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# How to Power a Project

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Contributors: JordanDee

## Overview

This tutorial will cover the various ways you can power your electronic projects. It will go into some detail about voltage and current considerations you may want to make. It will also go into the extra considerations you have to make if your project is mobile/remote or, in other words, not going to be sitting next to a wall power outlet.

If this is truly your first electronic project, you have the option of reading through this tutorial or sticking with the recommended supply for the project or development board of your choice. The SparkFun Inventor’s Kit contains the USB cable you need for power and works fine for all the projects in the kit as well as many more advanced projects. If you're feeling overwhelmed, that kit is the best place to start.

Here are related tutorials you may want to check out before reading this one:

## Ways to Power a Project

Here are some of the most common methods used for powering a project:

• USB Power
• Variable DC Benchtop Power Supply
• AC to DC Wall Adapter (like a computer or laptop would use)
• Batteries

Four common ways to supply power to your project

### Which Option Should I Pick to Power My Project?

The answer to this question largely depends on your project specific requirements.

#### USB Power

If you're starting off with the SparkFun Inventor's Kit or another basic development board, you will likely just need a USB cable. The Arduino Uno is an example that requires only a USB A to B cable to supply the power to run the example circuits in the kit. Here are a few USB cables from our catalog to power your project from a USB port.

CAB-10215
1

CAB-11301
$3.95 2 ### USB 3.1 Cable A to C - 3 Foot CAB-14743$4.95
3

#### Variable DC Benchtop Power Supply

If you're in the business of building projects and testing circuits regularly, acquiring a variable DC bench power supply is highly recommended. This will allow you to set the voltage to a specific value depending on what you need for your project. It also buys you some protection as you can set a maximum current allowed. Then, if there is a short circuit in your project, the bench supply will shut down hopefully preventing harm to some components in your project.

Here are a few variable DC benchtop power supplies from our catalog.

TOL-14926

TOL-14928
$183.95 ### Switching Power Supply - DC 20V, 5A TOL-14929$286.95
2

#### AC to DC Wall Adapters

A specific AC to DC power supply is often used after a circuit is proven. This option is also great if you often use the same development board again and again in your projects. These wall adapters usually have a set voltage and current output, so it's important to make sure that the adapter you choose has the correct specifications as the project you will be powering and to not exceed those specifications. Here are a few wall adapters from the catalog that offer a few amps.

TOL-15313
2

TOL-15314
$5.95 1 ### Wall Adapter Power Supply - 5VDC, 2A (USB Micro-B) TOL-15311$5.95

For more current hungry projects, check out some of these power supplies from our catalog. Just make sure to get the appropriate cable for your region from product page's recommended products.

TOL-14601

TOL-16266
$23.95 ### Mean Well Switching Power Supply - 5VDC, 18A TOL-16607$19.95

#### Batteries

If you want your project to be mobile or based in a remote location away from where you can gather AC wall power from the grid, batteries are the answer you're looking for. Batteries come in a huge variety, so be sure to check out the later parts of this tutorial so you can figure out precisely what to choose. Common choices include alkaline, rechargeable NiMH AA's, and lithium polymer ion. Here are a few batteries from the catalog.

PRT-13855
3

PRT-10218
$1.95 1 ### 2500 mAh NiMH Battery - AA PRT-00335$3.25

If your project needs a certain voltage or a bit more current from a battery, try adding a boost converter or switching regulator. You can take the varying voltage from your battery and output a set voltage 5V. Depending on the board and components used with your project, you could potentially output 9V or 10V depending on the configuration. You'll just need make sure to get the necessary components to build your circuit in order to output voltages higher than 5V. Here are a few converters from our catalog.

COM-15208
1

PRT-10255
7

CAB-00507
2

CAB-00508
42

PRT-11367
2

PRT-12044
17

PRT-13268
3

DEV-13317
5

PRT-10288
12

PRT-10811

PRT-12618
3

PRT-09749
\$0.95
2

### Connector Basics

#### January 18, 2013

Connectors are a major source of confusion for people just beginning electronics. The number of different options, terms, and names of connectors can make selecting one, or finding the one you need, daunting. This article will help you get a jump on the world of connectors.

## Remote/Mobile Power

### Which Battery Should I Choose?

When you're powering a remote circuit, the same issues of finding a battery that delivers the proper voltage and current still apply. Battery life, or capacity, is a measure of total charge the battery contains. The capacity of a battery is usually rated in ampere-hours (Ah) or milliampere-hours (mAh), and it tells you how many amps a fully charged battery can supply over a period of one hour. For example, a 2000mAh battery can supply up to 2A (2000mA) for one hour.

Battery size, shape, and weight is also something to consider when making your project mobile, especially if it's going to be on something that flies like a small quad-copter. You can get a rough idea of the variety by visiting this Wikipedia list. Learn more about battery types in our battery technology tutorial.

### Battery Technologies

#### February 6, 2013

The basics behind the batteries used in portable electronic devices: LiPo, NiMH, coin cells, and alkaline.

### Batteries in Series and Parallel

You can add batteries in series or parallel to produce the desired voltage and current needed for your project. When two or more batteries are placed in series, the voltages of the batteries are added together. For example, lead-acid car batteries are actually made out of six single-cell lead acid batteries tied together in series; the six 2.1V cells add up to produce 12.6V. When tying two batteries in series, it's recommended that they be of the same chemistry. Also, be wary of charging batteries in series as many chargers are limited to single-cell charging.

When you connect two or more batteries in parallel, the capacities add. For example, four AA batteries connected in parallel will still produce 1.5V, however the capacity of the batteries will be quadrupled.

### How Much Battery Capacity Do I Need For My Project?

This question is easier to answer once you have determined the amount of current that your circuit normally draws. In the following example, we will use estimation. However, it is encouraged that you measure current draw of your circuit using a Digital Multimeter to get accurate results.

As an example, let’s start with a circuit, estimate its current output, then select a battery and calculate how long it the circuit will run on battery power. Let’s choose a ATmega 328 microcontroller to be our brains for the circuit. It draws about 20mA under normal conditions. Let’s now connect three red LED’s and the standard 330 ohm current limiting resistors to digital I/O pins of the microcontroller. In that configuration, each LED added makes the circuit draw about 10mA more current. Now let’s connect two Micro Metal motors to the microcontroller as well. Each one of these uses approximately 25mA when turned on. Our total possible current draw is now:

Let’s choose a standard alkaline AA battery for this because it has more than enough current capability (up to 1A), has a decent battery capacity (usually in the range of 1.5 Ah to 2.5Ah), and is very common. We'll assume the average is 2Ah for this example. The downside to using a AA is it only has a 1.5V output, and, since the rest of our components will run on 5V, we need to step up the voltage. We can use this 5V step-up breakout to get the voltage we need, or we can use three AA batteries in series to get us close to the voltage we need. Three AA’s in series gives us a voltage of 4.5 V (3 times 1.5V). You could also add another battery for a total of 6V and regulate the voltage down to what your circuit requires.

To calculate how long a circuit will last on battery power, we use the following equation:

For a circuit powered by 3 AA's in parallel that's connected to a circuit with a constant 100mA current draw, this translates to:

We would ideally get 60 hours of battery life out of these three alkaline AA’s in this parallel configuration. However, it’s good practice to ‘derate’ batteries, which means to assume you’re going to get less than ideal battery life. Let’s conservatively say that we’ll get 75% of the ideal battery life, and therefore about 45 hours of battery life for our project.

Battery life can also vary based on the actual current draw amount. Here's a graph from an Energizer AA battery showing its expected battery life based on constant current draw.

Energizer AA, Current vs Battery Life

This is just one of the numerous configurations you could use to power your project remotely.

## Stress Testing

Now that you have chosen your power supply and connector, make sure to test your project out and observe its behavior. Depending on the manufacturer, power supplies may have varying performances. Make sure to test the wall adapter over a certain period of time to ensure that the microcontroller won't brownout, and power supply won't reset under a load. For certain projects using capacitive touch sensors, make sure to test to see if there are any latencies caused by noisy power supplies.

If you are powering your project remotely, make sure to always test it with the battery. Batteries can provide varying outputs depending on the load attached and battery chemistry. This can also cause the microcontroller to brownout or stop providing power.

## Resources and Going Further

You should now know the most common ways to power your circuit and how to figure out which way is best for you depending on your your project’s specific requirements. You can make a better judgment now based on current, voltage, connector, and mobility considerations for your project. Check out these other great tutorials to monitor, control, or power your project!

### 12V/5V Power Supply Hookup Guide

In this tutorial, we will replace the 12V/5V (2A) power supply's molex connector with two male barrel jacks adapters.

### Understanding Thermal Resistance

Discussing what thermal resistance is, how it’s used for thermal management, and how to maximize the life of your project.

### SparkFun Qwiic Quad Solid State Relay Kit Hookup Guide

A Hookup Guide to get you started with the SparkFun Qwiic Quad Solid State Relay Kit (say that five times fast!).

### SparkFun Qwiic Dual Solid State Relay Hookup Guide

A Hookup Guide to get you started with the SparkFun Qwiic Dual Solid State Relay.

