Exercises English,Exercises Match,Exercises Science: กุมภาพันธ์ 2011

2/19/2554

Physics 736 - Exercises #6

Physics 736 - Exercises #6
1 Instructions
The homework is due on Wednesday, April 7, 2010. Please leave
solutions in Karsten
Heeger's mailbox.
This homework will require the use of some plotting routine and/or
computing pack-
age such as Mathematica, ROOT, etc. Pick your favorite. Mathematica is installed
on the library computers.
Topics: probability density function, Monte Carlo, random numbers, covariance,
maximum likelihood.
2 Questions
1. Random Numbers { Generate random distributions of one thousand 2D and 3D
events. Make 2D and 3D scatter plots to illustrate the random
distribution of your
sequence of 1000 events. Describe what random number generator you or your soft-
ware package uses.
2. Illustrating the Central Limit Theorem { Show that whatever the
initial distribution,
a linear combination of a few variables almost always approximates to a Gaussian
distribution: Add together four random numbers ri , each distributed
uniformly and
independently in the range 0 to 1 to obtain a new variable z =
P
ri. Repeat the
procedure 50 times to obtain a set of 50 zj values. Plot these as a
histogram and
compare it with the appropriate Gaussian distribution. Give the mean
and variance
of this Gaussian distribution.
3. Simulating Radioactive Decay { This is a truly random process. The
probability of
decay is constant. The probability that a nucleus undergoes
radioactive decay in time
t is p, where p = t for t 1. The initial number of nuclei is
N0. Graph the
number of remaining nuclei as a function of time for the following cases:
N0 = 100, = 0:01 s􀀀1, t = 1 sec
N0 = 5000, = 0:03 s􀀀1, t = 1 sec
Show the results on both linear and logarithmic scales. Plot on the
same graphs the
expected decay curves.
4. Simulating Distributions { Write a program that generates the value
according
to the distribution function f() = (sin2 + acos2)􀀀1 in the range 0 2.
Generate 10000 values using a=0.5 and a=0.001. Plot the results for
each and overlay
the distribution curve, f(), properly normalized.
5. Correlation and Error Matrix { Generate 1000 events of uncorrelated
(x,y) values,
each given by a Gaussian distribution with x = 1, x = 2, y = 2, y
= 0:5. Show
a scatter plot of the data. Calculate the error matrix and the
correlation coecient,
, for this data set.
1
Consider the function f(x; y) = 5x + 8y. Evaluate the variance of this function
directly using the data set, and also by using the equation using the
error matrix.
Now rotate the events by 30 degrees around the center of the
distribution and repeat
above exercises.
6. Consider an experiment that measures the lifetime of an unstable
nucleus, N, using
the reaction A ! Ne, N ! Xp. The creation and decay of N is signaled by the
electron and proton. The lifetime of each N, which follows the
probability density
function (PDF) f = 1
e􀀀t= , is measured from the time between observing the elec-
tron and proton with a resolution of t. The expected probability
density function
is the convolution of the exponential decay and the Gaussian resolution:
f(tj; t) =
R 1
0
e
􀀀
(t􀀀t0)2
22
t p
2t
e􀀀t0=
dt0
Generate 200 events with = 1 and t = 0:5 sec. Use the maximum likelihood
method to nd ^ and the uncertainty, ^ . Plot the likelihood function and the
resulting PDF for the measured times compared to a histogram
containing the data.
Automate the maximum likelihood procedure so as to be able to repeat
this exercise
100 times, and plot the distribution of (^ 􀀀 )=^ for your 100
experiments and show
that it follows a unit Gaussian.
For one data sample assume that t is unknown and show a contour plot
in the ; t
plane with constant likelihood lnL = lnLmax 􀀀 1=2.
2

