OpenLLM-France/wikimedia huggingface dataset:
# Turkish/Services

For those of who will visit Turkey for a short duration or for those who have just started with Turkish, here are some standard phrases that you can learn quickly and put to use straight away.
Many sentences required for common tasks are provided which will be very helpful in day to day life.
Hotel check-in:
Boş odanız var mı? Do you have a room?
Tek kişilik odanız var mı? Do you have a single room?
İki kişilik odanız var mı? Do you have a double room?
Geceliği ne kadar? How much is it for a night?
Kahvaltı dahil mi? Is breakfast included?
Kredi kartı ile ödeyebilir miyim? Can I pay with credit card?
Odada klima var mı? Is there air conditioner in the room?
Bu yakınlarda iyi bir restoran var mı? Is there a good restaurant nearby?
Bir problemim var (Bir sorunum var). Bakabilir misiniz? I've a problem. Could you please help me?
Teşekkür ederim. Thank you.
Rica ederim. You're welcome
Ben teşekkür ederim. You're welcome. (Literally "I thank you.)
# Structural Biochemistry/Cellulose Acetate Electrophoresis
## Overview

Cellulose acetate electrophoresis utilizes native protein charge to separate proteins based on their isoelectric point.

## How it Works

A sample protein is dotted on the marked center of a cellulose acetate strip and the strip is placed in barbital buffer of a desired pH and voltage is applied across the strip. The proteins that migrate towards the anode have a pI greater than the pH of the buffer while proteins that migrate towards the cathode have a pI less than the pH of the buffer. Positively charged proteins migrate towards the cathode while negatively charged proteins migrate toward the anode.

## Application

Cellulose acetate electrophoresis can be useful in identifying multimeric proteins formed by different isoforms since each ratio of isoforms will have a different charge due to the different amino acid structure.
# Cells Turn Nutrients into Usable Energy

Cells manage a wide range of functions in their tiny package — growing, moving, housekeeping, and so on — and most of those functions require energy. Cellular nutrients come in many forms, including sugars and fats. In order to provide a cell with energy, these molecules have to pass across the cell membrane, which functions as a barrier — but not an impassable one. Like the exterior walls of a house, the plasma membrane is semi-permeable. In much the same way that doors and windows allow necessities to enter the house, various proteins that span the cell membrane permit specific molecules into the cell, although they may require some energy input to accomplish this task.
Complex organic food molecules such as sugars, fats, and proteins are rich sources of energy for cells because much of the energy used to form these molecules is literally stored within the chemical bonds that hold them together. Scientists can measure the amount of energy stored in foods using a device called a bomb calorimeter. With this technique, food is placed inside the calorimeter and heated until it burns. The excess heat released by the reaction is directly proportional to the amount of energy contained in the food.
In reality, of course, cells don't work quite like calorimeters. Rather than burning all their energy in one large reaction, cells release the energy stored in their food molecules through a series of oxidation reactions. Oxidation describes a type of chemical reaction in which electrons are transferred from one molecule to another, changing the composition and energy content of both the donor and acceptor molecules. Food molecules act as electron donors. During each oxidation reaction involved in food breakdown, the product of the reaction has a lower energy content than the donor molecule that preceded it in the pathway. At the same time, electron acceptor molecules capture some of the energy lost from the food molecule during each oxidation reaction and store it for later use. Eventually, when the carbon atoms from a complex organic food molecule are fully oxidized at the end of the reaction chain, they are released as waste in the form of carbon dioxide.
Cells do not use the energy from oxidation reactions as soon as it is released. Instead, they convert it into small, energy-rich molecules such as ATP and nicotinamide adenine dinucleotide (NADH), which can be used throughout the cell to power metabolism and construct new cellular components. In addition, workhorse proteins called enzymes use this chemical energy to catalyze, or accelerate, chemical reactions within the cell that would otherwise proceed very slowly. Enzymes do not force a reaction to proceed if it wouldn't do so without the catalyst; rather, they simply lower the energy barrier required for the reaction to begin.
# Structural Biochemistry/Channelopathies
## Overview