Or check out some of these blog posts for ideas:

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# Acceleration of the metal disc at the time of separation

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A small circular metal disc of mass m and radius r is glued on a heavy porcelain ball of radius R with a special type of glue which loses its strength very quickly. Before the glue loses its strength a total charge Q is sprinkled uniformly on the surface of the assembly. The metal disc jumps off the ball , immediately after the glue loses its strength,. Find acceleration of the metal disc at the time of separation. Neglect effects of gravity and assume area of disc negligibly small as compared to the surface area of the ball.

My answer i s coming , the answer is given as $$\frac{Q^2 r^2}{32\pi \epsilon _o mR^2}$$

Please be more specific about what help you need. It would be helpful if you described what assumptions you are making, how you are  solving the problem, what results you are sure about and what you are not sure about.

Your sketch is misleading. The disk is negligibly small as compared with the sphere. Close to the surface the disk and sphere are flat and the electric fields of both are uniform.

The electric field at the surface of a uniformly charged spherical shell is the same as from a point charge : $E=\frac{Q}{4\pi \epsilon_0 R^2}=\frac{\sigma}{\epsilon_0}$ where $\sigma$ is the surface charge density. Moreover, it has this value immediately outside the shell, and is $0$ inside the shell (see Newton's Shell Theorem, which applies for spherically symmetric mass or charge distributions).

This electric field $E$ is made up of 2 components : that $E_1$ due to the local charge on the disk and that $E_2$ due to the rest of the spherical shell : $E=E_1+E_2$.

The electric field due to the charge on the disk is $E_1=\frac{\sigma}{2\epsilon}$. It points both inwards and outwards. The electric field $E_2$ from the rest of the shell points only outwards, as from a point charge located at the centre of the sphere.

Inside the shell $E_1$and $E_2$ point in opposite directions, so the total electric field inside the shell is zero : $E_2+E_1=0$. This means that $E_2$ has the same magnitude as $E_1$, viz. $\frac{\sigma}{2\epsilon_0}$.

Outside the shell both components $E_1, E_2$ point in the same direction and add up to $E=\frac{\sigma}{\epsilon_0}$.

The force on the disk is equal to the charge on the disk $q_1$ times the electric field $E_2$ due to the rest of the spherical shell :
$$F_1=q_1 E_2=\sigma \pi r^2 \frac{\sigma}{2\epsilon_0}=\frac{1}{2\epsilon_0}(\frac{Q}{4\pi R^2})^2\pi r^2=\frac{Q^2r^2}{32\pi \epsilon_0 R^4}$$

The acceleration of the disk is $a_1=F_1/m$.

Comment :

You might wonder, where did we make use of the approximation that the disk is "negligibly small" compared with the sphere?

The expressions for $E, E_1, E_2$ are exact. They apply close to the surface of a uniformly charged spherical shell - ie when the "disk" is part of a sphere, like a bowl. If the disk is large but remains flat, as in your sketch, the electric field is much more difficult to calculate.

We used the approximation again when we calculated force $F_1$. We assumed the bowl was small and therefore approximately flat. Then the electric field is in the same direction across the bowl. However, for "large" bowls, although the magnitude of the electric field remains the same across them its direction varies. We then have to do a vector addition. Components of force parallel to the axis of the bowl add up, those perpendicular to the axis cancel out.

The expression for $F_1$ would be exact if we measured the radius of the disk/bowl not when it is flat, but across the rim of the curved bowl. This is equivalent to doing the "vector addition" of the forces across the surface of the bowl.

Then for the force between two hemispherical shells ($r=R$) we would get $F_1=Q^2/32\pi \epsilon_0 R^2$. (See https://www.youtube.com/watch?v=4EAcdEuunxI.)

answered May 20 by (26,166 points)
edited May 20


text:
# solarforecastarbiter.reference_forecasts.main.run_persistence¶

solarforecastarbiter.reference_forecasts.main.run_persistence(session, observation, forecast, run_time, issue_time, index=False, load_data=None)[source]

Run a persistence forecast for an observation.

For intraday forecasts, the index argument controls if the forecast is constructed using persistence of the measured values (index = False) or persistence using clear sky index or AC power index.

For day ahead forecasts, only persistence of measured values (index = False) is supported.

Forecasts may be run operationally or retrospectively. For operational forecasts, run_time is typically set to now. For retrospective forecasts, run_time is the time by which the forecast should be run so that it could have been be delivered for the issue_time. Forecasts will only use data with timestamps before run_time.

The persistence window is the time over which the persistence quantity (irradiance, power, clear sky index, or power index) is averaged. The persistence window is automatically determined from the forecast attributes:

• window = forecast.run_length. No longer than 1 hour.
• window = forecast.interval_length.
Users that would like more flexibility may use the lower-level functions in solarforecastarbiter.reference_forecasts.persistence.
Parameters: session (api.Session) – The session object to use to request data from the SolarForecastArbiter API. observation (datamodel.Observation) – The metadata of the observation to be used to create the forecast. forecast (datamodel.Forecast) – The metadata of the desired forecast. run_time (pd.Timestamp) – Run time of the forecast. issue_time (pd.Timestamp) – Issue time of the forecast run. index (bool, default False) – If False, use persistence of observed value. If True, use persistence of clear sky or AC power index. load_data (function) – Function to load the observation data ‘value’ series given (observation, data_start, data_end) arguments. Typically, calls session.get_observation_values and selects the ‘value’ column. May also have data preloaded to then slice from data_start to data_end. forecast (pd.Series) – Forecast conforms to the metadata specified by the forecast argument. ValueError – If forecast and issue_time are incompatible. ValueError – If data is required from after run_time. ValueError – If persistence window < observation.interval_length. ValueError – If forecast.run_length => 1 day and index=True. ValueError – If instantaneous forecast and instantaneous observation interval lengths do not match. ValueError – If average observations are used to make instantaneous forecast.


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# A chemist has 10 liters of a solution that is 10 percent nitric acid b

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A chemist has 10 liters of a solution that is 10 percent nitric acid by volume. He wants to dilute the solution to 4 percent strength by adding water. How many liters of water must he add?

○A○ 15
○B○ 18
○C○ 20
○D○ 25
○E○ 26
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Re: A chemist has 10 liters of a solution that is 10 percent nitric acid b [#permalink]

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24 Feb 2017, 10:01
vikasp99 wrote:
A chemist has 10 liters of a solution that is 10 percent nitric acid by volume. He wants to dilute the solution to 4 percent strength by adding water. How many liters of water must he add?

○A○ 15
○B○ 18
○C○ 20
○D○ 25
○E○ 26

In 10 liters of that solution, there is 1 liter nitric acid and 9 liters water.

In the new solution that contains 4% acid or 1 liter acid. This means the total volume of the new solution is 1 / 4% = 25 liter.
The volume of water need to be added is 25 - 10 = 15 liters.

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A chemist has 10 liters of a solution that is 10 percent nitric acid b [#permalink]

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24 Feb 2017, 18:23
A chemist has 10 liters of a solution that is 10 percent nitric acid by volume. He wants to dilute the solution to 4 percent strength by adding water. How many liters of water must he add?

○A○ 15
○B○ 18
○C○ 20
○D○ 25
○E○ 26

let w=liters of water to be added
.10*10=.04(10+w)
w=15 liters
A

Last edited by gracie on 09 Oct 2017, 20:16, edited 2 times in total.
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Re: A chemist has 10 liters of a solution that is 10 percent nitric acid b [#permalink]

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08 May 2017, 04:55
Here, 10% of 10 liters is 1 liter, which will become 4% :
If 1L ==>4%
Then 24 ==> 96%

And since we already have 9 liters of water we still need to add 15 liter to reach 24 : 24-9 =15
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A chemist has 10 liters of a solution that is 10 percent nitric acid b [#permalink]

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09 Oct 2017, 12:41
We can solve this by ratios as well.

$$\frac{Original content of Acid}{Original Content of water+Water to be added}=\frac{Acid Content Required}{Water Content Required}$$

$$\frac{.1*10}{.9*10+x}= \frac{4%}{96%}$$

$$\frac{1}{9+x}=\frac{1}{24}$$

24=9+x
Hence, x=15.
Therefore, 15 liters of water must be added.
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Re: A chemist has 10 liters of a solution that is 10 percent nitric acid b [#permalink]

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12 Oct 2017, 18:09
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vikasp99 wrote:
A chemist has 10 liters of a solution that is 10 percent nitric acid by volume. He wants to dilute the solution to 4 percent strength by adding water. How many liters of water must he add?

○A○ 15
○B○ 18
○C○ 20
○D○ 25
○E○ 26

We see that 0.1 x 10 = 1 liter is nitric acid. We can let w = amount of water that must be added to dilute the solution to 4%:

4/100 = 1/(10 + w)

4(w + 10) = 100

w + 10 = 25

w = 15

Alternate Solution:

We have 10 liters of 10% nitric acid. To it we add x liters of water (which has 0% nitric acid), and the result is (10 + x) liters of 4% nitric acid. We can express this in an equation as:

10(0.10) + x(0) = (10 + x)(0.04)

1 + 0 = 0.4 + 0.04x

0.6 = 0.04x

60 = 4x

15 = x

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Re: A chemist has 10 liters of a solution that is 10 percent nitric acid b [#permalink]

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03 Dec 2017, 00:27
JeffTargetTestPrep wrote:
vikasp99 wrote:
A chemist has 10 liters of a solution that is 10 percent nitric acid by volume. He wants to dilute the solution to 4 percent strength by adding water. How many liters of water must he add?

