chest retractions,
difficult in feeding,
arterial oxygen saturation in room air
Hospital LOS
|
RV infection bronchiolitis had a lower CSI at admission than RSV infection
(3.00 ± 2.0 vs. 4.3 ± 2.4,
p = 0.05)
RV infection had a lower LOS than RSV-infection
(4.0 ± 1.6 d vs. 5.3 ± 2.4 d,
p = 0.05)
|
Miller EK et al.,
2011,
USA
[
20]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Fall 2004–Spring 2008
(4 winter seasons)
|
<1 year |
455 infants:
Viral types
- RV 81/455 (17.8%)
- Others 374/455 (82.2%)
Co-infections
- RV + other 40/455 (9%)
|
12-points CSI based on respiratory rate, nasal flaring or chest retractions,
wheezing,
oxygen saturation in room air
Oxygen requirement
Hospital LOS
|
RV infection
associated with a lower median CSI than infants with other viruses
(2.0, (IQR, 1.0–5.5) vs.
5.0 (IQR, 2.0–8.0),
p < 0.001)
RV infection associated with less requirement of supplemental oxygen than infants with
other viral causes
(18% vs. 51%,
p < 0.001)
No significant differences in LOS
|
Brand HK et al.,
2011,
the Netherlands
[
21]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Nov 2006–Apr 2009
(3 winter seasons)
|
<2 years |
142 children,
138 virus-positive cases:
Viral types
- RSV 104/138 (75.3%)
- RV 43/138 (31.2%)
- Others 64/138 (46.4%)
Co-infections
- Coinfect. 58/138 (42%)
- RV + RSV 24/58 (41.4%)
|
Disease severity
3 groups:
mild group (no hypoxia or feeding problems), moderate group (oxygen requirement, feeding problems),
severe group (MV requirement)
|
Co-infections more frequently found in mild and moderate disease
than in severe disease
(56 and 44% respectively vs. 19%,
p = 0.003)
|
Mansbach JM et al.,
2012,
USA
[
22]
|
Prospective multicenter cohort study (MARC),
hospitalized children with a clinical diagnosis of bronchiolitis
|
Nov 2007–Mar 2010
(3 winter seasons)
|
<2 years |
2207 children,
2068 virus-positive cases:
Viral types
- single RSV 1075/2068 (52%)
- single RV 167/2068 (8.1%)
N.B. frequency of other viral infection not specified
Co-infections
- Coinfect. 658/2068 (31.8%)
- RV + RSV 287/2068 (13.9%)
- RV + non-RSV 110/2068 (5%)
-RSV + non-RV 227/2068 (11%)
|
Hospital LOS (cut-off ≥3 d)
PICU admission
CPAP/intubation requirement
|
RV only infection or RV + non-RSV infection associated with shorter LOS than RV + RSV co-infection
(5% and 4% LOS ≥ 3 d
vs. 18% LOS ≥ 3 d,
p < 0.001)
No significant differences in terms of PICU admission and CPAP/intubation requirement between viral groups
|
Ricart S et al.,
2012,
Spain
[
23]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Oct 2007–Oct 2008 |
<1 year |
418 infants,
410 virus-positive cases:
Viral types
- RSV 287/410 (70%)
- RV 125/410 (30.5%)
- Others 254/410 (62%)
Co-infections
- Coinfect. 189/410 (46.1%)
- RV + RSV 56/189 (24.3%)
|
16-points CSI based on respiratory and heart rate,
air entrance, retractions, wheezing, oxygen saturation in room air
|
RV infection associated with a severe CSI (≥11) than the other viral types at the univariate analysis
(39% CSI ≥ 11
vs. 27.7% CSI < 11,
p = 0.041),
but no significant difference at the multivariate logistic regression analysis
|
Miller EK et al.,
2013,
USA
[
24]
|
Prospective single-center,
children with a clinical diagnosis of bronchiolitis
|
Sep 2004–May 2008 |
<1 year |
455 infants,
412 virus-positive cases
Viral types
- RSV 268/412 (65%)
- RV 41/412 (10%)
- Others 72/412 (17.5%)
Co-infections
- RV + RSV 30/412 (7.3%)
N.B. frequency of global co-infections not specified
|
12-points CSI based on respiratory rate, nasal flaring or chest retractions, wheezing, oxygen saturation in room air |
RV infection associated with lower CSI than RSV-infection
(median 6.5 (IQR 3.9–8) vs. median 7 (IQR 5–9),
p = 0.013)
|
Chen YW et al.,
2014,
Taiwan
[
25]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Jan 2009–Mar 2011 |