Channelopathies are genetic diseases due to alterations in ion channel genes. Examples:
Familial Hemiplegic migraines (FHM). These have been shown to occur due to mutations in the Ca²+ channel in the brain. Mutations in pore-forming region of this channel result in FHM characterized by uncontrolled muscle movement. This and other examples suggest a correlation between mutations in the gene for this ion channel and specific implications of FHM symptoms. While this relation is evidenced, the original cause of the migraine is unknown.
Episodic ataxia type 2, or EA2. These mutations cause Ca²+ channels to be cut off, leading to abnormal composition of the channel. Symptoms include uncontrolled muscle movement, vertigo, nausea and headaches.
CSNB (congenital stationary night blindness) is caused by similar mutations as EA2 and leads to abnormal retinal function. It results in numerous affects on vision, including night blindness and lack of acuity.
(GEFS) Epilepsy results from a defect in the Na+ channel. These mutations slow the channel activation, which may explain the hyperexcitability phenoma characteristic of epilepsy occurring here.
(BFNC) seizures occur from mutations in the gene for the K+ channel, leading to hyperexcitability that causes abrupt seizures in the first few weeks-months of life.

## Reference

Purves, Dave, et all. Neuroscience, Fourth Edition. Sunderland, MA: C. 2008, Sinauer Associates, Inc. Text.
# Switches, Routers, Bridges and LANs/Routers/RIP

Interior Gateway Protocol is nothing but intra-AS routing protocol. It determines how routing is performed within an autonomous system. The intra routing protocols are Routing Information Protocol : (RIP) RIP is the distance vector routing protocol. Participating machines in RIP are participated as active and passive machines. Active machines participate in adverting the route and passive machines do not advertise but they listen to RIP messages and use them to update their routing table. Routing updates are exchanged( broadcast) between neighbors approximately every 30 seconds. This update message is called as RIP response message or RIP advertisement. In RIP routers apply hysteresis when it learns from another router. Hysteresis means router does not replace the route with an equal cost route. It will improve performance and reliability.
# Switches, Routers, Bridges and LANs/Routers/OSPF

OSPF is the link state protocol which uses flooding of link state information and Dijkrstra least cost path algorithm. It develops complete topological map with the help of OSPF and shortest-path in the autonomous system by running Dijkrastra’s algorithm on the router. In OSPF, router broadcasts routing information to all the other routers in the autonomous system. The router broadcast the link state information whenever there is change of link cost. It broadcast the link’s sate periodically at least once every 30 minutes. The OSPF protocol checks that links are operational by sending HELLO message that is sent to the attached neighbors. It allows the OSPF router to obtain a neighboring router’s database of network-wide link state.
Some of the advantages of OSPF are as follows:
* The specification is available in the published literature. As it is open standard anyone can implement it without paying the license fees that encourage many vendors to support OSPF.
* Load balancing is performed by OSPF. If there are multiple routes with the same cost then OSPF distributes traffic over all routes equally.
* OSPF allows site to partition networks and routers into subnets called area. The area topology is hidden from other area and each area is self-contained. The area can change its internal topology independently thus it permits growth and make the networks at a site easier to manage.
* All the exchanges between the routers are authenticated. OSPF allows variety of authentication schemes. Different area can use different authentication scheme. Router authentications are done because only trusted routers should propagate routing information.
* There is an integrated support for unicast and multicast routing. Multiple OSPF (MOSPF) provides multicast routing. MOSPF is simple extension to OSPF. MOSPF uses existing OSPF link database and adds a new type of link-state advertisement to the existing OSPF link-state broadcast mechanism.
* OSPF support for the hierarchy within a single routing algorithm.
Hierarchical structure of OSPF network is shown below:
[ Diagram similar to diagram from kurose]
* Internal router - They are within an area. They only perform intra AS routing.
* Area border router - These router belong to both an area and the backbone.
** attached to multiple areas
** runs a copy of SPF for each attached area
** relays topological info on attached areas to backbone
* Backbone router - These routes perform routing within the backbone but themselves are not area border routers.
* AS boundary routers – A boundary router perform inter-AS routing. It is through such a boundary router that other routers learn about paths to external networks.
# Switches, Routers, Bridges and LANs/Routers