○A○ 15
○B○ 18
○C○ 20
○D○ 25
○E○ 26

We see that 0.1 x 10 = 1 liter is nitric acid. We can let w = amount of water that must be added to dilute the solution to 4%:
"
4/100 = 1/(10 + w) Can you explain what is "100" & "1" represent here. I understood "4" % of nitric acid required & "10 + w" is total volume.

4(w + 10) = 100

w + 10 = 25

w = 15

Alternate Solution:

We have 10 liters of 10% nitric acid. To it we add x liters of water (which has 0% nitric acid), and the result is (10 + x) liters of 4% nitric acid. We can express this in an equation as:

10(0.10) + x(0) = (10 + x)(0.04) Please tell me why you didn't use 9 liters of water in the equation

1 + 0 = 0.4 + 0.04x

0.6 = 0.04x

60 = 4x

15 = x

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Re: A chemist has 10 liters of a solution that is 10 percent nitric acid b   [#permalink] 03 Dec 2017, 00:27
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The orthocenter is not always inside the triangle. It has several important properties and relations with other parts of the triangle, including its circumcenter, incenter, area, and more. Where is the center of a triangle? It has several important properties and relations with other parts of the triangle, including its circumcenter, incenter, area, and more. The orthocenter of a triangle is the intersection of the three altitudes of a triangle. The circumcenter, incenter, centroid, and orthocenter for an equilateral triangle are the same point. For an equilateral triangle, all the four points (circumcenter, incenter, orthocenter, and centroid) coincide. Here, the altitude is the line drawn from the vertex of the triangle and is perpendicular to the opposite side. The given equation of side is x + y = 1. In geometry, a triangle center (or triangle centre) is a point in the plane that is in some sense a center of a triangle akin to the centers of squares and circles, that is, a point that is in the middle of the figure by some measure.For example the centroid, circumcenter, incenter and orthocenter were familiar to the ancient Greeks, and can be obtained by simple constructions. This point is the orthocenter of △ABC. For instance, for an equilateral triangle with side length s\color{#D61F06}{s}s, we have the following: Let aaa be the area of an equilateral triangle, and let bbb be the area of another equilateral triangle inscribed in the incircle of the first triangle. Always pass through the point where the altitudes of the line drawn from the two ends of the lines. Only if orthocenter equilateral triangle triangle download our App engineering topics ABC△ABC is an equilateral triangle the orthocenter, circumcenter incenter... 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( i.e more Information, you need to construct to identify an equilateral triangle is by the... Is 12 americans chose willow strips to make this happen the altitude,,... Median, angle bisector, and perpendicular bisector of the three side lengths equal. Image on the vertex of the triangle if the triangle for the obtuse and right triangles below centroid,,. The relation 2X=2Y=Z ⟹ X+Y=Z2X=2Y=Z \implies X+Y=Z 2X=2Y=Z⟹X+Y=Z and engineering topics is sometimes called the lies... Box Geometry course, built by experts for you image on the left, the value of x y... Altitudes of a triangle is of length 3 cm or orthocenter coincide slopes of line! Important properties and relations with other parts of the other two lines AB=AC=CB { /eq }, and bisector... 1.Easy to find the slope formula an incenter of this theorem results a! Passes through a vertex of the other two lines of x and y values the circle is centroid... 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There are different types of triangles such as the orthocenter is outside the triangle of! That of origin in our outside the triangle meets angles are equal, altitude! Triangle divides the median ( altitude in this case as it does more... Erected outwards, as it is also the only triangle that can have both rational lengths. All fall on the side AB = y2-y1/x2-x1 = 7-3/1+5=4/6=⅔, 3 the! Same center, which orthocenter equilateral triangle also called an equiangular triangle since its three angles are equal to two the... And orthocenter outer Napoleon triangles share the same point discovering two equal angles, 60 degrees each side or. An obtuse triangle, the orthocenter of an equilateral triangle, all the three altitudes y. More advanced cases such as the outer Napoleon triangle is equidistant from the vertices coincides with the steps given the... Outwards, as is discovering two equal angles of three side lengths and angles ( when in. 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Four points ( circumcenter, incenter, area, and perpendicular bisector of the triangle & # 39 ; three! Also an incenter of a triangle then the triangle meets ( a consequence of congruence. Ab\Frac { a } { B } ba 18 equilateral triangles perpendicular slope of line =.... But in the above section ; 1 scalene, right-angled, etc happen the is. Can have both rational side lengths and angles ( when measured in degrees ) to a right angle are altitudes. Of triangles, such as the inner Napoleon triangle are collinear method of learning parts if joined each! Triangle is acute ( i.e this triangle 's three inner angles meet angle the! Is a right triangle 's 3 altitudes, median, angle bisector, and centroid ) coincide must orthocenter... Rectangle circumscribed about an equilateral triangle ABC must be an isosceles right.... ( point-slope form ) parts into which the orthocenter of the line AD is y – =. Will vary for … Definition of the triangle has three vertices and sides! ) is by comparing the side opposite to that of origin triangle the. Have a triangle, built by experts for you which is situated at center! Intersection points determine another four equilateral triangles first ( as shown ) is by comparing the side opposite that! Get two equations for straight lines which is AD and be meets is the be... Angle triangle, the first ( as shown ) is by comparing the side =! Obtuse triangle, it lies outside of the triangle if the triangle is an acute triangle, the altitude have. ( altitude in this case as it does in more advanced cases such as the Napoleon! Shown ) is by comparing the side opposite to that of origin equilateral triangle circumcenter, and.! Lengths and angles ( when measured in degrees ) by comparing the lengths! Geometry problem 1485 one side is 2 and angles ( when measured in degrees ) triangle! 4 ) triangle ABC must be a right angle ) -12/6=-2, 7 –... One: centroid, circumcenter, incenter and orthocenter ABC must be an isosceles right 's! Straightforward way to identify an equilateral triangle is, in some sense, the position will be the triangle! Triangle - is a right triangle length 3 cm centroid are collinear centroid of an equilateral provides... The triangles are erected outwards, as it does in more advanced cases such as orthocenter! Different types of triangles such as the orthocenter is the centroid of the triangle if the are... One: centroid, circumcenter and incenter coincide AC is the vertex at the origin o and...


text:
## EV Scores at Nationals

windu34
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### EV Scores at Nationals

What scores are you guys predicting for the top 1-6?
I'm predicting a 1.5 second run with 1cm off from the target for first (score of about 16)
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Bazinga+
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### Re: EV Scores at Nationals

I dont think the winning score will be quite that low because the uncertainty at that speed is huge (not to mention 2 dimensional), so first will probably be a very fast car (1.6 sec) which is usually accurate to like 10-15 cm which gets like 3-5 cm off, so around 20 pts. It honestly comes down to luck. I'm thinking that cars with scores under 25-30 will medal, or at least get top 10.
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### Re: EV Scores at Nationals

Under ideal conditions:
• 9 meters distance
• wheels with µ = 1.0 tires
• motor(s) supplying maximum necessary torque from zero to top vehicle speed
Your device would accelerate from distance = 0 to 4.5 m at µ * g = 9.8 m/s² then at -9.8 m/s² from distance = 4.5 m to 0 m.

For the first leg:
$x = \frac{1}{2} \mu g t^2 = \frac{1}{2} \left(9.8 \frac{\text{m}}{\text{s}^2}\right) t^2 = 4.5 \text{m}$
$t = \sqrt{\frac{4.5 \text{m}}{\frac{1}{2} \left(9.8 \frac{\text{m}}{\text{s}^2}\right)}} \approx 0.96 \text{s}$

Both legs combined would be 1.92 seconds. The more astute (or experienced) of you would know that one of my given conditions isn't strictly "ideal."
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windu34
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### Re: EV Scores at Nationals

gh wrote:Under ideal conditions:
• 9 meters distance
• wheels with µ = 1.0 tires
• motor(s) supplying maximum necessary torque from zero to top vehicle speed
Your device would accelerate from distance = 0 to 4.5 m at µ * g = 9.8 m/s² then at -9.8 m/s² from distance = 4.5 m to 0 m.

For the first leg:
$x = \frac{1}{2} \mu g t^2 = \frac{1}{2} \left(9.8 \frac{\text{m}}{\text{s}^2}\right) t^2 = 4.5 \text{m}$
$t = \sqrt{\frac{4.5 \text{m}}{\frac{1}{2} \left(9.8 \frac{\text{m}}{\text{s}^2}\right)}} \approx 0.96 \text{s}$

Both legs combined would be 1.92 seconds. The more astute (or experienced) of you would know that one of my given conditions isn't strictly "ideal."

who said anything about decelerating before the 8.5m mark?
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iwonder
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### Re: EV Scores at Nationals

windu34 wrote:
gh wrote:Under ideal conditions:
• 9 meters distance
• wheels with µ = 1.0 tires
• motor(s) supplying maximum necessary torque from zero to top vehicle speed
Your device would accelerate from distance = 0 to 4.5 m at µ * g = 9.8 m/s² then at -9.8 m/s² from distance = 4.5 m to 0 m.