2/18/2554

Physics Exercises: Warming of Water in a Glass

Physics Exercises: Warming of Water in a Glass
Webster's defines physics as a science that deals with matter and
energy and their interactions in fields like mechanics, acoustics,
optics, heat, electricity, and magnetism. The power of physics lies in
describing a great variety of phenomena by a limited set of
fundamental laws and principles. Schecker (1996) observes that
students sometimes fail to distinguish between a fundamental law and a
formula derived in class. For example, all physics students learn the
fundamental law about the acceleration of an object: Force = Mass
times Acceleration.1 Schecker cites an example of a student who
interprets a fundamental law like:
F = m*A
as just another equation of the same quality as
S(t) = 1/2 g t^2
(which is the equation for the free fall of bodies). Schecker feels
that system dynamics modeling could help improve how we learn physics
by shifting our attention away from memorizing formulas. By focusing
on stocks and flows, system dynamics may direct our attention to the
key concept of accumulation. I believe his argument make sense, but
only in systems where the stocks and flows are easily visualized.
These exercises provide an example. We use stocks and flows to keep
track of the heat flows in a glass of water.
Glass of Water
The glass of water in Figure 1 is exposed to a constant air
temperature of 20 ºC. There are 1,000 cubic centimeters (cc) of water
in the glass, and the water temperature is 19 ºC.
Figure 1. Glass of water
Suppose we know that the air temperature remains constant at 20 ºC.
What would you expect the water temperature to be if we come back in a
couple of hours? You might answer 20 ºC, the same as the air
temperature. Or you might think that the water temperature may not yet
be at 20 ºC. But if we wait long enough, it will reach 20 ºC. Now
consider a longer term question: suppose we come back in 6
Andrew Ford BWeb for Modeling the Environment 1
months to measure the volume of water in the glass. Will there still
be 1,000 cc in the glass?
You probably realize that evaporation will gradually remove water from
the glass, so there will be less than 1,000 cc after 6 months. And you
are probably aware that heat is used to evaporate water. If the heat
comes from the internal energy stored in the water, we might wonder if
the water temperature will decline over time? These exercises develop
and test a system dynamics model to simulate whether the water
temperature will rise or fall over time.
Heat Flows and Assumptions
The water at the top of the glass has a radius of 5.64 centimeters
(cm), a circumference of 35.4 cm and an exposed surface area of 100
square centimeters. The glass is a perfect cylinder, so the exposed
surface area will remain constant at 100 square centimeters as the
water evaporates. Initially, the water stands 10 cm high. Water
density is 1 gram/cc, so the initial mass is 1,000 grams. The glass is
0.5 cm thick, and it sits on a well insulated table in a room with a
constant air temperature of 20 ºC and a relatively low humidity.
Evaporation takes place at the rate of 2 feet/year. The latent heat of
evaporation (the heat needed to evaporate the water) is 585 calories
per gram. If we are to study changes in the energy stored in the
water, we might consider simulating four heat flows:
•
Evaporation (top surface): The heat loss due to evaporation depends on
the latent heat of evaporation and the rate at which water evaporates.
Let's include this flow in the model.
•
Conduction (side wall): Heat may flow into the water from conduction
across the side surface where the water touches the glass. Initially,
this surface area is 354 square centimeters. But this surface area
will shrink over time as evaporation removes water from the glass.
Let's include this flow in the model.
•
Conduction (bottom surface): Heat may flow out of the water through
conduction through the bottom of the glass. Let's ignore this flow
since the table is well insulated.
•
Convection (top surface): Heat may flow into the water by convective
forces through the surface area exposed to the air. Let's ignore the
convective flow because it would probably amount to only about 1% of
the conductive flows across the side surface of the glass.
A
Model to Simulate Heat Flows
You know that it's best to "start with the stocks" when building a
flow diagram for a new model. And you know that that the stocks
represent the storage in the system. In this example, we need two
stocks:
• one stock to keep track of the volume of water stored in the glass, and
•
a second stock to represent the internal energy stored in the water.
T
ime will be measured in seconds; volume will be measured in cc; and
any flows affecting the volume of water will be measured in cc/second.
The internal energy content will be measured in calories, and any
flows affecting the energy content will be measured in
calories/second. We know that water will gradually leave the glass
through evaporation, so we need one flow to account for the reduction
in volume as the water evaporates over time. The internal energy
content will be controlled by two flows:
Andrew Ford BWeb for Modeling the Environment 2
• Energy content will be reduced as the water is evaporated. This heat
flow depends on the rate of evaporation and the latent heat of
evaporation.
• Energy content will be increased if heat flows across the side wall
of the glass. This heat flow depends on the temperature difference
across the side wall, the thickness of the glass and the conductivity
of the glass.
We now have two stocks and three flows as shown in Figure 2. The model
is completed by using converters to explain each of the flows.