* Basics of Routing
* RIP
* OSPF
* BGP
* MPLS
* Border Router Selection
# Switching from macOS to Linux

This book documents the transition of an IT administrator, who pretty much hid in his Apple comfort zone for years, from macOS to Linux, specifically, from macOS 10.12 to Ubuntu 16.04 Server. This book will cover the Server for a few reasons. First, the Desktop version is intuitive enough that almost everything can really be figured out simply by using the "click it, did it work?" method. Second, there are many versions of Linux GUI's, even for Ubuntu, but configuring Linux via the command line is more uniform.
The intended audience is experienced Mac administrators who have little to no Linux experience and would like a quick reference. As such, everything is named and organized the way it is on the Mac. This book focuses on how to perform tasks that are easily done using Mac's GUI in the Linux command line.
This book is not a Unix tutorial. Unix commands will be shown when a task requires using the command line, but they will not be explained. Many tasks will simply refer to better documentation. I'm listing the topics that I think need to be covered to make this a good switching guide, but I realize that I may never get to all of these topics.

### How to Learn

While you learn Linux you need to realize that you will probably have to start over at least once, if not many times. For this reason I recommend you use a virtual machine (like the free VirtualBox) before you install Linux on bare metal hardware, use snapshots, and keep notes. This book actually happens to be my notes and I had to revise it many times to reflect all of the changes I made as I learned.
As an example of why you might have to start over, this is what I did. I began this book installing Ubuntu 16.04 Server and learned a little. Then I decided to switch to 17.10, which was the latest but was not an LTS version. I went pretty far with that when I found some software that I wanted to install, but it only installed on LTS versions. So I had to go back to the 16.04 version.
There's also a good chance that you will make mistakes and break your server several times. While learning, use a virtual machine, keep notes, and use snapshots so you don't have to constantly start from scratch.

## Contents

* Installing
** Downloading
** Installing
** Software Update
** Getting new software
* System Preferences (Desktop)
* System Preferences
* Finder
** Navigating the Filesystem
** Add or delete items
** Move or duplicate items
** Compress items
** Aliases
** Searching
** Viewing by file type
** Viewing by file size
** Get Info
** How much space is left
** Mounting disk images
** Mounting network volumes
** Mounting and ejecting media
* Applications
** App Store
** Calculator
** Calendar
** Chess
** Contacts
** Dictionary
** DVD Player
** Font Book
** Image Capture
** iTunes
** Mail
** Messages
** Notes
** Preview
** QuickTime Player
** Reminders
** Safari
** TextEdit
* Utilities
** Activity Monitor
** Keychain Access
** Disk Utility
** Console
** System Information
** Terminal
# University of Alberta Guide/STAT/222/Combining Continuous Random Variables
## Convolution

* $f_{X+Y}(z)=\int _{-\infty }^{\infty }f_{X}(y-z)f_{Y}(z)\delta z=\int _{-\infty }^{\infty }f_{X}(z)f_{Y}(y-z)\delta z$