<2 years |
113 children,
86 virus-positive cases:
Viral types
- RSV 49/86 (57%)
- RV 14/86 (16.3%)
- Others 56/86 (65.1%)
Co-infections
- Coinfect. 28/86 (32.6%)
N.B. frequency of RV+RSV infection not specified
|
Hospital LOS |
No significant differences among RV infections and other single and mixed infections |
Jartti T et al.,
2014,
Finland and USA
[
26]
|
Prospective multicenter cohort study (MARC),
hospitalized children with a clinical diagnosis of bronchiolitis
|
Nov 2008–Mar 2010
(2 winter seasons)
|
<2 years |
408 children,
350 virus-positive cases:
Viral types
- RSV 144/350 (41.4%)
- RV 92/350 (26.3%)
- Others 113/350 (32.3%)
Co-infections
- Coinfect. 63/350 (18%)
- RSV + RV 10/63 (15.9%)
|
Hospital LOS
(2 groups: <3 d
and ≥3d)
|
Overall RV infection associated with shorter hospital LOS compared to RSV-infection
(adjusted OR 0.45,
95% CI 0.22–0.92,
p = 0.03)
|
Mehta R et al.,
2014,
USA
[
27]
|
Prospective cross-sectional single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Oct 2010–Apr 2011 |
<2 years |
131 children,
120 virus-positive cases:
Viral types
- RSV 84/120 (70%)
- RV 34/120 (28.3%)
- Others 37/120 (30.8%)
Co-infections
- Coinfect. 39/120 (32.5%)
- RV + RSV 18/39 (46.2%)
|
Admission to PICU |
No significant differences between types of viral infection about admission to PICU |
Selvaggi C et al.,
2014,
Italy
[
28]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
2008–2011
(3 epidemic seasons)
|
<1 year |
250 infants,
118 single RV or RSV-infection
Viral types
- RSV 78/118 (66.1%)
- RV 40/118 (33.9%)
- RV-A 18/40 (45%)
- RV-C 22/40 (55%)
|
Hospital LOS |
No significant differences in term of LOS between RV infection, RV types, and RSV bronchiolitis |
Jartti et al.,
2015,
Finland and USA
[
29]
|
Prospective multicenter cohort study (MARC),
hospitalized children with a clinical diagnosis of bronchiolitis with a focus on RV genomic viral load
|
2007–2010
(4 winter seasons, USA)
2008–2010
(3 winter seasons, Finland)
|
<2 years |
2615 children,
694 with RV detection
Viral types
- RV 694/2615 (26.5%)
- RV only 259/694 (37.3%)
Co-infections
- RV + RSV 297/694 (43%)
- RV + non RSV 138/694 (20%)
|
Hospital LOS (cut-off ≥3 d)
PICU admission
CPAP/intubation requirement
N.B. analyzed outcomes after classification of RV genomic load into 3 groups (low, intermediate, high)
|
No significant association between RV genomic load, nor co-infection, and LOS ≥ 3 days or risk of PICU admission or CPAP/intubation requirement in unadjusted and multivariable models |
Cangiano G et al.,
2016,
Italy
[
30]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Oct 2004–Mar 2014
(10 winter seasons)
|
<1 year |
723 infants,
351 virus-positive cases:
Viral types
- RSV 234/351 (66.7%)
- RV 44/351 (12.5%)
- Others 34/351 (9.7%)
Co-infections
- Coinfect. 39/351 (11.1%)
- RV + RSV 14/39 (35.9%)
|
8-points CSI based on respiratory rate, chest retractions, difficult in feeding, arterial oxygen saturation on room air
Hospital LOS
|
No significant differences in terms of clinical severity score and LOS between different viruses’ group, including co-infected infants |
Skjerven HO et al.,
2016,
Norway
[
31]
|
Prospective multicenter,
hospitalized children with a clinical diagnosis of moderate-severe bronchiolitis based on a 10-points CSI
|
Jan 2010–May 2011
(2 winter seasons)
|
<1 year |
363 infants,
330 virus-positive cases:
Viral types
- RSV 300/330 (90.9%)
- RV 122/330 (37%)
- RV A/B 35/122 (28.7%)
- RV C 87/122 (71.3%)
- Others 314/330 (95.2%)
Co-infections
- Coinfect 223/330 (67.6%)
- RV + RSV 105/233 (45%)
|
Hospital LOS
Level of supportive care (3 groups:
no supportive care, use of oxygen and/or nasogastric tube feeding,
use of ventilatory support)
|
No significant differences in terms of LOS nor level of supportive care between RV infection, RV types, other single virus-infections, RV + RSV infections, other co-infections |
