diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json
index 34eb2a6f6..b5f4f1a46 100755
--- a/.obsidian/workspace.json
+++ b/.obsidian/workspace.json
@@ -246,6 +246,8 @@
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+    "4. Qualifying Exam/qualifying_exam_application_form_filled.docx",
+    "4. Qualifying Exam/lu177571y8ue.tmp",
     "4. Qualifying Exam/99. Exports/QE Abstract For Dan_v2.pdf",
     "4. Qualifying Exam/2. Writing/0. QE Abstract.md",
     "4. Qualifying Exam/2. Writing/QE Abstract For Dan.md",
@@ -280,8 +282,6 @@
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     "300s School/301. ME 2016 - Nonlinear Dynamical Systems 1/venv/share/man",
     "300s School/301. ME 2016 - Nonlinear Dynamical Systems 1/venv/share",
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diff --git a/4. Qualifying Exam/.~lock.qualifying_exam_application_form_filled.docx# b/4. Qualifying Exam/.~lock.qualifying_exam_application_form_filled.docx#
new file mode 100644
index 000000000..51937c36a
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+++ b/4. Qualifying Exam/.~lock.qualifying_exam_application_form_filled.docx#	
@@ -0,0 +1 @@
+,danesabo,danesabo-desktop,10.09.2024 12:41,file:///home/danesabo/.config/libreoffice/4;
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diff --git a/4. Qualifying Exam/2. Writing/QE Abstract For Dan.md b/4. Qualifying Exam/2. Writing/QE Abstract For Dan.md
index 9872bc472..0e97c40cd 100644
--- a/4. Qualifying Exam/2. Writing/QE Abstract For Dan.md	
+++ b/4. Qualifying Exam/2. Writing/QE Abstract For Dan.md	
@@ -1,11 +1,11 @@
 **Diffusion Generative Models For Unstructured Uncertainty Perturbations**
-The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant. If this research is successful, this diffusion model will accomplish three main tasks:
+The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant. If this research is successful, we will be able to use a diffusion generative model to do the following:
 1. Generate Bode plots based on training data of example dynamic systems
 2. Perturb a nominal plant in an unstructured manner with a controllable amount of uncertainty
 3. Approximate a set of controllable plants by generating a large number of perturbed examples
 
-The diffusion generative model has shown great promise in creating novel and realistic samples from training data. This research will train a generative model to create Bode plots of transfer functions. This model will be given a nominal plant as an input and then generate a perturbed plant. Once created, this perturbed plant can be evaluated if it belongs to the set of controllable plants for a desired controller. This process will be repeated several times to generate enough plants to approximate the set of controllable plants.
+The diffusion generative model has shown great promise in creating novel and realistic samples from training data. This research will train a generative model to create Bode plots of transfer functions. This model will be given a nominal plant as an input and then generate a perturbed plant. Once created, this perturbed plant can be evaluated if it belongs to the set of controllable plants for a desired controller. This process will be repeated several times to generate enough plants to approximate the set.
 
 These generated plants can be used to verify robustness of controller implementations. A model of a controller can use robust control theory to establish the set of controllable plants, but an actual implementation of a controller can not be verified as robust in the same way. Instead, it must be verified experimentally using elements of the set. Extracting elements of the set is not a trivial task, but if this research is successful, a generative model can reduce the effort required to create perturbed plants.
 
-**STATS: 247 / 250 words**
\ No newline at end of file
+**STATS: 250 / 250 words**
\ No newline at end of file
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