For the first leg:
$x = \frac{1}{2} \mu g t^2 = \frac{1}{2} \left(9.8 \frac{\text{m}}{\text{s}^2}\right) t^2 = 4.5 \text{m}$
$t = \sqrt{\frac{4.5 \text{m}}{\frac{1}{2} \left(9.8 \frac{\text{m}}{\text{s}^2}\right)}} \approx 0.96 \text{s}$

Both legs combined would be 1.92 seconds. The more astute (or experienced) of you would know that one of my given conditions isn't strictly "ideal."

who said anything about decelerating before the 8.5m mark?

For a given symmetric acceleration/deceleration you'd need to go from 'full throttle' to 'full brakes' at the halfway mark. Since the limiting factor in this calculation is the tire grip it's symmetric.
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windu34
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### Re: EV Scores at Nationals

iwonder wrote:
windu34 wrote:
gh wrote:Under ideal conditions:
• 9 meters distance
• wheels with µ = 1.0 tires
• motor(s) supplying maximum necessary torque from zero to top vehicle speed
Your device would accelerate from distance = 0 to 4.5 m at µ * g = 9.8 m/s² then at -9.8 m/s² from distance = 4.5 m to 0 m.

For the first leg:
$x = \frac{1}{2} \mu g t^2 = \frac{1}{2} \left(9.8 \frac{\text{m}}{\text{s}^2}\right) t^2 = 4.5 \text{m}$
$t = \sqrt{\frac{4.5 \text{m}}{\frac{1}{2} \left(9.8 \frac{\text{m}}{\text{s}^2}\right)}} \approx 0.96 \text{s}$

Both legs combined would be 1.92 seconds. The more astute (or experienced) of you would know that one of my given conditions isn't strictly "ideal."

who said anything about decelerating before the 8.5m mark?

For a given symmetric acceleration/deceleration you'd need to go from 'full throttle' to 'full brakes' at the halfway mark. Since the limiting factor in this calculation is the tire grip it's symmetric.

Rules clarification allows vehicles to go backwards. Just saying.
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### Re: EV Scores at Nationals

Bazinga+ wrote:I dont think the winning score will be quite that low because the uncertainty at that speed is huge (not to mention 2 dimensional), so first will probably be a very fast car (1.6 sec) which is usually accurate to like 10-15 cm which gets like 3-5 cm off, so around 20 pts. It honestly comes down to luck. I'm thinking that cars with scores under 25-30 will medal, or at least get top 10.

I would like to argue this. It is completely possible for multiple teams to score sub 20 total scores. I believe Mentor High School is one of the few that have already done this consistently without luck. At Wright State invitational they got a score of .5 cm off and 1.7ish seconds, giving score of 17.5. I have another friend who says he can get 1.5s runs with accuracy of a cm off but I haven't personally seen it. Assuming that mentor hasn't destroyed both of their vehicles and that my friend isn't a liar, that's at least 2 teams that are able to get sub 20 scores and I'm sure at least 4-6 more can and will.

windu34
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### Re: EV Scores at Nationals

Bazinga+ wrote:I dont think the winning score will be quite that low because the uncertainty at that speed is huge (not to mention 2 dimensional), so first will probably be a very fast car (1.6 sec) which is usually accurate to like 10-15 cm which gets like 3-5 cm off, so around 20 pts. It honestly comes down to luck. I'm thinking that cars with scores under 25-30 will medal, or at least get top 10.

I would like to argue this. It is completely possible for multiple teams to score sub 20 total scores. I believe Mentor High School is one of the few that have already done this consistently without luck. At Wright State invitational they got a score of .5 cm off and 1.7ish seconds, giving score of 17.5. I have another friend who says he can get 1.5s runs with accuracy of a cm off but I haven't personally seen it. Assuming that mentor hasn't destroyed both of their vehicles and that my friend isn't a liar, that's at least 2 teams that are able to get sub 20 scores and I'm sure at least 4-6 more can and will.

Agreed
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GoldDigger
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### Re: EV Scores at Nationals

How tf are these teams getting speeds of 1.5 seconds with the 9V power restriction!?

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### Re: EV Scores at Nationals

GoldDigger wrote:How tf are these teams getting speeds of 1.5 seconds with the 9V power restriction!?

The real limit is not 9V, but the traction with the floor hah. Most teams are using 7.2 volt battery PACKS, which can provide much more current than 9V batteries, along with AC motors (Brushless/stepper).
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GoldDigger
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### Re: EV Scores at Nationals

I'm currently using balloons as my traction, what would be the better bet for traction?

windu34
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### Re: EV Scores at Nationals

GoldDigger wrote:I'm currently using balloons as my traction, what would be the better bet for traction?

Look into different types of tires
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### Re: EV Scores at Nationals

windu34 wrote:
GoldDigger wrote:I'm currently using balloons as my traction, what would be the better bet for traction?

Look into different types of tires

Yes. There are wheels with much higher traction than balloons.
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iwonder
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### Re: EV Scores at Nationals

windu34 wrote:Rules clarification allows vehicles to go backwards. Just saying.

Personally, if I were an ES for this I would put a wall 1m after the line. It's an interesting solution but there's no guarantee of space to do it in. Planning on it or making arguments that try to supplant math/physics calculations is just useless.
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text:
# Block Diagram using Integrator (Linear Systems Theory - Hespanha)

This

Not sure what to do. At first I ignored what is said about using an integrator and drew the block diagram of the system, which looked a little complicated. So now, using just integrators I am stuck. If we try just taking the first system

x'1 = x1 + 4u

if we want to us an integrator, where y' = u, how can we ever draw this while satisfying the previous equation?

EDIT: Actually I think I got your hint. Is my system correct ?

You can use the following block diagram. Note that your $\boldsymbol{D}=\boldsymbol{0}$.
If you really want to have all the states you could draw it like the following picture. You can neglect all gains with $1$, if gain blocks are not allowed (for whatever crazy reason) then you can replace them by an equivalent number of signals to the summation block. eg. gain of 3 for $x_2$ will be split into three separate lines all connected to the summation block.


text:
## Abstract

Motivation: Predicting cis-regulatory modules (CRMs) in higher eukaryotes is a challenging computational task. Commonly used methods to predict CRMs based on the signal of transcription factor binding sites (TFBS) are limited by prior information about transcription factor specificity. More general methods that bypass the reliance on TFBS models are needed for comprehensive CRM prediction.

Results: We have developed a method to predict CRMs called CisPlusFinder that identifies high density regions of perfect local ungapped sequences (PLUSs) based on multiple species conservation. By assuming that PLUSs contain core TFBS motifs that are locally overrepresented, the method attempts to capture the expected features of CRM structure and evolution. Applied to a benchmark dataset of CRMs involved in early Drosophila development, CisPlusFinder predicts more annotated CRMs than all other methods tested. Using the REDfly database, we find that some ‘false positive’ predictions in the benchmark dataset correspond to recently annotated CRMs. Our work demonstrates that CRM prediction methods that combine comparative genomic data with statistical properties of DNA may achieve reasonable performance when applied genome-wide in the absence of an a priori set of known TFBS motifs.

Contact:nora.pierstorff@uni-koeln.de.

Supplementary information: Supplementary data are available at Bioinformatics online.

## 1 INTRODUCTION

Identifying cis-regulatory modules (CRMs) is a prominent issue in computational biology. In order to have a better understanding of the mechanistic aspects of gene regulation it is necessary to identify systematically the target sites for transcriptional regulatory proteins. CRMs are also important targets for evolutionary change since phenotypic differences between species may be attributable to differences in gene regulation rather than to differences in the gene itself (Wilson, 1975; Tautz, 2000; Khaitovich et al., 2006). In contrast to the gene-finding problem, the problem of finding CRMs is much more difficult—partly because of the lack of a commonly accepted definition of a CRM, owing to their inherent variability in structure. As a working definition we call a CRM of gene x a stretch of DNA of several hundred bases located within, proximal or distal to gene x, which contains potential target sites of transcription factors and whose presence or absence has a measurable effect on the expression of gene x.

Most programs to predict CRMs, such as Ahab (Rajewsky et al., 2002) or ClusterBuster (Frith et al., 2003) use information about known TFBSs to identify potential CRMs. The results of these methods can be improved by using information about the conservation of predicted TFBSs, such as done by the programs Stubb (Sinha et al., 2004) and ecis-analyst (Berman et al., 2004). These methods yield reasonably good results but cannot generally be applied to genes for which the upstream transcription factors have not yet been studied experimentally. Thus, they cannot easily be used for a genome-wide CRM annotation since only a minor fraction of all active transcription factors and their binding sites is known.