Figure 2. A model to simulate the temperature of water in the glass.
Most of the variables in Figure 2 are converters. You will probably
notice that many of the variable names are shorter than names in any
of the other models in the book or on the website. I have used short
names for the physics exercise because shorter names will make it
easier for you to check the model equations against equations in your
introductory physics text.
Andrew Ford BWeb for Modeling the Environment 3
Several of the converters in Figure 2 are inputs (like the air
temperature or the density of water). Several are conversion factors.
For example, three converters are used to convert Erate 1 (in
feet/year) to Erate 2 (in cm/sec).
Most of the units are relatively easy to identify from your
introductory course in physics. But the thermal conductivity of glass
requires some extra consideration. The value of "k" is 0.2 calories
per second-degree C per square meter of glass. This means that the
flow across 1 square meter of glass (with a thickness of 1 meter)
would be 0.2 calories per second for each degree of temperature
gradient across the surface.
Table 1 shows the equations for the stocks and flows. Table 2 shows
the equations for the rest of the model.
Internal_Energy_Content(t) = Internal_Energy_Content(t - dt) +
(HeatFlowIn - HeatLoss) * dt
INIT Internal_Energy_Content = 9000
DOCUMENT: internal energy content in calories
INFLOWS:
HeatFlowIn = k*TempDif*SideA2/thick2
DOCUMENT: The heat flow in across the side surface in calories per sec
OUTFLOWS:
HeatLoss = latent_heat_of_evap*evap
DOCUMENT: Heat Loss due to evaporation in calories/second
Volume_of_Water_in_Glass(t) = Volume_of_Water_in_Glass(t - dt) + (- evap) * dt
INIT Volume_of_Water_in_Glass = 1000
DOCUMENT: the volume of water remaining in the glass (in cubic centimeters)
OUTFLOWS:
evap = Erate2*TopA
DOCUMENT: evaporation is measured in cubic centimeters per second
Table 1. Equations for the stocks and flows.
Andrew Ford BWeb for Modeling the Environment 4
AirTemp = 20
DOCUMENT: the ambient air temperature in ºC C
Base_Temp = 10
DOCUMENT: an arbitrary base temperature used to establish the
temperature of the water
circum = 2*PI*radius
DOCUMENT: circumference measured around the top surface of water (cm)
CM_per_FT = 30.5
DOCUMENT: conversion factor - centimeters in a foot
cm_per_m = 100
DOCUMENT: conversion factor -- centimeters in a meter
densityW = 1
DOCUMENT: in grams per cubic centimeter
ERate1 = 2
DOCUMENT: evaporation rate in feet/year
Erate2 = ERate1*CM_per_FT/(HR_per_YR*SEC_per_HR)
DOCUMENT: evaporation rate in cm per second
height = Volume_of_Water_in_Glass/TopA
DOCUMENT: height of the water in the glass (cm)
HR_per_YR = 8760
DOCUMENT: conversion factor - hours in a year
int_energy_concentration = Internal_Energy_Content/massW
DOCUMENT: calories of energy per gram of water; assumes an even
distribution of energy
k = .2
DOCUMENT: k is the conductivity of glass. It has complicated units
latent_heat_of_evap = 585
DOCUMENT: 585 calories are needed to evaporate 1 gram of water
massW = densityW*Volume_of_Water_in_Glass
DOCUMENT: mass of the water in grams
radius = 5.64
DOCUMENT: in cm
SEC_per_HR = 3600
DOCUMENT: conversion factor - seconds in an hour
SideA1 = circum*height
DOCUMENT: area of water along the sides of the glass (square centimeters)
SideA2 = SideA1/(cm_per_m*cm_per_m)
DOCUMENT: side surface area measured in square meters
SpecHeatW = 1.0
DOCUMENT: 1 calorie is needed to raise the temperature of 1 gram of
water by 1 degree C.
This is called the specific heat of water.
TempDif = AirTemp-Water_Temp
DOCUMENT: the difference between the air temperature and the water in ºC C
thick1 = .5
DOCUMENT: thickness of the glass in cm
thick2 = thick1/cm_per_m
DOCUMENT: glass thickness in meters
TopA = PI*radius^2
DOCUMENT: area of the top surface of water in square centimeters
Water_Temp = Base_Temp+(int_energy_concentration/SpecHeatW)
DOCUMENT: water temperature in ºC C
Table2. Equations for the rest of the model.
Andrew Ford BWeb for Modeling the Environment 5
Testing the Model
The simulation begins with 1,000 cc of water and an internal energy
content of 9,000 calories. The internal energy content is defined
relative to a base temperature of 10 ºC Celsius. The water temperature
depends on the Internal Energy Concentration which is measured in
calories per gram. At the start of the simulation, there are 9,000
calories evenly distributed over 1,000 grams, so the concentration is
9 calories per gram. The water temperature equation assumes that zero
concentration corresponds to 10 ºC. The specific heat of water is 1
calorie per gram per degree. In other words, 1 calorie of heat is
required to increase the temperature of a gram of water by 1 degree.
Working from a base of 10 ºC, the energy concentration of 9
calories/gram means that the initial water temperature is 19 ºC.
The heat flow into the glass depends on TempDif, the difference
between the air temperature and the water temperature which is 1
degree at the start of the simulation. The initial heat flow is around
1.4 calories/sec. But the heat loss due to evaporation is much smaller
(only about 0.1 calories/sec). With more heat flowing in than flowing
out, you would expect the internal energy content and the water
temperature to increase. Let's run the model over thousands of seconds
to learn whether the water temperature will eventually reach 20 ºC.
Figure 3 shows the results.
Figure 3. Water temperature over a one-hour simulation.
Remember that time is measured in seconds, so the simulation runs for
3,600 seconds. The comments in ( ) remind us of how many minutes have
passed. The air temperature is constant at 20 ºC, and the water
temperature increases over time. The heat flows are scaled from 0 to 2