### Example

$f_{X}(x)={\begin{cases}{\frac {3x^{2}}{2}}&x\in [-1,1]\\0&else\end{cases}}\qquad f_{Y}(y)={\begin{cases}{\frac {y}{9}}&y\in [4,5]\\0&else\end{cases}}\qquad X{\mbox{ and }}Y{\mbox{ are independent RVs, find }}f_{X+Y}(z)$
* Start by converting the pdf's to indicator functions
** $f_{X}(x)={\frac {3x^{2}}{2}}\cdot 1_{[-1,1]}(x)\qquad f_{Y}(y)={\frac {y}{9}}\cdot 1_{[4,5]}(y)$
*** Now $f_{X}(x)\,$ is defined only when $x\in [-1,1]\,$ and $f_{Y}(y)\,$ is defined only when $y\in [4,5]\,$
* Use the convolution formula above to write out the integral
** $\int _{-\infty }^{\infty }f_{X}(z)f_{Y}(y-z)\delta z=\int _{-\infty }^{\infty }{\frac {3z^{2}}{2}}\cdot 1_{[-1,1]}(z){\frac {y-z}{9}}\cdot 1_{[4,5]}(y-z)\delta z$
* Factor out any constants, in this case, a multiplier
** ${\frac {3}{18}}\int _{-\infty }^{\infty }z^{2}\cdot 1_{[-1,1]}(z)(y-z)\cdot 1_{[4,5]}(y-z)\delta z={\frac {1}{6}}\int _{-\infty }^{\infty }z^{2}\cdot 1_{[-1,1]}(z)(y-z)\cdot 1_{[4,5]}(y-z)\delta z$
* Factor out the indicator function for $(z)\,$ into the integral bounds
** ${\frac {1}{6}}\int _{-1}^{1}z^{2}(y-z)\cdot 1_{[4,5]}(y-z)\delta z$
*** Note that $4\leq y-z\leq 5,({\mbox{ try to isolate }}z)\rightarrow 4-y\leq -z\leq 5-y\rightarrow y-5\leq z\leq y-4$
* Now that have isolated the indicator for z, we can combine the entire integral for that indicator
** ${\frac {1}{6}}\int _{-1}^{1}\left(z^{2}(y-z)\right)\cdot 1_{[y-5,y-4]}(z)\delta z={\frac {1}{6}}\int _{-1}^{1}\left(z^{2}y-z^{3}\right)\cdot 1_{[y-5,y-4]}(z)\delta z$
* Finally, split the integral into the separate cases based on the remaining indicator function
** $f_{X+Y}(z)={\frac {1}{6}}{\begin{cases}0&y<3\\\int _{-1}^{y-4}(z^{2}y-z^{3})\delta z&3\leq y<4\\\int _{y-5}^{y-4}(z^{2}y-z^{3})\delta z&4\leq y<5\\\int _{y-5}^{1}(z^{2}y-z^{3})\delta z&5\leq y<6\\0&y\geq 6\\\end{cases}}\qquad ={\begin{cases}0\\-{\frac {255}{4}}+43z+{\frac {z^{4}}{12}}-8z^{2}\\{\frac {369}{4}}-{\frac {122z}{3}}+{\frac {9z^{2}}{2}}\\156-83z-{\frac {z^{4}}{12}}+{\frac {25z^{2}}{2}}\\0\\\end{cases}}$
*** When $y<3\,$ the integral has no bounds since $(y<3)-4<-1\,$ so the upper bound would be less than $-1\,$ which would be $0\,$.
*** When $3\leq y<4\,$ the integral is bound between $-1\,$ and $y-4\,$since $y-4\,$ will be at least $-1\,$ but less than $0\,$
*** As you can see there is a pattern here, it goes as follows:
**** Given $\int _{a}^{b}(\cdot )1_{[c,d]}\delta z\,$ you will have ${\begin{cases}y<(d-a=0)\\(d-a=0)\leq y<(c-a=0)\\(c-a=0)\leq y<(d-b=0)\\(d-b=0)\leq y<(c-b=0)\\y\geq (c-b=0)\\\end{cases}}$
# University of Alberta Guide/STAT/222/Formulas and Functions/General Formulas