Paul SP et al.,
2017,
UK
[
32]
|
Prospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Apr 2012–Dec 2015 |
<2 years |
319 children,
227 single RV or RSV-infection
Viral types
- RSV 162/227 (71.4%)
- RV 65/227 (28.6%)
|
Hospital LOS |
RV infection associated with a longer hospital LOS than RSV-infection
(4.6 d (range 0–21 d) vs. 3.2 d (range 0–13 d),
p = 0.032)
|
Garcìa-Garcìa ML et al.,
2017,
Spain
[
33]
|
Prospective cross-sectional single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Oct 2013–Apr 2016 |
<2 years |
213 hospitalized children vs. 45 healthy controls,
186 virus-positive cases:
Viral types
- RSV 149/186 (80.1%)
- RV 42/186 (22.6%)
- Others 42/186 (22.6%)
N.B. frequency of co-infections nor RV + RSV infection not specified
|
Hospital LOS |
No significant differences among RV bronchiolitis and RSV-bronchiolitis |
Petrarca L et al.,
2018,
Italy
[
34]
|
Retrospective single-center,
hospitalized children with a clinical diagnosis of bronchiolitis
|
Oct 2004–May 2016
(12 winter seasons)
|
<1 year |
486 virus-positive infants:
Viral types
- RSV 365/486 (75.1%)
- RV 89/486 (18.3%)
Co-infections
- Coinfect. 54/486 (11.1%)
- RV + RSV 20/54 (37%)
|
8-points CSI based on respiratory rate, nasal flaring or chest retractions,
difficult in feeding,
arterial oxygen saturation in room air
PICU admission
Hospital LOS
Oxygen requirement
|
No significant differences about clinical severity score, PICU admission, LOS, oxygen requirement between RSV-infection, RV infection, RV + RSV infection |
Praznik A et al.,
2018,
Slovenia
[
35]
|
Retrospective single-center,
children with a clinical diagnosis of bronchiolitis with focus on the need for hospitalization (stratification in 3 groups of patients: outpatients with no hospitalization, standard hospitalization, admission to PICU)
|
May 2014–Apr 2015 |
<2 years |
761 children,
473 virus-positive cases:
Viral types
- RSV 272/473 (57.5%)
- RV 121/473 (25.6%)
- Others 87/473 (18.4%)
Co-infections
- Coinfect. 151/473 (31.9%)
N.B. frequency of RV+RSV infection not specified
|
Disease severity classified according to the site management
(3 groups: outpatients, standard hospital, PICU)
|
No significant differences between types of viral infection about patient’s site management |
Bergroth E et al.,
2019,
Finland
[
36]
|
Prospective multicenter cohort study (MARC-30),
hospitalized children with a clinical diagnosis of bronchiolitis with follow-up 4 years after discharge
|
Nov 2008–Mar 2010 |
<2 years |
349 children,
302 virus-positive cases:
Viral types and RV types
- RSV 145/302 (48%)
- RV 101/302 (33.4%)
- RV-A 24/101 (23.8%)
- RV-B 3/101 (3%)
- RV-C 73/101 (72.3%)
- Others 51/302 (16.9%)
Co-infections
- RSV + RV 8/302 (2.6%)
N.B. frequency of co-infections not specified
|
Hospital LOS
(cut-off ≥ 3 d)
Admission to PICU
|
RV-A and RV-C infection associated with lower LOS than RSV-infection
(25% and 19% vs. 41%,
p = 0.003)
RV infection associated with lower admission to PICU than RSV-infection
(0 vs. 9%,
p = 0.02)
|
Hasegawa K et al.,
2019,
Finland and USA
[
37]
|
Prospective multicenter cohort study
(MARC-30, Finland and MARC-35 USA),
hospitalized children with a clinical diagnosis of bronchiolitis with a focus on RVspecies
|
2008–2010
(2 winter seasons, Finland)
Nov 2011–Apr 2014
(3 winter seasons, USA)
|
<1 year |
MARC-30 Finland
408 infants,
109 RVinfection
RV types
RV-A (23%)
RV-B (3%)
RV-C (74%)
Co-infections
RV + RSV (7%)
|
MARC-35 USA
1016 infants, 197 RVinfection
RV types
RV-A (47%)
RV-B (6%)
RV-C (47%)
Co-infections
RV + RSV (56%)
|
Hospital LOS
PICU admission
CPAP/intubation requirement
|
No significant differences in terms of LOS, PICU admission, and CPAP/intubation requirement between the 3 different RV types |