calories/second. At the beginning of the simulation, the heat flowing
in is around 1.4 calories/second while the heat loss due to
evaporation is only around 0.1 calories/second. The net inflow is over
1 calorie/second. If these flows were to persist for 1,000 seconds, we
would expect over 1,000 calories to be added to the energy content of
the water--more than enough to increase the water temperature to 20
ºC. But the simulation shows
Andrew Ford BWeb for Modeling the Environment 6
that the temperature never reaches 20 ºC. After 15 minutes, for
example, the water temperature is only up to around 19.7 ºC. There is
still a temperature difference across the glass surface of 0.3 ºC.
This means the heat flowing into the glass is much smaller.
By the 30th minute of the simulation, the heat flowing into the glass
has declined almost to the same value as the heat loss from
evaporation. The water temperature appears to be approaching 20 ºC
after 30 minutes, but it is still not quite the same as the air
temperature. By the 45th minute of the simulation, the heat flows in
and out of the water are nearly equal. And the water temperature
appears to be leveling off slightly below 20 ºC. By the end of the
simulation, the water temperature is 19.92 ºC.
We see a small temperature difference across the surface of the glass,
and it isn't going away! The size and persistence of this temperature
difference is the focus of the exercises.
Exercises with the Model of Water Temperature
1. Verification:
Build the heat flow model in Figure 2 and verify the results in Figure 3.
2. Longer Term Expectations:
What do you expect will happen if we allow the simulation to continue
for another hour or two? Will the water temperature eventually reach
20 ºC? Run the model for two hours to see if the simulation results
match your expectations.
3. Equilibrium Diagram
If the water temperature reaches equilibrium, draw an equilibrium
diagram to confirm that the HeatFlowIn is countered exactly by the
HeatLoss.
4. Thinner Glass:
The 0.5 cm thickness is much thicker than typical glass containers, so
run the model with the thickness set at 0.25 cm. Before performing the
new simulation, pencil in the likely results on a time graph from the
1st exercise. Then run the model with the new value of the thickness.
Do the simulation results match your expectations?
5. Derive the Long Term Temperature Difference:
Write an algebraic expression that will permit you to derive the 0.08
ºC as a combination of the physical parameters (such as the glass
thickness and conductivity). Check the algebraic expression to see if
it gives the same results as in exercises #2 and #4.
Andrew Ford BWeb for Modeling the Environment 7
Andrew Ford BWeb for Modeling the Environment 8
6. Experimental Verification with a Standard Thermometer:
A simple glass thermometer might reveal a temperature difference of
0.2 ºC. Suppose you wish to design an experiment where the expected
temperature difference is at least 0.2 ºC, and you have glasses of
many different thicknesses in your laboratory. How thick must the
glass be to yield a measurable temperature gap?
Reference and End Note
Schecker 1996
Horst Schecker, "Modeling Physics: System Dynamics in Physics
Education," The Exchange, Vol 5, Nu. 2, the newsletter of the Creative
Learning Exchange, 1 Keefe Road, Acton, MA 01720.
1 The formula F = m*A tells us the force that must be applied to an
object to achieve acceleration of the object. Students learn this
formula in introductory physics classes, and they apply it frequently
to find the velocity of an object after application of a force. Their
familiarity with this formula has led some of my previous students to
ask when they get to use the formula in system dynamics modeling. They
are surprised to learn that the formula does not come up in the book
or on the BWeb materials to supplement the book. It is natural to
wonder how one could build useful models and never invoke such an
important and fundamental law of physics. It appears from the examples
in the book that we can simulate the dynamics of environmental and
economic systems without ever invoking the formula F = m*A. In the
Mono Lake case, for example, we know that water flows down the Sierra
Nevada slopes due to the force of gravity. But we do not need to
invoke F = m*A to calculate the annual flow toward Mono Lake. (We have
data from gauges on the streams to tell us the annual flow.) To take a
different example from the book, commercial buildings must be
constructed to withstand the force of gravity, but that does not mean
that F = m*A must appear somewhere in the model of the boom and bust
in real-estate construction. (The effect of gravity is implicit in the
cost of new constructing new buildings.)
The most likely opportunity to make use of F = m*A is the hiking
example on the BWeb. The Let's Go For a Hike Exercise describes a
group of hikers who must apply force to accelerate themselves to a
natural pace for the hike up the mountain. The hikers must overcome
the force of gravity, and they must deal with complicated issues
involving the traction that their hiking shoes achieve on the trail
surface. You might think F = m*A would be useful to help us simulate
their progress up the mountain. But if you interview typical hikers,
you will learn that they have no idea what force is applied as they
accelerate themselves to a good pace. Although the hikers cannot talk
in the language of introductory physics, they can certainly tell you
about their ability to accelerate during the course of the hike. And
they can tell you the natural pace that they prefer to maintain over
most of the hike. The hiking exercise assumes that you have
interviewed the hikers, and you put the interview results to use in
the model.