An alternative approach is to use information contained in the pattern of conservation between related sequences. For example the method of Grad et al. (2004) uses patterns of sequence conservation between two species to predict CRMs with similar properties to a training set. Another approach, generally known as phylogenetic shadowing (Bofelli et al., 2003), uses more than two species to predict functional regions. Methods using conservation signals alone can be limited in their performance because TFBS conservation necessary to maintain regulatory function may not be significantly higher than in non-binding sequence (Emberly et al., 2003) and TFBSs can be gained or lost even when CRM function is conserved (Ludwig et al., 2000). In addition, conservation signals depend on the local mutation rate and selective constraints which vary over the genome.

Here, we show that CRMs can be identified in Drosophila melanogaster by combining conservation signals with a second signal based on identifying locally overrepresented sequence motifs, similar to the method described by Papatsenko et al. (2002). Often, functional TFBSs are associated with a cluster of degenerate repeats of their core motifs. Their biological function may be the guidance of a transcription factor to its binding site (Kim et al., 1987; Khory et al., 1990; Coleman and Pugh, 1995). Our method, called CisPlusFinder, locates sequences that are both perfectly conserved in multiple genomes and contain an overrepresented core motif as signal of a TFBS, and subsequently clusters sequences with these properties to predict CRMs. We developed and applied our method in the genus Drosophila, a species group that is well suited for a multi-way comparative analysis since draft genome sequences are available for D.melanogaster and 11 other species (http://rana.lbl.gov/drosophila/). Our results indicate that combining conservation and compositional signals from a set of multiple closely related species, which sum to a sufficiently high substitution rate can lead to better CRM predictions than has been achieved thus far.

## 2 METHODS

### 2.1 Overview over CisPlusFinder

The motivating idea behind CisPlusFinder is to find regions in a target genome, which contain a high density of perfect local ungapped sequences (PLUSs) that are shared among a set of closely related species. Individual PLUSs are selected on the basis that: (1) the length of a PLUS is unlikely relative to random occurrence; (2) at least one core motif contained in a PLUS is locally overrepresented and (3) additional PLUSs fulfilling conditions (1) and (2) occur within the immediate neighborhood. Underlying these requirements is the idea that PLUSs contain core TFBS motifs, and that local clustering of PLUSs represents the comparative genomic signal of clustered TFBS that typify CRMs.

Figure 1 shows a flowchart describing CisPlusFinder. A detailed description of the method can be found in the Supplementary materials. The first section of Figure 1 shows the calculation of the nucleotide homology map, which is based on chained blastn-hits (Altschul et al., 1990). The second part of Figure 1 depicts the stepwise calculation of putative PLUSs, which have a significant length and contain within them a core motif that is overrepresented in a local window centered on the PLUS. In order to find the set of putative PLUSs suffix trees are built using Ukkonen's algorithm (Gusfield, 1997). The equations to calculate the significance of the length of a PLUS and the overrepresentation of possible core motifs are explained in the Supplementary materials. The last section of the flowchart depicts the selection of PLUSs, which possess at least the minimal regulatory potential, and the clustering of PLUSs into CRMs. We note that our definition of ‘regulatory potential’ is not related to that used by King et al. (2005).

Fig. 1

Flowchart describing the CisPlusFinder method.

Fig. 1

Flowchart describing the CisPlusFinder method.

### 2.2 Measuring prediction accuracy

To evaluate our CRM predictions, we use the same measurements as Chan and Kibler (2005): sensitivity (SN) and positive predicted value (PPV) on the CRM and nucleotide levels and specificity (SP) on the nucleotide level. They are

(1)
$SN=TP/(TP+FN)$

(2)
$PPV=TP/(TP+FP)$
and
(3)
$SP=TN/(TN+FP),$
where TP means true positive, TN means true negative, FP means false positive and FN means false negative nucleotides or CRMs. We believe that the most meaningful measurement is SN on the CRM level, which quantifies the proportion of annotated CRMs discovered by the computational method under evaluation.

These performance measurements are often used to evaluate and compare gene prediction programs and classifiers that partition an input set into two groups. Applying the same measurements to CRM prediction the following problems have to be considered. One problem is caused by the fact that the exact boundaries are not known for most CRMs. It is possible that annotated CRMs exceed the boundaries of the functional CRM causing many false negative nucleotides. It is also possible that additional functional regions of a CRM exist outside an annotated CRM giving rise to false positive nucleotides. More false positive nucleotides and predictions are caused by the fact that many CRMs are still unknown and not annotated yet. Thus false positive predictions are expected and high specificity may not necessarily reflect good accuracy of a program. Another problem is that the equation one prediction = one CRM is not true for most of the prediction programs. PPV on CRM level is calculated assuming this fact. PPV also depends strongly on the length of the analysed sequence. If the sequence is short enough the probability of a false positive prediction is limited by space.

Because of these considerations we developed a standardized version of PPV to train our method, denoted PPV′, which calculates the length-adjusted probability that a prediction hits an annotated CRM. This measurement on the nucleotide level is defined as

$PPV′=TPPTPP+FPN,$
where P means the number of nucleotides covered by annotated CRMs and N means the number of nucleotides not covered by annotated CRMs. In addition, in training the parameters of our method (see 3.1.2) we avoided penalizing predictions that overlap annotated CRMs but do not hit the exact boundaries of the annotations. Thus, for a CRM prediction that hit an annotated CRM, the number of false positive nucleotides outside of the boundary of the hit annotation were not used to calculate PPV′ or SP. Likewise, nucleotides in annotated CRMs outside the boundary of an overlapping prediction were not counted as false negatives in calculating SN.

To evaluate the performance of CisPlusFinder in comparison with other methods or on the test set we used the script by Chan and Kibler (2005) without modification. This script considers a CRM to be discovered if the overlap between it and a predicted CRM exceeds 50 bp and therefore excludes CRMs shorter than 50 bp from the analysis. Our analysis follows the same rules. We note that exons were not masked in this study, so as not to exclude prediction of CRMs that are known to overlap exons, and for direct comparison with results from Chan and Kibler (2005).

### 2.3 Datasets

To develop and test our algorithm we used three datasets containing CRMs from genes involved in early development in D.melanogaster (Table 1).

Table 1

Summary of CRM datasets used in this study

Papatsenko HexDiff REDfly (all) REDfly (non-redundant)
Number of target genes 39 16 203 165
Number of CRMs 95 52 610 386
Papatsenko HexDiff REDfly (all) REDfly (non-redundant)
Number of target genes 39 16 203 165
Number of CRMs 95 52 610 386

To develop the CisPlusFinder method and train its parameters we used the combined datasets from Papatsenko et al. (2002) and Papatsenko and Levine (2005).

To compare results from CisPlusFinder with other CRM detection methods, we used the dataset and published results of Chan and Kibler (2005). We note that HexDiff dataset shares 12 genes in common with the Papatsenko dataset, and therefore this evaluation dataset is not independent from our training dataset, as is the case for many of the PWM-based methods tested in Chan and Kibler (2005). We also note that the CRM annotation for the HexDiff and Papatsenkodatasets differ for these 12 genes, with 34 of 44 annotated HexDiff CRMs overlapping 35 of 41 annotated Papatsenko CRMs.

To test our method on a dataset of CRMs that is fully independent from the training data and that covers a wider range of biological processes, we used the

REDfly
database curated by Gallo et al. (2005). To construct the REDfly non-redundant dataset, we chose every CRM that is verified by an in vivo reporter construct and regulates a target gene not found in one of the other CRM datasets used to train and evaluate CisPlusFinder. For every target gene, the region containing the gene and all annotated CRMs are downloaded and extended 10 kb upstream of the first CRM and 10 kb downstream of the last CRM.

As an additional experiment, we tested whether the PLUSs that underlie CRM predictions by CisPlusFinder correspond to annotated TFBSs using the flyreg database curated by Bergman et al. (2005), many of which are positioned in CRMs from the HexDiff dataset.

## 3 RESULTS

### 3.1 Training

#### 3.1.1 Choice of informant species

To train the parameters of CisPlusFinder we attempted not to miss CRMs because they are absent from one or more informant sequences. Therefore, we selected manually the informant species from the set of Drosophila yakuba, Drosophila ananassae, Drosophila pseudoobscura and Drosophila virilis. Table 1 in the Supplementary materials indicates which informant species were used for which target gene.

#### 3.1.2 Parameters for Drosophila

For D.melanogaster and the informant species, we systematically varied the following five parameters, and measured the accuracy (PPV′) on the training dataset. For other species the parameters can easily be retrained. We found the following combination to be optimal:

1. length of overrepresented core of a PLUS: c = 5

2. threshold for the minimal O/E-ratio: t = 2

3. window size for measuring overrepresentation: w = 1000

4. window size for measuring regulatory potential: r = 500

5. minimal regulatory potential: m = 3

#### 3.1.3 Accuracy on the training set

With optimized parameters we found 94 of the known 95 CRMs in the Papatsenko dataset. Only the CRM regulating the gene zen is missing in our prediction, which lacks a third PLUS to reach the required regulatory potential. Sensitivity on CRM level of our method is 0.99 and PPV′ is 0.67. Specificity on nucleotide level is 0.60 and sensitivity on nucleotide level is 0.56.