Leonardo di ser Piero da Vinci more commonly Leonardo da Vinci or simply Leonardo, was an Italian polymath of the Renaissance whose areas of interest included invention, painting, sculpting, architecture, science, music, mathematics, engineering, literature, anatomy, geology, astronomy, botany, writing, history, and cartography. He has been variously called the father of palaeontology, ichnology, and architecture, and he is widely considered one of the greatest painters of all time. Sometimes credited with the inventions of the parachute, helicopter, and tank, he epitomised the Renaissance humanist ideal.
Many historians and scholars regard Leonardo as the prime exemplar of the "Universal Genius" or "Renaissance Man", an individual of "unquenchable curiosity" and "feverishly inventive imagination", and he is widely considered one of the most diversely talented individuals ever to have lived.According to art historian Helen Gardner, the scope and depth of his interests were without precedent in recorded history, and "his mind and personality seem to us superhuman, while the man himself mysterious and remote". Marco Rosci notes that, while there is much speculation regarding his life and personality, his view of the world was logical rather than mysterious, although the empirical methods he employed were unorthodox for his time.
Simplicity is the ultimate sophistication. When once you have tasted flight, you will forever walk the earth with your eyes turned skyward, for there you have been, and there you will always long to return. Art is never finished, only abandoned. The human foot is a masterpiece of engineering and a work of art. Learning never exhausts the mind. Painting is poetry that is seen rather than felt, and poetry is painting that is felt rather than seen. Where the spirit does not work with the hand, there is no art. Iron rusts from disuse; stagnant water loses its purity and in cold weather becomes frozen; even so does inaction sap the vigor of the mind.
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Flight Insurances
Oracles
Airline
Passenger
Passengers & airlines
In case of flight delay, passengers get back 1.5 X the insurance fee paid at booking.
Airlines can withdraw ticket prices paid to them
In case of flight delay, passengers get back 1.5 X the insurance fee paid at booking.
Airlines can withdraw ticket prices paid to them