Derive the expression for the period of a harmonic oscillator with mass m and spring constant k.

1. Derive the expression for the period of a harmonic oscillator
with mass m and spring constant k.
2. Derive the expression for the speed of a deep water wave in
terms of the wave period T.
3. What are sinh, cosh, and tanh in terms of the exponential
function? What is tanh(10-6)? What is tanh(10)?

Derive the expression for the period of a harmonic oscillator with mass m and spring constant k.

Waves

• Physics of waves
• Characteristics of waves
• Generation of waves by storms
• Wave speed - shallow vs. deep ocean
• Sets - dispersion

Can computer programming exercises improve the performance of physics students?

Can computer programming exercises improve the performance of physics students?
Carey Witkov
Physics Instructor
Broward Community College North Campus, 1000 Coconut Creek Pkwy.,
Coconut Creek, FL 33063
INTRODUCTION
Learning physics is a challenge for most students who attempt it.
Physics courses require
extensive problem-solving. Physics problems are usually "word
problems," i.e., students must
translate a verbal description of a problem into mathematical form and
then apply mathematical
operations to solve for the unknown quantities. This method of solving
physics problems is
similar to the method used to program a computer. Hence, it is natural
to ask whether learning to
program a computer would improve the performance of physics students.
The existing educational literature on the utility of computer
programming exercises to improve
student performance has mainly focused on general problem-solving
ability. It is a widely held
belief of computer programming teachers that computer programming
improves students' general
problem-solving abilities. For example, more than 90% of the 60
Information Processing and
Technology teachers who responded to a survey conducted in Queensland,
Australia agreed or
strongly agreed that programming increases students' problem-solving
abilities (King, Feltham
and Nucifora, 1994). There is some experimental evidence to support
this belief. Choi and
Repman (1993) exposed college students to Pascal and FORTRAN and found
an increase in
general problem-solving skills. Salomon and Perkins (1987) also found
some benefit but caution
that "... implementing such conditions on a wide scale may be
difficult, and that programming
instruction must compete with other means of improving cognitive skills."
The strongest argument against trying to improve the performance of
students by programming
exercises is the task-specific nature of learning. The argument is
that teaching students to
program a computer teaches students precisely that, to program a
computer, but not to solve any
other type of problem. Making the programming exercises relevant to
the subject being taught
can minimize this objection. For physics students this objection is
even less applicable because
some physics problems literally require programming a computer to
solve. The study described
in this paper involved physics problems that require a computer to
solve and those that do not.
METHODOLOGY
The primary study involved 16 students enrolled in the General Physics
with Calculus II Lab
course PHY 2049L. A smaller secondary study involved 7 students
enrolled in Applied Physics
PHY1001L. The choice of computer programming language and exercises
were classroomtested
during Term I. Data for the study was collected during Term II. The
Term I experience of
teaching physics students to use the computer language BASIC as a lab
tool suggested that an
alternative computer programming language would be preferable for use
in this study. Physics
students are more familiar with math syntax than computer syntax.
While the syntax of BASIC
is simpler than that of many other programming languages, it was still
found to be too
"computer-like" and less "math-like" for students to feel comfortable
with. A computer
spreadsheet (i.e., Microsoft Excel) offered several advantages. First,
many students already
possessed spreadsheet experience. Second, students who have never used
spreadsheets learned to
be productive with spreadsheets during a single session. Third, most
students appeared to enjoy
using Excel. The same cannot be said about learning to use a
conventional programming
language.