* Calculus
** ${\mbox{exp}}(y)=e^{y}=1+y+{\frac {y^{2}}{2!}}+\cdots +{\frac {y^{\infty }}{\infty !}}\,$
** $\int _{a}^{b}u\delta v=uv|_{a}^{b}-\int _{a}^{b}v\delta u$
** $\Gamma (\alpha )=\int _{0}^{\infty }y^{\alpha -1}e^{-y}\delta y$
** $\Gamma (1/2)={\sqrt {\pi }},\Gamma (1)=1,\Gamma (\alpha +1)=\alpha \Gamma (\alpha )$
* Moment Formulas
** $M_{X}(u)=E\left[e^{uX}\right]$
** ${\mbox{Var}}(X)=E\left[X^{2}\right]-\left(E\left[X\right]\right)^{2}$
* Central Limit Theorem
** $P\left(a<{\frac {{\sqrt {m}}\left({\hat {\Theta }}\left(m\right)-\Theta \right)}{\rho }}\leq b\right){\begin{matrix}_{m\rightarrow \infty }\\{\overrightarrow {\qquad \qquad }}\\\end{matrix}}\int _{a}^{b}{\frac {e^{-{\frac {x^{2}}{2}}}}{\sqrt {2\pi }}}\delta x$
* Convolution
** $f_{X+Y}(y)=\int _{-\infty }^{\infty }f_{X}(y-z)f_{Y}(z)\delta z=\int _{-\infty }^{\infty }f_{X}(z)f_{Y}(y-z)\delta z$
* Reliability and Hazard
** $R_{S}(t)=\prod _{i=1}^{N}R_{i}(t)$
** $R_{P}(t)=1-\prod _{i=1}^{N}\left(1-R_{i}(t)\right)$
** $h_{T}(t)={\frac {f_{T}(t)}{R_{T}(t)}}$
** $R_{T}(t)={\mbox{exp}}\left(-\int _{0}^{t}h_{T}(s)\delta s\right)$
* Redundancy
** $R(t)=\sum _{i=k}^{m}{m \choose i}\left(R_{a}(t)\right)^{i}\left(1-R_{a}(t)\right)^{m-i}$
# University of Alberta Guide/STAT/222/Formulas and Functions/Probability Density Functions

The probability density function, $f_{X}(x)\,$, represents the distribution of the random variable $X\,$. Wikipedia defines the PDF as a smoothed out version of the random variable's histogram of its distribution. Basically, the larger the value of $f_{X}(x)\,$ for a specific value $x\,$ means the more often that value $x\,$ will appear when choosing a random variable of its type of distribution.
The total area under the curve formed by $f_{X}(x)\,$ will always be one, since that total area represents the probability that the random variable $X\,$ will be a value.
PDF for an Exponential RV
One way to calculate the PDF is to differentiate its CDF, ${\frac {\delta }{\delta x}}F_{X}(x)=f_{X}(x)\,$
# University of Alberta Guide/STAT/222/Uniform Random Variables

Uniform Random Variables are used where each outcome of an event is equally probable. For $p_{U}(u)=P(U\leq u)$ where $U\,$ is some $[a,b]\,$-Uniform Random Variable, $p_{U}(u)\,$ will always be zero unless $(a\leq u\leq b)$. When $(a\leq u\leq b)$, then $p_{U}(u)={\frac {1}{b-a}}$.

## Equations

| $\int _{-\infty }^{\infty }f_{X}(x)\delta x$ | $=1\,$ |
| $f_{X}(x)\,$ | $={\begin{cases}{\frac {1}{b-a}}&a<x<b\\0&{\mbox{else}}\\\end{cases}}$ |
| $F_{X}(x)\,$ | $={\begin{cases}0&x<a\\{\frac {x-a}{b-a}}&a\leq x<b\\1&x\geq b\\\end{cases}}$ |
| $h_{X}(x)\,$ | $=???\,$ |
| $M_{X}(x)\,$ | $={\frac {e^{xb}-e^{xa}}{x\left(b-a\right)}}$ |