{* BKo: this performs background refresh of candidate's LinkedIn token *} {if $linkedInRefreshSilently} {/if}


Random Quote
These are some of my favorite quotes. I hope you enjoy them!
Do not go gentle into that good night. Rage, rage against the dying of the light. ~ Dylan Thomas
These are some of my favorite quotes. I hope you enjoy them!


Data Structures
Algorithms
Machine Learning
Web Development
C/C++
Python
JavaScript
Problem Solving
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Client: Professor Que, Advisor: Professor Qiao
| Member | Position |
|---|---|
| Ben Theisen | Team Lead |
| Michael Rupert | Web Master |
| Zakk Belloma | Key Concept Holder |
| Ryan Young | Key Concept Holder #2 |
| Ben Engebrecht | Communication Leader |
| Logan Boas | Communication Lead #2 |
This project deals with a portable system that will be used to analyze water in the field. The purpose of this is to determine whether there are certain nutrients in the water in certain locations. This system has two methods of testing the water. The first is sending a high voltage across a small reservoir of water that essentially vaporizes the water creating a light that goes into a fiber optic cable and into a spectrometer. The second method is shining a strong enough light through the water sample that then goes into a fiber optic cable connected to the spectrometer. After the data is collected by the spectrometer, it will be sent through our system and out to an app for the user to easily view the data.
This project could be very beneficial in several capacities, most notably in agriculture and environmental scientists. Agriculture could use this system to ensure that the water they are using is free of any harmful chemicals. This could help farmers gain better yields from crops, use their resources more efficiently (not harming crops with bad water and having to replace them), and ultimately have better financial results from their business. Environmental studies would be able to use this system to analyze potential pollutions in hard to reach areas with other equipment. With this portable system, they would be able to keep a watch on problem areas. Also, using this system, with the analysis of the spectrometer these scientists would know exactly what is causing problems in the environment. This means that they should be able to find a solution quickly. Society would benefit greatly from both uses of this project. There would be more food coming from farms and that food would be free of the harmful chemicals that would be detected by the system. Environmentalists finding pollution in water sources and dealing with the issues would lead to a cleaner environment and creating a cleaner place for people to live.
| Week | Report Link |
|---|---|
| 1 | Week 1 |
| 2 | Week 2 |
| 3 | Week 3 |
| 4 | Week 4 |
| 5 | Week 5 |
| 6 | Week 6 |
| 7 | Week 7 |
| 8 | Week 8 |
| 9 | Week 9 |
| 10 | Week 10 |