Three exams were given in each lab course. Exams 1 and 2 consisted of
physics problems whose
solution required algebraic or calculus methods. Let's call these
traditional exercises. Exam 3
consisted of programming exercises, i.e., physics problems which
either cannot be solved by
traditional means, (e.g., transcendental equations), or physics
problems for which most of the
students did not yet know a traditional means of solution (e.g., some
types of differential or
difference equations). In the latter case, physics problems for which
students did not yet possess
the math tools to solve by traditional means were converted into
programming problems that can
be easily solved numerically.
RESULTS
For the 16 students, average scores for traditional exercises on exams
1 and 2 were 4.94 and 4.91
out of 6 respectively. The average score for the programming exercises
was 5.34. Exam 1 and 2
scores for traditional exercises were pooled and compared to Exam 3
scores for programming
exercises. An ANOVA analysis of the scores for the two groups gave a
probability of less than
20% that the difference in mean scores was the result of chance.
Hence, in this study,
programming exercises resulted in physics test scores that, with an
80% likelihood, were higher
than traditional exercises. The smaller secondary study using Applied
Physics (PHY1001L)
students (N=7) also showed higher exam scores than traditional
exercises, but the small sample
size did not allow for a meaningful statistical comparison.
An alternative explanation for the higher exam scores using
programming exercises is the
possibility that the exams using traditional exercises were more
difficult than those using
programming exercises. This is unlikely to be the case since the
traditional exercises were
standard physics problems while the programming exercises were
problems that are usually
included in physics textbooks as advanced problems. Hence, programming
exercises made
advanced topics accessible to physics students and still resulted in
higher test scores than
traditional exercises.
CONCLUSION
The results of this study suggest that physics teachers can teach
basic programming skills using a
computer spreadsheet embedded in the physics curriculum and be
confident that students will,
with an 80% likelihood, perform better than using traditional
exercises. Even if students
receiving programming exercises were to perform only as well as those
receiving traditional
exercises, the programming approach offers several additional
benefits. First, topics that are
usually regarded as advanced or too difficult were made accessible by
the use of programming
exercises. This is because programming exercises involve repetition of
simple arithmetic
operations. By this means even algebra-based physics students can
numerically solve a
differential equation that students who have taken a course in
differential equations might not
easily solve by closed-form methods. Moreover, because programming
reduces complex
problems to step-by-step procedures involving simple arithmetic
operations, students fully
understand the mechanics of the solution. Second, the skill of
programming a computer
spreadsheet to solve physics problems is a useful one that can be used
by students in their future
math, science, and engineering courses and employment.
REFERENCES
Choi, W. S. & Repman, J. (1993). Effects of Pascal and FORTRAN
programming on the
problem-solving abilities of college students. Journal of Research on
Computing in Education,
25(3), 290-302.
King, J. A., Feltham, J., & Nucifora D. (1994) Novice Programming in
High Schools: Teacher
Perceptions and New Directions. Australian Educational Computing, 9(2), 17-23.
Salomon, G., & Perkins, D. (1987). Transfer of cognitive skills from
programming: When and
how? Journal of Educational Computing Research, 3(2), 149-169.

2/17/2554

Texas Education Agency. All rights reserved. Reproduction of all or portions of this work is prohibited without express written permission from the Texas Education Agency

1	D
2	H
3	B
4	J
5	C
6	F
7	B
8	F
9	B
10	F
11	C
12	J
13	D
14	H
15	D
16	F
17	D
18	G
19	A
20	2
21	A
22	H
23	A
24	G
25	B
26	H
27	C
28	G
29	A
30	G
31	B
32	F
33	A
34	G
35	A
36	F
37	B
38	H
39	B
40	G

Read the question and choose the best answer. Then mark the circle next to the letter for the answer you have chosen.

