diff --git a/_includes/workflow.html b/_includes/workflow.html
index d6023a1b38b7023bab90b94a661de1f21c77accb..cdece70e484bbebfb893d95e639cf76d32fba435 100644
--- a/_includes/workflow.html
+++ b/_includes/workflow.html
@@ -2,7 +2,6 @@
 <link rel="stylesheet" href="/project-pages/css/mermaid.forest.css">
 <script>mermaid.initialize({startOnLoad:true});</script>
 
-
 <!-- Get Content for This Post and Split at Pound Signs to Find Headers -->
 {% for post in site.posts %}
 	{% if post.title == page.title and post.author == page.author and post.date == page.date %}
@@ -70,7 +69,7 @@ graph TB
 
 			node{{ sub }}({{ currentheader }})
 			
-			click node{{ sub }} "{{ currentheader | strip | downcase | replace:' ','-' | prepend:'page.url' }}"
+			click node{{ sub }} "{{ currentheader | strip | downcase | replace:' ','-' | prepend:'#' }}"
 
 		{% else %}
 		
diff --git a/_includes/workflowmatlab.html b/_includes/workflowmatlab.html
new file mode 100644
index 0000000000000000000000000000000000000000..ecc29c6eeb3a58444d01fcab6bc7f3063d9a287d
--- /dev/null
+++ b/_includes/workflowmatlab.html
@@ -0,0 +1,76 @@
+<script src="/project-pages/js/mermaid.min.js"></script>
+<link rel="stylesheet" href="/project-pages/css/mermaid.forest.css">
+<script>mermaid.initialize({startOnLoad:true});</script>
+
+<!-- Get Content for This Post and Split at Pound Signs to Find Headers -->
+{% for post in site.posts %}
+	{% if post.title == page.title and post.author == page.author and post.date == page.date %}
+		{% assign text = post.content | newline_to_br | split:"<h" %}
+	{% endif %}
+{% endfor %}
+{% assign subcount = text | size | minus:1 %}
+
+<!-- Encapsulate in Capture to Handle the Whitespace and Allow Commenting -->
+{% capture MermaidBlock %}
+
+<!-- Initialize Header Counts -->
+{% assign smallheadercount = 0 %}
+
+<!-- Initialize Flowchart -->
+graph TB
+
+<!-- Loop over Potential Headers -->
+{% for sub in (1..subcount) %}
+
+	<!-- Get Length of Potential Header String -->
+	{% assign headerlevel = text[sub] | truncate: 1 %}
+
+		<!-- If I am the First in Sequence Nowhere to Connect -->
+		{% if smallheadercount == 0 %}
+		
+			{% assign currentheader = text[sub] | newline_to_br | split:"<br />" | first | split:">" %}
+			{% assign currentheader = currentheader[1] | split:"<" | first %}
+			{% assign currentsub = sub %}
+			{% assign smallheadercount = smallheadercount | plus:1 %}
+
+			node{{ sub }}({{ currentheader }}) 
+			
+			click node{{ sub }} "{{ sub | prepend:'#' }}"
+
+		{% else %}
+		
+			{% assign prevheader = currentheader %}
+			{% assign prevsub = currentsub %}
+			{% assign currentheader = text[sub] | newline_to_br | split:"<br />" | first | split:">" %}
+			{% assign currentheader = currentheader[1] | split:"<" | first %}	
+			{% assign currentsub = sub %}
+			
+			node{{ prevsub }}({{ prevheader }}) --> node{{ sub }}({{ currentheader }})
+			
+			click node{{ sub }} "{{ sub | prepend:'#' }}"
+
+		{% endif %}
+
+	{% assign subcheck = 2 %}
+
+
+{% endfor %}
+
+<!-- Finalize Flowchart -->
+
+<!-- Finalize Capture -->
+{% endcapture %}
+
+<!-- Make the Graph -->
+<h1> Workflow Overview </h1>
+
+<!-- Strip HTML and Seperate at Line Breaks -->
+{% assign Blocks = MermaidBlock | strip_html | newline_to_br | split:"<br />" %}
+{% assign BlocksLength = Blocks | size %}
+
+<!-- Strip Each Line of Whitespace and Only Keep Lines Longer than 3 -->
+<div class="mermaid" style="margin-left:100px">
+{% for line in (0..BlocksLength) %}{% assign BlockSize = Blocks[line] | size %}{% if BlockSize > 3 %}
+{{ Blocks[line] | strip | strip_newlines }}
+{% endif %}{% endfor %}
+</div>
\ No newline at end of file
diff --git a/_posts/2015-01-01-New-Post2.markdown b/_posts/2015-01-01-New-Post-2.markdown
similarity index 96%