Auto Attendance Using Face Recognition
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“In the beginning of the eighteenth century the great-grandfather of the famous Lord Macaulay, the author of the glowing and impassioned History of England, was minister of Tiree and Coll, when his stipend was taken from him at the instance of the Laird of Ardchattan. The slight inconvenience of having nothing to live upon did not seem to incline the old minister in the least degree to resign his charge and to seek a flock who could feed their shepherd. He stayed valiantly on, doing his duty faithfully by his humble people. But after some time had elapsed, ‘his health being much impaired, and there being no church or meeting-house, he was exposed to the violence of the weather at all seasons; and having no manse or glebe, and no fund for communion elements, and having no mortification for schools or other pious purposes in either of the islands, and the air being unwholesome,’—he was finally compelled to leave, much to his own regret and that of his poor little flock.”
Hattie Tyng Griswold, Home Life Of Great Authors (1886)


Before Change
with tf.variable_scope(self.name):
// adds placeholders, data normalization and data noise to graph as desired
self.layer_in_x, self.layer_in_y = self._build_input_layers()
self.y_input = L.get_output(self.layer_in_y)
flow_classes = [FLOWS[flow_name] for flow_name in self.flows_type]
// get the individual parameter sizes for each flow
param_split_sizes = [flow.get_param_size(self.ndim_y) for flow in flow_classes]
mlp_output_dim = sum(param_split_sizes)
core_network = MLP(
name="core_network",
input_layer=self.layer_in_x,
output_dim=mlp_output_dim,
hidden_sizes=self.hidden_sizes,
hidden_nonlinearity=self.hidden_nonlinearity,
output_nonlinearity=None,
weight_normalization=self.weight_normalization
)
outputs = L.get_output(core_network.output_layer)
flow_params = tf.split(value=outputs, num_or_size_splits=param_split_sizes, axis=1)
// instanciate the flows with their parameters
flows = [flow(params, self.ndim_y) for flow, params in zip(flow_classes, flow_params)]
// build up the base distribution that will be transformed by the flows
base_dist = tf.distributions.Normal(loc=[0.]*self.ndim_y, scale=[1.]*self.ndim_y)
// chain the flows together and build the transformed distribution using the base_dist + flows
// Chaining applies the flows in reverse, Chain([a,b]).forward(x) being a.forward(b.forward(x))
// We reverse them so the flows are stacked ontop of the base distribution in the original order
flows.reverse()
chain = tf.contrib.distributions.bijectors.Chain(flows)
target_dist = tf.contrib.distributions.TransformedDistribution(distribution=base_dist, bijector=chain)
// since we operate with matrices not vectors, the output would have dimension (?,1)
// and therefor has to be reduce first to have shape (?,)
if self.data_normalization:
self.pdf_ = tf.squeeze(target_dist.prob(self.y_input) / tf.reduce_prod(self.std_y_sym), axis=1)
self.log_pdf_ = tf.squeeze(target_dist.log_prob(self.y_input) - tf.reduce_sum(tf.log(self.std_y_sym)), axis=1)
self.cdf_ = tf.squeeze(target_dist.cdf(self.y_input) / tf.reduce_prod(self.std_y_sym), axis=1)
else:
self.pdf_ = tf.squeeze(target_dist.prob(self.y_input), axis=1)
self.log_pdf_ = tf.squeeze(target_dist.log_prob(self.y_input), axis=1)
After Change
assert p.shape[0] == X.shape[0], "Shapes should be equal, are {} != {}".format(p.shape[0], X.shape[0])
return p
def _build_model(self):
implementation of the flow model
with tf.variable_scope(self.name):
// adds placeholders, data normalization and data noise to graph as desired
self.layer_in_x, self.layer_in_y = self._build_input_layers()
self.y_input = L.get_output(self.layer_in_y)
flow_classes = [FLOWS[flow_name] for flow_name in self.flows_type]
// get the individual parameter sizes for each flow
param_split_sizes = [flow.get_param_size(self.ndim_y) for flow in flow_classes]
mlp_output_dim = sum(param_split_sizes)
core_network = MLP(
name="core_network",
input_layer=self.layer_in_x,
output_dim=mlp_output_dim,
hidden_sizes=self.hidden_sizes,
hidden_nonlinearity=self.hidden_nonlinearity,
output_nonlinearity=None,
weight_normalization=self.weight_normalization
)
outputs = L.get_output(core_network.output_layer)
flow_params = tf.split(value=outputs, num_or_size_splits=param_split_sizes, axis=1)
// instanciate the flows with their parameters
flows = [flow(params, self.ndim_y) for flow, params in zip(flow_classes, flow_params)]
// build up the base distribution that will be transformed by the flows
if self.ndim_y == 1:
// this is faster for 1-D than the multivariate version
// it also supports a cdf, which isn"t implemented for Multivariate
base_dist = tf.distributions.Normal(loc=0., scale=1.)
else:
base_dist = tf.contrib.distributions.MultivariateNormalDiag(loc=[0.] * self.ndim_y,
scale_diag=[1.] * self.ndim_y)
// chain the flows together and build the transformed distribution using the base_dist + flows
// Chaining applies the flows in reverse, Chain([a,b]).forward(x) being a.forward(b.forward(x))
// We reverse them so the flows are stacked ontop of the base distribution in the original order
flows.reverse()
chain = tf.contrib.distributions.bijectors.Chain(flows)
target_dist = tf.contrib.distributions.TransformedDistribution(distribution=base_dist, bijector=chain)
// since we operate with matrices not vectors, the output would have dimension (?,1)
// and therefor has to be reduce first to have shape (?,)
if self.ndim_y == 1:
// for x shape (batch_size, 1) normal_distribution.pdf(x) outputs shape (batch_size, 1) -> squeeze
self.pdf_ = tf.squeeze(target_dist.prob(self.y_input), axis=1)
self.log_pdf_ = tf.squeeze(target_dist.log_prob(self.y_input), axis=1)