1 Which of the following would be safe to do during a lab activity?

A Running in the classroom
B Leaving a water spill on the floor
C Touching hot surfaces
D Following lab rules
2 The wires connecting the battery and the lightbulb create a closed
circuit. What would happen if one of these wires were cut?

F The battery would lose its charge.
G The glass would crack.
H The light would go out.
J The wire would become hot.

2/07/2554

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*

ข้อสอบครูชำนาญการพิเศษ *
ข้อสอบครูผู้ช่วย,สอบบรรจุครูผู้ช่วย,แนวข้อสอบครูผู้ช่วย,สอบครูผู้ช่วย,แนวข้อสอบครูผู้ช่วย,ครูผู้ช่วย,ข้อสอบบรรจุครู,ข้อสอบครูชำนาญการพิเศษ
*http://www.oopps.bloggang.com*
*
* ฟิสิกส์ ข้อสอบฟิสิกส์ บทเรียนฟิสิกส์ ฟิสิกส์ออนไลน์
โจทย์ฟิสิกส์ แบบฝึกหัดวิชาฟิสิกส์ โจทย์วิชาฟิสิกส์ โจทย์วิชาฟิสิกส์
http://thaiphysics.blogspot.com

* บทเรียนบทรัก ความรัก บทความความรัก ดูดวงความรัก นิยามความรัก
กลอนรัก เพลงรักhttp://love8love9.blogspot.com

* ความรัก บทความความรัก ดูดวงความรัก นิยามความรัก กลอนรัก
เพลงรักhttp://kongkoymusic.blogspot.com

Content-Transfer-Encoding: base64

Electronic In-Box Exercises:
Using Technology to Support Student's Intellectual and Aesthetic
Learning about the World of Managers

{author names should not be included in your proposal manuscript, as
it will be blind peer}

Abstract

In this session, we will discuss the use of electronic in-box
exercises and explore how a shift from a traditional pen and paper
simulation to the use of e-mail can make this a dramatically different
learning experience both for professors and students. The presenters
will use their experience with designing, conducting, and evaluating a
fast-paced electronic in-box exercise among 400 undergraduate business
students to launch a discussion of how these exercises create an
innovative teaching methodology that helps students develop both
sensory and intellectual knowledge about the challenges managers face.

--
*

2/05/2554

Ocean Data View – Presentations and Exercises

Exercise 1:

1. Copy the directories under \SeaDataNet\Reiner Schlitzer to a work
directory on your local hard disk.
2. Install the optional package odvmpOP_coastRegional_w32.zip from
http://odv.awi.de. This package contains high resolution regional
coastlines for the Mediterranean, Baltic, North Sea and other regions.
3. Run ODV and open the MedatlasII_Btl collection on your hard disk.
Make the map to use the MeditHR coastlines installed during step 2.
Invoke ODV Help and read the chapter on map Display Options for
instructions on how to do this.
4. In MAP mode produce full screen maps of (a) all stations in
MedatlasII_Btl, (b) of all stations containing data for Phosphate, and
(c) of all stations containing data for Phosphate and Nitrate. Save
the configuration of (a). Use a descriptive name for the
configuration, such as AllStationsMap. How many stations have
Phosphate and Nitrate data?
5. Load the AllStationsMap configuration saved during (4) and make
Display Option changes, e.g., size and color of station dots, map
projection, coastline/bathymetry layers etc. Save your favourite
configuration. Undo last configuration changes.
6. Switch to STATION mode and produce plots with temperature, salinity
and oxygen profiles. Change the window layout so that you only have
two large windows. Change the variables on x and y axis. What are
typical oxygen values in 200 m depth in the Black Sea and
Meditterranean?
7. (Advanced) Specify a station selection rectangle or polygon to
select only stations in a region of interest. Define Potential
Temperature as a derived variable. Switch to SCATTER mode and produce
a large T/S diagram with salinity on x and Tpot on y. Define Potential
Density as second derived variable and define a second window showing
profiles of potential density. Do you see any suspicious data?
8. (Advanced) Switch to STATION mode and undo any regional station
selection from step 7. Switch to SECTION mode and define your
favourite section. Produce section plots of potential temperature,
salinity and oxygen. Also create a T/S diagram of the section data.
Add contour lines to the section plots and produce GIF, PNG or JPG
image output.