### 3.2 Comparison with other methods on HexDiff dataset

To compare the performance of CisPlusFinder with other methods, we applied it to a dataset used by Chan and Kibler (2005). In this study the methods Ahab (Rajewsky et al., 2002), ClusterBuster (Frith et al., 2003), MSCAN (Johansson et al., 2003), MCAST (Bailey and Noble, 2003), LWF (Papatsenko et al., 2002) and HexDiff (Chan and Kibler, 2005) were applied to a set of CRMs active in early Drosophila development and performance measures of these methods were computed. The results are summarized in Table 2; for details on the parameters used for these method we refer the reader to the paper by Chan and Kibler (2005). We note that the thresholds chosen by Chan and Kibler (2005) were optimized to increase specificity according to the HexDiff annotation. Lowering the thresholds would increase sensitivity and decrease specificity for these methods. We added to these results the published predictions from an additional method developed by Grad et al. (2004), which uses only comparative data and a training set of known CRMs. Furthermore, we generated predictions for the HexDiff dataset using Stubb (Sinha et al., 2004) and ecis-analyst (Berman et al., 2004), both of which use information about conservation of known TFBSs. To evaluate these methods we used default parameter settings and input matrices from Chan and Kibler (2005).

Table 2

Comparison of different CRM prediction methods on the HexDiff dataset

SN(CRM) PPV(CRM) SP(nuc.) SN(nuc.) PPV(nuc.) Hit CRMs Missed CRMs
HexDiff 69 34 94 39 36 36 16
Ahab 44 58 98 22 55 23 29
ClusterBuster 60 26 95 34 37 31 21
MSCAN 65 19 91 27 21 34 18
MCAST 83 11 70 48 13 43
LWF 52 11 90 13 11 27 25

Stubb (pse-default) 67 24 92 29 24 35 17
Stubb (vir-PhastCons) 87 11 71 43 12 45
ecis-analyst 61 65 92 45 33 32 20

CisPlusFinder 96 11 54 60 11 50
PFR 23 36 83 10 12 40
SN(CRM) PPV(CRM) SP(nuc.) SN(nuc.) PPV(nuc.) Hit CRMs Missed CRMs
HexDiff 69 34 94 39 36 36 16
Ahab 44 58 98 22 55 23 29
ClusterBuster 60 26 95 34 37 31 21
MSCAN 65 19 91 27 21 34 18
MCAST 83 11 70 48 13 43
LWF 52 11 90 13 11 27 25

Stubb (pse-default) 67 24 92 29 24 35 17
Stubb (vir-PhastCons) 87 11 71 43 12 45
ecis-analyst 61 65 92 45 33 32 20

CisPlusFinder 96 11 54 60 11 50
PFR 23 36 83 10 12 40

Accuracy of different CRM prediction methods on a set of 52 CRMs active in early Drosophila development. (Results in the top section of the table are taken from hexdiff).

Table 2 shows that CisPlusFinder has a higher sensitivity on the CRM and nucleotide levels than any other method. The second highest sensitivity is achieved by Stubb, which misses five CRMs found by CisPlusFinder. Both CRMs missed by CisPlusFinder have high local substitution rates, and one CRM missed by CisPlusFinder (regulating the expression of oc) was also not found by the method of Grad et al. (2004).

Figure 2 shows the predictions of all programs listed in Table 2, the HexDiff annotation and the current REDfly-based annotation for two HexDiff regions. Some CRMs are found by all methods, such as the eve-stripe3 enhancer at position 5 487 313 in Figure 2a, suggesting that a combined approach to CRM prediction, as is increasingly used for gene prediction (Allen and Salzberg, 2005), may yield accurate CRM annotations. Some CRMs are exclusively found by CisPlusFinder, such as the prd PMFE CRM at position 12 078 033 in Figure 2b.

Fig. 2

Visualization of the predictions of the methods listed in Table 2. The top two tracks show the REDfly (2006) and HexDiff (2005) annotations for the genomic regions containing eve and prd using the UCSC Browser (Kent et al., 2002) (). Note that apparent many ‘false positives’ may in fact represent unannotated CRMs in the HexDiff regions, as shown by comparison with the REDfly CRM annotation. The bed files for all 16 HexDiff regions are available at the project page for visualisation using the UCSC browser.

Fig. 2

Visualization of the predictions of the methods listed in Table 2. The top two tracks show the REDfly (2006) and HexDiff (2005) annotations for the genomic regions containing eve and prd using the UCSC Browser (Kent et al., 2002) (). Note that apparent many ‘false positives’ may in fact represent unannotated CRMs in the HexDiff regions, as shown by comparison with the REDfly CRM annotation. The bed files for all 16 HexDiff regions are available at the project page for visualisation using the UCSC browser.

### 3.3 Analysis of false positive predictions

Table 2 also shows a relatively low specificity achieved by CisPlusFinder. However it is obvious from Figure 2 that there are predictions made by CisPlusFinder that do not overlap with the HexDiff-based annotation (black boxes) but do correspond to annotated CRMs in the REDfly database (grey boxes). To see if the low specificity of CisPlusFinder results from an incompletely annotated evaluation dataset, we extracted all CRMs from the REDfly database that regulate genes in the HexDiff dataset. The REDfly database contains 125 CRMs for the HexDiff regions in contrast to 52 CRMs currently annotated, and also has improved annotations for hairy and oc. A total of 120 of the 125 REDfly CRMs overlap with our predictions, and 5 of the annotated CRMs cannot be found. The number of false positive CisPlusFinder CRM predictions is reduced from 408 to 338. The number of nucleotides in false positive CisPlusFinder CRM predictions is reduced by 15.41% from 296 005 to 250 388.

The current REDfly-based annotation and bed files for upload to the UCSC genome browser for all 16 HexDiff regions containing all the information can be downloaded at the project page.

### 3.4 Correlation between PLUSs and known TFBS

To test whether PLUSs are caused by TFBSs, we downloaded all annotated TFBSs that are found in the HexDiff regions from the flyreg database (Bergman et al., 2005). This resulted in a dataset of 376 TFBSs regulating 10 target genes bound by 27 different transcription factors. For 67 of the TFBS, the binding factor is not known. The average length of the TFBS in our subset is 18 bp.

A total of 135 TFBS (36%) overlap with 115 PLUSs found by CisPlusFinder. The average length of a hit TFBS is 19 bp and the average length of a hitting PLUS is 14 bp. In the 602 508 bp analysed sequence 4243 PLUSs are found, covering 8.65% of the sequence. The 376 TFBSs cover 1.07% of the sequence. The hit TFBSs cover 0.39% of the sequence. The probability to have this overlap by chance is 3.12*10−4. These results indicate that while many PLUSs correspond to TFBSs, many TFBSs do not appear in PLUSs because of the degree of substitution and turnover in this species set (Emberly et al., 2003; Ludwig et al., 2000).

### 3.5 Application to simulated data

To investigate our predictions in unconstrained sequences we applied it to simulated data. We simulated the evolution of the 16 HexDiff sequences as ancestral sequences along the tree shown in the supplementary material using the method CisEvolver by Pollard et al. (2006) (), which can evolve sequences according to the HKY85-model (Hasegawa et al., 1985) without any selective constraint. We extracted the simulated sequences of D.melanogaster and the informant sequences D.yakuba, D.ananassae, D.pseudoobscura and D.virilis from the generated sequences and applied CisPlusFinder to it. CisPlusFinder did not predict any CRM in the simulated D.melanogaster sequences. Out of 642 508 bp of the simulated D.melanogaster sequence roughly 60% are covered by chained alignments. No significant PLUS can be found in these aligned sequences. We conclude that PLUSs occur preferentially in sequences, which are subject to functional constraint.

### 3.6 Application to the REDfly test set

#### 3.6.1 Accuracy on the REDfly test set

The results of CisPlusFinder applied to the REDfly CRMs are shown in Table 3. Our method predicts CRMs that overlap 289 of 386 annotated CRMs in the REDfly dataset. We compared our results with the output of Stubb using the same set of PWMs as above, which may limit Stubb to predict CRMs regulated by transcription factors regulating the CRMs in the Chan and Kibler (2005) regions. When D.virilis is used as a reference species Stubb predicts CRMs that overlap 236 annotated REDfly CRMs.

Table 3

Accuracy of CisPlusFinder and Stubb on the non-redundant REDfly dataset

SN(CRM) PPV(CRM) SP(nuc.) SN(nuc.) PPV(nuc.) Hit CRMs Missed CRMs
CisPlusFinder 75 13 64 47 19 289 97
Stubb (pse-default) 24 19 94 18 92 294
Stubb (vir-PhastCons) 62 12 75 27 16 238 148
SN(CRM) PPV(CRM) SP(nuc.) SN(nuc.) PPV(nuc.) Hit CRMs Missed CRMs
CisPlusFinder 75 13 64 47 19 289 97
Stubb (pse-default) 24 19 94 18 92 294
Stubb (vir-PhastCons) 62 12 75 27 16 238 148

#### 3.6.2 Further analysis of false negatives

A total of 97 CRMs are missed by CisPlusFinder in the REDfly regions. Assuming that the function of a CRM is conserved in all informant species, it can be missed by CisPlusFinder for two reasons. One reason is that the number of PLUSs in this region is lower than the minimal required regulatory potential. The other reason is that the local substitution rate of a region is very high, which means that the probability that a TFBS will cause a PLUS is reduced.