## Links

* The Continuous Uniform Distribution
* The Discrete Uniform Distribution
# University of Alberta Guide/STAT/222/Downloadable Resources

University of Alberta Guide > STAT > 222

## Exam Material and Solutions

* Winter 2006

## Assignment Solutions

* Winter 2006
*** 3.2.3, 3.2.6 (mistake found - Please use [a_i,b_i] instead of [0,1]), 3.2.7, 3.3.3 (ans: 2ln(4/3)), 3.3.4 (hint: see Stat 221 Notes on line), 3.3.5, 3.3.6, 3.4.1, 3.4.4, 3.4.5
** Assignment 2
*** 3.5.2, 3.5.4 (ans: 0.696735), 3.5.5, 3.5.6, 3.6.3, 3.6.4 (ans: 0.062), 3.6.9, 3.6.11, 3.7.1, 3.7.4 (ans: 0.696665)
*** 3.8.2, 3.8.6, 3.8.7, 4.2.1, 4.2.2, 4.2.3 (There are typos in the errors. I always wanted to say that.), 4.2.4, 4.3.4, 4.3.5, 4.3.6
*** 4.4.4, 4.4.8, 4.4.9, 4.5.1, 4.5.2, 4.5.4, 4.6.1, 4.6.4, 4.6.5, 4.6.6
*** 5.2.2 (Ans: 930), 5.2.3, 5.2.6, 5.2.7 (Hint: Read section 5.3 first), 5.3.2, 5.3.3, 5.3.4, 5.4.2, 5.4.3, 5.4.7 (Computers)
*** 5.5.3, 5.5.4, 5.6.1, 5.6.2, 5.6.3, 6.2.6, 6.2.9, 6.3.1, 6.3.2, 6.4.3, 6.4.5
# University of Alberta Guide/STAT/222/Independence and Conditional Expectations

The basic idea behind independence is that if your random variables (or vectors) are independent then the combination of several of these random variable/vectors $\left(P\left(X_{1}\leq x_{1},...,X_{d}\leq x_{d}\right)\right)$ can be multiplied together.
Given $T_{1},T_{2}\,$ are independent $\lambda \,{\mbox{-Exponential}}$ random variables, then $E\left[T_{1}+T_{2}\right]=E\left[2T_{1}\right]=E\left[2T_{2}\right]$. But ${\mbox{Var}}\left(T_{1}+T_{2}\right)<{\mbox{Var}}\left(2T_{1}\right)$. This is because ${\mbox{Var}}\left(T_{1}+T_{2}\right)={\mbox{Var}}\left(T_{1}\right)+{\mbox{Var}}\left(T_{2}\right)=2{\mbox{Var}}\left(T_{1}\right)$ by independence, whereas ${\mbox{Var}}\left(2T_{1}\right)=2^{2}\cdot {\mbox{Var}}\left(T_{1}\right)$.
See the equations section for some more examples.

## Equations

| $P\left(X_{1}\leq x_{1},...,X_{d}\leq x_{d}\right)\,$ | $=P\left(X_{1}\leq x_{1}\right)\cdots P\left(X_{d}\leq x_{d}\right)\,$ |
| $F_{X_{1},X_{2},...X_{d}}(x_{1...d})\,$ | $=F_{X_{1}}(x_{1})\cdots F_{X_{d}}(x_{d})\,$ |
| $f_{X_{1},X_{2},...X_{d}}(x_{1...d})\,$ | $=f_{X_{1}}(x_{1})\cdots f_{X_{d}}(x_{d})\,$ |
| $E\left[X_{1}\cdot X_{2}\cdots X_{d}\right]\,$ | $=E\left[X_{1}\right]\cdots E\left[X_{d}\right]\,$ |
| $E\left[g_{1}\left(X_{1}\right)\cdot g_{2}\left(X_{2}\right)\cdots g_{d}\left(X_{d}\right)\right]\,$ | $=E\left[g_{1}\left(X_{1}\right)\right]\cdots E\left[g_{d}\left(X_{d}\right)\right]\,$ |
| $M_{X_{1}+...+X_{d}}(x)\,$ | $=M_{X_{1}}(x)\cdots M_{X_{1}}(x)\,$ |
| $F_{XY}(x,y)\,$ | $=???\,$ |
| $f_{XY}(x,y)\,$ | $=???\,$ |
| $F_{X|Y}(x|y)\,$ | $=???\,$ |
| $F_{X+Y}(x)\,$ | $=P\left(X+Y\leq x\right)\,$ |
# University of Alberta Guide/STAT/222/Introduction/Notation