Mombasa Hospital Shut Down Following Night Inferno
by Eva NyamburaMombasa Hospital has been closed indefinitely following a gas cylinder explosion on Tuesday night.
The 8.30 pm inferno is said to have started in the kitchen before spreading to other parts of the 125 bed capacity health facility.
According to Coast region police commander Rashid Yakub, property of unknown value was destroyed but no lives were lost.
Read: Medical Board Orders Closure of Consolata Hospital Mathari’s Newborn Unit Over Negligence
“We cannot also ascertain the extent of the damage caused but we are lucky to have no life lost. We will be able to get more details as soon as we put out the fire. There is loss of property and we cannot immediately tell the amount lost,” Mr Yakub told reporters.
Panicked patients ran out of the hospital while those in critical condition were transferred to Pandya Memorial, Aga Khan, Premiere and Coast Provincial General Hospital.
Mombasa County Commissioner Gilbert Kitiyo also present at the scene of incident, confirmed that all patients had been accounted for and the fire was contained.
Read Also: NBK Lost Ksh 9 Billion in Deposits in 3 Months After KCB Buyout
“I want thank the rescuers who came in to help get the patients from their wards. It is an excellent job done. Everybody is safe for now but we will get to assess the damage caused on the property,” Mr Kitiyo said.


EVP_set_default_properties, EVP_default_properties_enable_fips, EVP_default_properties_is_fips_enabled - Set default properties for future algorithm fetches
#include <openssl/evp.h>
int EVP_set_default_properties(OSSL_LIB_CTX *libctx, const char *propq);
int EVP_default_properties_enable_fips(OSSL_LIB_CTX *libctx, int enable);
int EVP_default_properties_is_fips_enabled(OSSL_LIB_CTX *libctx);
EVP_set_default_properties() sets the default properties for all future EVP algorithm fetches, implicit as well as explicit. See "ALGORITHM FETCHING" in crypto(7) for information about implicit and explicit fetching.
EVP_set_default_properties stores the properties given with the string propq among the EVP data that's been stored in the library context given with libctx (NULL signifies the default library context).
Any previous default property for the specified library context will be dropped.
EVP_default_properties_enable_fips() sets the 'fips=yes' to be a default property if enable is non zero, otherwise it clears 'fips' from the default property query for the given libctx. It merges the fips default property query with any existing query strings that have been set via EVP_set_default_properties().
EVP_default_properties_is_fips_enabled() indicates if 'fips=yes' is a default property for the given libctx.
EVP_set_default_properties() and EVP_default_properties_enable_fips() are not thread safe. They are intended to be called only during the initialisation phase of a libctx.
EVP_set_default_properties() and EVP_default_properties_enable_fips() return 1 on success, or 0 on failure. An error is placed on the error stack if a failure occurs.
EVP_default_properties_is_fips_enabled() returns 1 if the 'fips=yes' default property is set for the given libctx, otherwise it returns 0.
The functions described here were added in OpenSSL 3.0.
Copyright 2019-2021 The OpenSSL Project Authors. All Rights Reserved.
Licensed under the Apache License 2.0 (the "License"). You may not use this file except in compliance with the License. You can obtain a copy in the file LICENSE in the source distribution or at https://www.openssl.org/source/license.html.


Guidance
PE and sport premium allocations for 2014 to 2015
This publication
was withdrawn on
Final 2014 to 2015 PE and sport premium allocations for maintained schools, free schools and academies, including conditions of grant.
Details
The spreadsheet gives a breakdown of
PE
and sport premium allocations for the 2014 to 2015 academic year. It is divided into 3 sections:
-
maintained schools
-
academies (including free schools)
-
non-maintained special schools
The 2 conditions of grant documents explain how to use the premium to improve
PE
and sport provision.
Guidance for schools on how to use the
PE
and sport premium
is available.


CREATING, ACCESSING AND ANALYSING BIODIVERSITY DATA FOR AFRICA
This course is taylored to postgraduate students, researchers, and professionals who are interested in the interface between biodiversity and modelling (biodiversity informatics) for decision making. Students should have a background in one or more of the following disciplines: biodiversity and conservation, mathematics, bio-mathematics, ecology, ecological modelling, or their related disciplines.
The course aims to build the next generation of interdisciplinary professionals in biodiversity informatics, with the ability to make an informed decision to pursue a career path in biodiversity or bio-mathematics research.
Learning Objectives
Proposed course outcomes:
- Awareness of key mathematical concepts applied to analysis of biodiversity data.
- Understanding of key ecological concepts such as competition, mutualism, stability.
- Awareness and understanding of different kinds of biodiversity data (as classified in the Essential Biodiversity Variables concept).
- Knowledge of availability of data on Essential Biodiversity Variables (EBVs) from freely available and restricted databases
- Ability to synthesise data to create a biodiversity database.
- Ability to apply R programming skills to process and analyse biodiversity data, in particular ecological network analyses and species distribution modelling.
Lectures will be paired with practical exercises in data handling, analysis, and visualisation to cover the majority of EBV classes (https://geobon.org/ebvs/what-are-ebvs/). Several seminar-style presentations will be included in which PhD students, postdocs, and alumni highlight their own biodiversity research. Recordings of lectures will be available to participants. Students will also be able to work on practicals in their own time if they desire, and four practicals are allocated as 'catch-up sessions' to provide give further assistance to students who have worked on practical exercises in their own time. Practicals will progressively develop skills in r-programming over the two-week course. Theory lectures will be approximately 45 minutes long and practicals will be 1-2 hours long. The course will take two weeks and participants can expect to spend up to 4-6 hours per day on the course.


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These are the logos I have created for my portfolio. The design draws inspiration from my maiden name, Ruby. I sought to create an impression that is friendly and informal, while also conveying a level of organization and clarity.
Color Logo
This is the standard logo that will be used in most cases.
font-family: Dolly
Text Color: #119b8e
Icon Color: #9b1141
Outline Color: #fffff
Background Color: Transparent
Black & White Logo
This is the logo that will be used when color is not available, or when high contrast is important.
font-family: Dolly
Text Color: #000000
Icon Color: #ffffff
Outline Color: #000000
Background Color: Transparent
Inverse Black & White Logo
This is the inverse version of the black and white logo, for use in dark modes or when light text is preferred.


| ^a${value} Second(s) Recharge | 30 |
| ${value}% Chance of Activating | 5 |
| ${value} Second Duration | 15 |
| ${value} Damage Absorption | 18 21 24 27 30 33 36 39 42 45 |
| ${value} Physical Retaliation | min: [5.0, 9.0, 13.0, 19.0, 25.0, 32.0, 39.0, 47.0, 55.0, 66.0] |
| ${value}% Damage Absorption | 7.5 9 10.5 12 13.5 15 16.5 18 19.5 21 |
| MaxLevel | 6 |
| UltimateLevel | 10 |