rename from _posts/2015-01-01-New-Post2.markdown
rename to _posts/2015-01-01-New-Post-2.markdown
index 45a89898285b8e39e016af32a96258e55bb4cea1..3419396cdebbb34127902eafaab1e14697c6f89a 100644
--- a/_posts/2015-01-01-New-Post2.markdown
+++ b/_posts/2015-01-01-New-Post-2.markdown
@@ -1,9 +1,9 @@
 ---
 layout:     post
-title:      Post Headline 2
-date:       2016-04-01 12:00:00
+title:      Post Headline
+date:       2015-01-01 12:00:00
 author:     Ahmet Cecen
-tags: 		thoughts
+tags: 		template
 subtitle:  Some short description of post.
 ---
 <!-- Start Writing Below in Markdown -->
diff --git a/_posts/2016-05-02-matlabpost.md b/_posts/2016-05-02-matlabpost.md
new file mode 100644
index 0000000000000000000000000000000000000000..47345b2884e6231558eafe6fc3e3d60298ce57ff
--- /dev/null
+++ b/_posts/2016-05-02-matlabpost.md
@@ -0,0 +1,100 @@
+---
+layout:     post
+title:      New Matlab Script
+date:       2016-05-02 
+author:     Ahmet Cecen
+tags: 		matlab workflows
+subtitle:   Some Short Description of the Script
+visualworkflow: true
+---
+{% if page.visualworkflow == true %}
+   {% include workflowmatlab.html %}
+{% endif %}   
+   
+
+<!--
+This HTML was auto-generated from MATLAB code.
+To make changes, update the MATLAB code and republish this document.
+-->
+
+<br>Obtain the direct to Project-Pages publish function <a href="https://github.com/ahmetcecen/MATLAB-PublishtoProjectPages">here.</a><br>
+
+<h2>Load Variables From Workspace<a name="1"></a></h2>
+<p>Loading the microstructures and the effective property.</p>
+
+{% highlight matlab %}
+
+load GDL200Volumes.mat
+load MPL290Volumes.mat
+load EffectiveProperty290MPL200GDLinThatOrder.mat
+{% endhighlight %}
+<h2>Obtain 2-Point Statistics<a name="2"></a></h2>
+{% highlight matlab %}
+
+GG = zeros(490,49^3);
+
+for i=1:290
+    current = MPL{i};
+    GGcurrent = TwoPointMaster('full','auto',25,current);
+    GG(i,:) = GGcurrent(:)/numel(current);
+    PP(i)=GGcurrent(25,25,25)/numel(current);
+end
+
+for i=1:200
+    current = GDL{i};
+    GGcurrent = TwoPointMaster('full','auto',25,current);
+    GG(i+290,:) = GGcurrent(:)/numel(current);
+    PP(i+290)=GGcurrent(25,25,25)/numel(current);
+end
+{% endhighlight %}
+<h2>PCA<a name="3"></a></h2>
+<p>Via SVD: 
+
+<img src="data:image/png;base64,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" />
+
+
+</p>
+
+{% highlight matlab %}
+
+[PC1,Var1,Basis1]=PCAConstruct(GG,10);
+scatter(PC1(:,1),PC1(:,2),30,[ones(290,1);2*ones(200,1)],'filled'); colormap jet;
+{% endhighlight %}
+
+<img src="data:image/png;base64,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" />
+<h2>Function Estimation<a name="4"></a></h2>
+<p>Using Multivariate Polynomial Regression</p>
+
+{% highlight matlab %}
+
+reg = MultiPolyRegress(PC1(:,1:2),R,3,'figure')
+{% endhighlight %}
+
+{% highlight matlab %}
+
+reg = 
+
+           FitParameters: '-----------------'
+             PowerMatrix: [10x2 double]
+                  Scores: [490x10 double]
+    PolynomialExpression: [10x2 table]
+            Coefficients: [10x1 double]
+                    yhat: [490x1 double]
+               Residuals: [490x1 double]
+           GoodnessOfFit: '-----------------'
+                 RSquare: 0.9572
+                     MAE: 0.0571
+                  MAESTD: 0.0561
+           Normalization: '1-to-1 (Default)'
+      LOOCVGoodnessOfFit: '-----------------'
+               CVRSquare: 0.9556
+                   CVMAE: 0.0583
+                CVMAESTD: 0.0570
+         CVNormalization: '1-to-1 (Default)'
+
+
+{% endhighlight %}
+    
+<img 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" />
+
+<br><a href="http://www.mathworks.com/products/matlab/" style="font-size: 0.7em">Published with MATLAB&reg; R2014b</a><br>