$F_{X}(x):X{\mbox{ is a random variable having }}cdf=P(X\leq x){\mbox{ for some value }}x\,$
$f_{X}(x):X{\mbox{ is a random variable having }}pdf{\mbox{ value for a specific random value }}x\,$
* This is basically stating that the value of $f_{X}(x)\,$ will represent the distribution or relative amount of times that the random value $x\,$ will appear when choosing a random value with the distribution of $X\,$
* Calculus gives us that ${\frac {\delta }{\delta x}}F_{X}(x)=f_{X}(x)\,$
# University of Alberta Guide/STAT/222/LaTeX Version

University of Alberta Guide > STAT > 222
# University of Alberta Guide/STAT/222/Uncatagorized

University of Alberta Guide > STAT > 222
# University of Alberta Guide/STAT/222/Formulas and Functions:Cumulative Distribution Functions

University of Alberta Guide > STAT > 222
The $F_{X}(x)\rightarrow (cdf)\,$, represents the probability that the value of a random variable $X\,$, regardless of its distribution, will be less than or equal to the value $x\,$ that is provided. For continuous random variables, $F_{X}(0)=0\,$ because $P(X=0)=0\,$ for all continuous random variables. Therefore, all cumulative distribution functions will start at zero.
Generally, $F_{X}(x)\,$ represents $P(X\leq x)\,$, however, if what is desired is $P(X>x)\,$ then $1-F_{X}(x)\,$ would be used.
CDF for an Exponential RV
Notice that as $x\,$ increases, the probability nears closer and closer to one. This is because as $x\,$ increases, the area under the curve of the PDF becomes closer and closer to one.
# Teaching Elementary School Health Education/Injury Prevention and Safety/Physical Violence and Abuse/Self Defense Strategies

For the Home
For Public Places
Social Situations
# Teaching Elementary School Health Education/Injury Prevention and Safety/Sexual Violence and Abuse/Alcohol and other Drug Use

Date Rape Drugs
Rohypnol
GHB
Ketamine
# Teaching Elementary School Health Education/Injury Prevention and Safety/Sexual Violence and Abuse/Recovering for Survivors of Sexual Violence

Recovery for Rape Survivors
Recovery for Perpetrators
# Teaching Elementary School Health Education/Injury Prevention and Safety/Sexual Violence and Abuse

Sexual Violence and Abuse
Rape
Acquaintance and Date Rape
Laws Concerning Date Rape
Alcohol and other Drug Use
Sexual Abuse
Sexual Harassment and Stalking
Recovering for Survivors of Sexual Violence
# Teaching Elementary School Health Education/Injury Prevention and Safety/Physical Violence and Abuse/Sexual Abuse

Incest
Laws and Sexual Abuse
# Teaching Elementary School Health Education/Injury Prevention and Safety/Severe Weather, Natural Disasters, and National Alerts

Severe Weather, Natural Disasters, and National Alerts
Guidelines
Landslides
Floods
Earthquakes
Tornadoes
Hurricanes
Wildland Fires
Electrical Storms
Winter Storms
The Homeland Security Advisory System
# Teaching Elementary School Health Education/Injury Prevention and Safety/Sexual Violence and Abuse/Sexual Harassment and Stalking

Sexual Harassment and Stalking Laws