ETSoc
Cardiff University Electronics and Technology Society
About Us
The society meets every week (almost without fail) and we hope to always find something you will enjoy building or doing, whatever your previous level of electronics experience. We'll be running a variety of events throughout the year, such as project sessions, talks, trips and (of course) socials. If you have your own ideas then we have the facilities to help you out too!
Just turn up! Our meetings run every Wednesday in E1.13 at 1:30pm. That's the big Electronics Lab in the East Wing of the Engineering Buildings. If you can't find us, just go to the first floor in either the South or West building, and follow it around anti-Clockwise until you see a room labelled E1.13 or ask someone - most people will be able to point you the right way.
If there any questions, don't hesitate to contact commitee members, or join the society group on facebook, and leave a message there. You can even collar us in the corridor. Regardless of that, we look forward to hearing from you soon!
Upcoming Events
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Contact Us
Email<EMAIL_ADDRESS>Lets get in touch. Send us a message:


09 August 1999
Disease Outbreak Reported
The cholera outbreak which began in late March has stabilized in the 2 provinces affected, and the number of reported cases has declined considerably during recent weeks. In Antananarivo province, the situation has been less acute than in Mahajanga province. There is a risk that some districts in Mahajanga province (Mahajanga I and II, Marovoay and Mitsinjo) may become endemic. Some cases have also been reported recently in Antsiranana province, Nosy Be district.
Strict control measures were implemented during the early stages of the outbreak, including: care of suspect cases in health centres and hospitals, and treatment of contacts; disinfection and other hygiene measures in treatment centres, as well as in patients' homes; widespread campaigns to educate and inform the public; and epidemiological surveillance.
Notification of cases to WHO and information on the areas infected have been very regular and in conformity with the requirements of the International Health Regulations. It has therefore been possible to give a true picture of the situation, thus avoiding unfounded rumours which could affect trade and tourism in the country.
There is no reason for travellers/tourists to postpone travel to Madagascar as the risk of infection is negligible if basic hygiene measures are taken.


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I've taken up programming on the side for the past year, as something to do on the side while I wait for college but it wasn't until this January that I began to take up web development. I found it quite enjoyful to build applications and websites which is what lead me here
Whether you're a student looking to showcase your work, a professional looking to attract clients, or a graphic artist looking to share your projects, this template is the perfect starting point!
I've taken up programming on the side for the past year, as something to do on the side while I wait for college but it wasn't until this January that I began to take up web development. I found it quite enjoyful to build applications and websites which is what lead me here
Whether you're a student looking to showcase your work, a professional looking to attract clients, or a graphic artist looking to share your projects, this template is the perfect starting point!


TABLE OF INTERACTIONS WITH
SULFINPYRAZONE
From the French ANSM drug interactions document of September 2016, p. 208
SULFINPYRAZONE RxNorm: 10205 ATC: M04AB02 |
CICLOSPORIN RxNorm: 3008 ATC: L04AD01 S01XA18 |
Decrease of the blood concentrations of ciclosporin due to increase of its metabolism by the sulfinpyrazone |
Precaution for use Testing of blood concentrations of ciclosporin and possible adjustment of its dosage during the treatment with sulfinpyrazone and after it is stopped. |


a lean, people-centered, data- & innovation-focused approach
innovate
create
empower
Activities & Interests
data analytics & AI
decision support systems
consulting & implementation
modelling & simulation
develop yourself, grow & shine
team building & communication
applications & consulting
reshaping business
smart marine devices


The sinking of the RMS Titanic was one of the most infamous shipwrecks in history. On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew.
SVC : 82%
KNN: 80%
Random Forest: 79%
The Titanic was 882 feet 9 inches (269.06 m) long with a maximum breadth of 92 feet 6 inches (28.19 m). Her total height was 104 feet (32 m).
The Titanic was equipped with three main engines: two reciprocating four-cylinder, triple-expansion steam engines and one centrally placed low-pressure Parsons turbine, each driving a propeller. The two reciprocating engines had a combined output of 30,000 hp.
The chart at the left shows the number of passengers in each passenger class on the Titanic.
The chart at the right shows the number of male and female passengers on the Titanic.


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