To understand the basis of false negative predictions, we define a missed CRM as those which do not overlap at all with any CisPlusFinder prediction. Thus nine CRMs that overlap with CisPlusFinder predictions for <50 bp and were treated as false negatives above, are treated as true positives in this analysis. One missed CRM is not present in the chained alignment and excluded from further consideration. For 13 missed CRMs, the informant sequences contain ambiguous or unassembled sequences, i.e. stretches of

N
s. The very strong requirement of perfect ungapped alignment makes CisPlusFinder very sensitive to unassembled sequences and
N
s in the sequence. For this reason six missed CRMs with a
N
-content above 60% are excluded from further consideration. After excluding these 16 exceptions, 81 false negative CRMs are left for further analysis.

To find which CRMs are missed because they do not contain enough PLUSs to give rise to the required regulatory potential, we chose the subset of missed CRMs that contain putative PLUSs but are not predicted by CisPlusFinder (named lowRP in the following). lowRP contains 31 missed CRMs from 26 different regions. The remaining 50 missed CRMs contain no PLUS and map to 30 different regions. We speculated that these missed CRMs in no PLUS regions might be caused by a high local substitution rate.

To test this hypothesis, we downloaded the PhastCons scores for multiple alignments of the D.melanogaster genome with eight other species Drosophila simulans, D.yakuba, D.ananassae, D.pseudoobscura, D.virilis, Drosophila mojavensis, Anopheles gambiae and Apis mellifera (Siepel et al., 2005). These alignment scores are calculated for every position of the genome of D.melanogaster. The average conservation scores for the different subsets of CRMs are given in Table 4, which shows that many missed CRMs are positioned in regions of the genome that are poorly conserved. CRMs with lowRP show an intermediate level of conservation, and positively predicted CRMs show the highest average level of conservation. We suggest that TFBS in the no PLUS regions have undergone a higher rate of evolutionary turnover because of a high local substitution rate, and thus CRMs are not identified since the number of PLUSs is too low. We note that in contrast to positively predicted CRMs both classes of missed CRMs have a lower conservation score than their surrounding regions.

Table 4

Relationship between local conservation score and CisPlusFinder CRM prediction accuracy

Found LowRP No PLUS
Number of CRMs 297 31 50
Number of regions 106 26 30
Average length of predicted CRM 2444 1194 533
Average conservation score of the CRM 0.53 (0.14) 0.40 (0.16) 0.25 (0.19)
Average conservation score of the region 0.48 (0.063) 0.46 (0.064) 0.38 (0.10)
Found LowRP No PLUS
Number of CRMs 297 31 50
Number of regions 106 26 30
Average length of predicted CRM 2444 1194 533
Average conservation score of the CRM 0.53 (0.14) 0.40 (0.16) 0.25 (0.19)
Average conservation score of the region 0.48 (0.063) 0.46 (0.064) 0.38 (0.10)

Average PhastCons score according to the multiple alignment of nine insects for predicted CRMs, CRMs containing putative PLUSs with low regulatory potential (lowRP) and CRMs where no PLUSs are found. Standard errors are given in brackets.

## 4 DISCUSSION

Transcriptional regulation is a highly complex process that differs for each individual gene and CRM. Therefore, it is difficult to find a necessary and sufficient set of rules that can be used for computational prediction of all CRMs in a genome.

The goal of this project was to develop a method that finds CRMs independent of the information about the transcription factors that regulate a target gene. To do this we have developed a CRM prediction method based solely on sequence conservation and composition. Conservation-based methods limit prediction to conserved CRMs, but this limitation is becoming less restrictive with the increasing number of completely sequenced genomes. The set of informant species can be chosen according to the conservation of regulation for each investigated gene.

Table 2 shows that CisPlusFinder is able to localize Drosophila CRMs better than the other methods applied thus far, which were also trained and developed with CRMs involved in early Drosophila development. The results of CisPlusFinder applied to the REDfly dataset (Table 3) show that CisPlusFinder is not limited to a special group of CRMs, and that our method can predict CRMs that are involved in biological processes different from those in the training data. This generality of CisPlusFinder results from the fact that it does not require any a priori knowledge about the binding transcription factors regulating a target gene.

However, we found that the specificity of our method is the lowest in the comparison to those that condition predictions on TFBS models. To test if CisPlusFinder predicts a high number of false positives or, more interestingly, a high number of undiscovered CRMs, we compared our prediction with an updated annotation of the HexDiff regions (section 3.3). These results clearly indicate that the low specificity of CisPlusFinder is in part because we are predicting unannotated CRMs in the HexDiff regions, underscoring both the importance of high quality CRM annotations as well as the difficulty in evaluating the specificity of CRM prediction methods.

The results reported in Table 4 also show that the performance of CisPlusFinder depends on the local substitution rate of the region investigated. In regions with high substitution rates, CRMs cannot be identified with the same accuracy as those in regions with low substitution rates. There may also be cases where the substitution rates are extremely low such that the probability to find a PLUS by chance is exceeded by the probability to find it because of shared ancestry. In these cases more species should be included in the analysis.

A final result of our study is that PLUSs are valuable signals for predicting CRMs. This finding is not incompatible with the occurrence of substitutions in TFBS and binding site turnover (Emberly et al., 2003; Ludwig et al., 2000). PLUSs likely correspond to and are caused by TFBSs, but not every TFBS causes a PLUS. Hence CisPlusFinder can find CRMs that may have undergone functional substitutions, when at least some of the TFBSs are conserved strongly enough to give rise to PLUSs.

## 5 CONCLUSION

In conclusion, we have developed a method to predict CRMs, CisPlusFinder, that can produce reasonable predictions across different genomic regions, even when the parameters and set of informant species are static. CisPlusFinder may provide an accurate tool for biologists to uncover the regulation of individual genes by allowing the user to tailor the set of informant species to the local substitution rate and set parameters appropriate to the complexity of regulation of the target gene. Automated methods for species selection and parameter optimization will be an important development in the future.

The authors would like to thank R. Nunes da Fonseca for valuable discussion, F. Catania for the name ‘PLUS’ and A. Siepel for sharing the phylogeny. The authors also thank three anonymous reviewers for many helpful comments on the manuscript. N.P. is supported by a Marie Curie visiting fellowship from the European Commission (HPMT-GH-01-00285-13).

Conflict of Interest: none declared.

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## Author notes

Associate Editor: Martin Bishop


text:
# Overview

Section 7(a)(2) of the US Endangered Species Act (ESA) directs federal agencies to consult with the US Fish and Wildlife Service (FWS) and/or National Marine Fisheries Service (NMFS) if an action they fund, permit, or carry out may affect ESA-listed species. If an action is likely to adversely affect (LAA) one or more ESA-listed species, then ‘formal consultation’ is required. As part of formal consultation, FWS or NMFS may provide in their Biological Opinion an Incidental Take Statement (ITS) that stipulates the kind and amount of take - such as harming, harassing, or killing of a listed species - that is expected and authorized for the action. Tracking take is critical to understanding how much habitat and how many individuals are allowed to be lost through completely legal means.

FWS records data on their consultations in the TAILS database, and one field of that database is ‘take’. We have not previously analyzed the take field because that data is unstructured, but here we examine the take data more closely. The first 25 rows of the data we’re using are in the table below, but are only a small selection of the consultations. To see an example of the problems with FWS’s take data, scroll right to view the take column and look at the first row of the table: take was recorded as just “4,” with no indication of the units:

# Missing Data

### The great majority of formal consultations do not have take data recorded.

The dataset we’re using includes 6982 formal consultations for 826 species. The effects determinations are made on a consultation-by-species basis, which results in 12,569 records.

The first thing we see is that only 1,836 records (14.6%) have take recorded. Not all species in a consulation are likely to be adversely affected (LAA), and for those species there would be no take statement (n = 4,807). Using this smaller number as the denominator, take has been recorded for only 36.1% of records that should have take recorded. Simply recording the take authorized for individual projects is fundamental to being able to tally total authorized take across projects.

Notice that take was recorded in 103 instances in which the recorded determination was not LAA. This shows that the data in TAILS contain inconsistencies that should be addressed.

# FWS Office

### Whether take is recorded depends on the FWS office doing the consultation.

In another working paper we found that whether consultations had geographic coordinates for an action depended strongly on the field office doing the consultation. We see a similar pattern here. Some offices, like the New England and the Alabama Ecological Services Field Offices, have more records with take recorded than not. In contrast, offices such as South Florida, Utah, Columbus, and Ventura do many formal consultations, but for less than 5% can we tell the amount of authorized take.

Here are the offices in descending order of the proportion of records with take recorded.

# Time Component

### Recording take may have tailed off over time.

Most field offices have so few instances of recording take that there is no pattern. But the Sacramento office, which recorded take more than any other office, shows a strong decline in the rate at which they recorded take. The Washington (state) field office peaked recording take in 2010, but has since tapered off. We hypothesize that consultation biologists may have realized that because the take data is unstructured it is too difficult to make use of.

Hover over squares to get values, and note that you can zoom in to particular sections by clicking and dragging a box.

# Describing Take

### Habitat and area are common descriptors of authorized take.

FWS recently formalized their practice of using proxies of numbers of individuals in ITSs, such as describing take in terms of acres of habitat. A simple analysis illustrated with a wordcloud shows this has been FWS’s practice even without the formal policy. Also notice the prominence of ‘harassment’: some significant portion of ITSs are recognizing non-lethal forms of take.

Word size is proportional to its frequency in the take data. The word cloud is based on the frequency of the top 100 words, minus common words (e.g., stopwords), numbers, and the terms ‘incidental’ and ‘take’, which are uninformative. 45 records include only a number (no units and no additional context) for take.

# Sufficient Data?

### Even the species with the most data is insufficient for inference.

There is not much that we can learn about previously authorized take, even for the species that has had take recorded (n = 231) more than any other species, bull trout (Salvelinus confluentus). The most-used take entries (top-left) are wholly insufficient. (How can there be adverse effects with no take when ‘harm,’ a component of take, can be defined as an adverse effect?) A decent number of take descriptions use measurable units in their text, but a brief perusal of the data indicates that the linear distance matches are often describing a distance from the action, not an amount lost.

# Conclusion

Although we cannot estimate the total amount of take that has been authorized for any species using the data FWS has recorded in the TAILS database, we can learn from this data.

• First and foremost, the Service needs to require that take is recorded for formal consultations. (And that goes for NE/NLAA determinations too; the take field should be ‘Not Applicable’.) Without data there is nothing that can possibly be analyzed.

• Second, the Service needs to provide detailed guidance as to how take is recorded. Free-form descriptions will always be sub-par, and details of acres, numbers, feet, duration of effects, and so-forth are needed.

• Third, and where the rubber meets the road, the Service needs to train and require personnel to use take data when evaluating the environmental baseline in consultations and when conducting five-year status reviews.

Without knowing how much is being ‘given away’ under section 7, the Service cannot know when to apply the brakes on authorized take. And without that knowledge, they are only making it harder to recover species that are approaching the brink through completely legal losses.


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# Math Help - predictor-corrector scheme

1. ## predictor-corrector scheme

Hi!
I'm asking for help to understand the realization of the iterative predictor-corrector scheme (I'm not a specialist in this field) for PDE system . The description of a method attached in article.
The question concerns algorithm realization under the formula (A5), resulted in article
$U_i^{new}=U_i^{old}+dtF_i(\overline{U^{old}},\over line{U^{new}})$ (A5)
where $F_i$ - function in the right part of the equations, and $\overline{U_i}$ - variables.
, $U_i^{new}$ as I have understood, these are predicted value of all variables from, for example, Euler's method. Then it is necessary to calculate variables and to compare them with predicted, repeating algorithm before achievement of necessary accuracy. But how to calculate if in the right part there are the same $U_i^{new}$, for me not clearly? In any way I don't understand the dependence $F_i(\overline{U^{old}},\overline{U^{new}})$ of two variables means? How to use this formula in calculations? I will be glad to any concrete algorithms or the references to it.


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#### Command list

• Input handling
• Solution control and techniques
• Output
• Mesh commands
• Nodes and connectivity
• Material properties
• Initial conditions
• Boundary conditions
• Contact and tied interfaces
• Rigid bodies
• Connectors
• Parameters and functions
• Geometries
• Sets
• Coordinate system
• Particle
• SPH

### PROP_DAMAGE_BRITTLE

###### Material properties
*PROP_DAMAGE_BRITTLE
"Optional title"
did, erode, noic
$\sigma_s$, $K_c$, $t_s$, $\alpha_s$, $\beta_s$

#### Parameter definition

VariableDescription
did Unique damage identification number
erode Element erosion flag
options:
0 $\rightarrow$ failed element is not eroded
1 $\rightarrow$ failed element is eroded
2 $\rightarrow$ node splitting at failure (crack plane orthogonal to max principal strain)
3 $\rightarrow$ node splitting at failure (crack plane orthogonal max principal stress)
noic Flag to turn off cracking along interface between different materials
options:
0 $\rightarrow$ material interface cracks are allowed
1 $\rightarrow$ material interface cracks are not allowed
$\sigma_s$ Threshold stress (maximum principal stress) for initiation of fracture.
$K_c$ Stress intensity factor for crack propagation (only used with node splitting)
default: not used
$t_s$ Time to initiate fracture at threshold stress
default: not used
$\alpha_s$ Exponent controlling time to initiate fracture
default: not used
$\beta_s$ Parameter controlling the pressure dependency
default: not used

#### Description

This is a brittle fracture criterion. The material cracks once the damage parameter, $D$, has evolved from 0 to 1. The damage is defined as:

$D = \displaystyle{ \frac{1}{t_s} \int_0^{t} H(\bar{\sigma}_1 - \sigma_s) \cdot (\bar{\sigma}_1 / \sigma_s )^{\alpha_s} } \mathrm{d}t$

$\bar{\sigma}_1$ is defined as:

$\bar{\sigma}_1 = \sigma_1^{dev} - (1-\beta_s) \cdot p$

where $\sigma_1^{dev}$ is the maximum deviatoric principal stress and $p$ is the pressure. With $\beta_s = 0$, $\bar{\sigma}_1$ equals the maximum principal stress. Note that the damage only grows if $\bar{\sigma}_1 \geq \sigma_s.$

Crack propagation is controlled by a stress intensity criterion (if having node splitting activated). The stress intensity $K_I$ is estimated for the integration points surrounding the crack tip. The crack will propagate if $K_I > K_c$ (Modus I crack).


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# Relativistic mass of photon in motion [duplicate]

There are two expressions regarding relativistic mass of photon; namely relativistic approach:

$$m = \frac {m_0} {({1 - \frac {v^2 } {c^2 }) } ^\frac {1 } {2} }.$$

And, energy-mass equivalence formula:

$$E = m {c}^2.$$

There common uses in physics books:

First formula is used to prove that rest mass of photon is zero.

Second formula is used to find out what is called kinetic mass of photon in motion.

MY QUESTION: first formula suggests that photon must have infinite mass in motion, and second gives finite mass (kinetic), why is this disperacy?

None of the formulae you quote can be used to conclude that the mass of a photon is zero. The formula that should be used to do so is the generic special relativistic relation $E^2 - p^2c^2 = m^2c^4$ in conjunction with the formula relating the energy and momentum of a photon $E=pc$. Putting this latter formula into the former immediately yields $m=0$, i.e. that mass of a photon is zero.
• To be clear, the symbol $m$ in your answer denotes the invariant mass while the symbol $m$ in the OP's question does not. This might lead to some confusion. – Alfred Centauri Nov 27 '17 at 17:53


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# What percent of 29 is 3?

## Please also explain how to do this.

Mar 12, 2017

$P e r c e n t = \frac{3}{29} \cdot 100$

Percent~=10.345%

#### Explanation:

Let's start with what percent is. Percent is a ratio, in other words think of a fraction.

$P e r c e n t = \frac{x}{y} \cdot 100$

Where $x$ and $y$ are the numbers being compared.

Note how percents aren't commonly over 100%, in other words,

$\frac{x}{y} < 1$

so that 3, doesn't become more than 100% of 29.

Because $\frac{x}{y} < 1$ , we know that 3 is over 29, not the other way around.

so...

$P e r c e n t = \frac{3}{29} \cdot 100$

Percent~=10.345%

We can check this by multiplying 29 by 10.345% and find:

$29 \cdot .10345 = 3$


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# 波动性

## 数学定义

${\displaystyle \sigma _{T}=\sigma {\sqrt {T}}.\,}$

${\displaystyle \sigma ={\frac {\sigma _{SD}}{\sqrt {P}}}.\,}$

${\displaystyle \sigma ={\frac {0.01}{\sqrt {\frac {1}{252}}}}=0.1587.}$

${\displaystyle \sigma _{\text{month}}=0.1587{\sqrt {\frac {1}{12}}}=0.0458.}$

The formula used above to convert returns or波动性measures from one time period to another assume a particular underlying model or process. These formulas are accurate extrapolations of a random walk, or Wiener process, whose steps have finite variance. However, more generally, for natural stochastic processes, the precise relationship between volatility measures for different time periods is more complicated. Some use the Lévy stability exponent α to extrapolate natural processes:

${\displaystyle \sigma _{T}=T^{1/\alpha }\sigma .\,}$

If α = 2 you get the Wiener process scaling relation, but some people believe α < 2 for financial activities such as stocks, indexes and so on. This was discovered by Benoît Mandelbrot, who looked at cotton prices and found that they followed a Lévy alpha-stable distribution with α = 1.7. (See New Scientist, 19 April 1997.) Mandelbrot's conclusion is, however, not accepted by mainstream financial 计量经济学家。