--
*

Ocean Data View – Presentations and Exercises

Exercise 2:

1. Create a new directory profileCollection on your local hard disk
and create a new ODV collection in this directory using ODV's File>New
option. Choose a name for the new collection, e.g., MyProfiles. When
prompted for the definition of variables in the collection choose
Medatlas Bottle variables. Note that the newly created collection is
empty, initially.
2. Import some Medatlas bottle data from file b0653311.987 in the
\SeaDataNet\Reiner Schlitzer\importData directory. Use option
Import>Medatlas Format Files>Profile Data>Single File. Then use option
Full Domain from the map's popup menu to adjust the domain covered by
the imported stations. Undo last change before proceeding.
3. (Advanced) Import data from the US NODC World Ocean Database 2005
file OSDO1985.gz. Use option Import>NODC Formats>World Ocean
Database>Single File. On the Import Options dialog note that the WOD
2005 import file provides values for Depth [m] while the collection
stores Pressure [decibars]. Therefore, depths have to be converted to
pressure values during import. Use the ODV help to find out how to do
this. Don't forget to associate or convert other variables as well.
Also note that units for the nutrients in the source file and target
collection appear to be different. Do we need to convert these?
4. (Advanced) Download ARGO float profile data from
http://www.coriolis.eu.org/cdc/argo.htm and import them using
Import>ARGO Formats>Float Profiles (...)>Single File.
5. Create a new directory time-seriesCollection on your local hard
disk and create a new ODV collection in this directory using ODV's
File>New option. Choose a name for the new collection, e.g.,
MyTimeSeries. When prompted for the definition of variables in the
collection choose Medatlas Time Series variables. Note that the newly
created collection is empty, initially.
6. Import some Medatlas current meter data from file
example_medatlas_time_series.dat in the \SeaDataNet\Reiner
Schlitzer\importData directory. Use option Import>Medatlas Format
Files>Time Series Data>Single File. Then use option Full Domain from
the map's popup menu to adjust the domain covered by the imported
stations. Undo last change before proceeding.
7. Produce plot of current speed versus time.

--
*

Ocean Data View – Presentations and Exercises

Exercise 5:

1. Drag the file xbt_noQF.txt from \SeaDataNet\Reiner
Schlitzer\qualityCheck onto the ODV icon. Switch to SCATTER mode and
make sure you have a large plot window with temperature profiles.
2. Note that some XBT locations fall on land. Lets assume you know the
correct longitude/latitude values. Edit the metadata of these XBTs
accordingly using the Edit Header option of the text window popup
menu.
3. Start cleaning the XBT data by applying range checks.
4. By clicking into the data plot try to find profiles that look good
over some depth range but have questionable or bad data above or below
(if you can't find one select station # 1248). Do questionable or bad
quality flagging for the entire set of problematic values. Note that
it may be useful to select a regional subset of stations before doing
the quality control.

--
*

Ocean Data View – Presentations and Exercises

-- Exercise 3:

1. If not already present create an ODV desktop icon.
2. Drag the file data_from_WoceBtl.txt from \SeaDataNet\Reiner
Schlitzer\importData\speadsheet onto the ODV icon. Note that ODV
created a new collection data_from_WoceBtl and imported the data from
the .txt file in a single, automatic step. This was possible because
data_from_WoceBtl.txt adheres to the ODV Generic Spreadsheet format.
Any such file can be simply dragged onto the ODV icon and can be
imported automatically.
3. Drag your own data file onto the ODV icon. Is it imported
automatically? Does it adhere to the ODV Generic Spreadsheet format?
(See help file for format specification.) Ask for help if you are
stuck.

Ocean Data View – Presentations and Exercises

Exercise 4:

1. Drag the file data_from_WoceBtl_compact_noQF.txt from
\SeaDataNet\Reiner Schlitzer\qualityCheck onto the ODV icon. Produce
section plots of Nitrate and Silicate with the unchecked data (Display
Options settings: Gridded Field, VG Gridding, 30 and 25 for X and Y
scale lengths). Save these sections to image files for comparison with
the cleaned sections later.
2. Switch to SCATTER mode and visually quality control the data. Note
that you can use different kinds of plots to identify outliers, e.g.,
profiles, T/S plots or general property/property plots, such as for
instance Phosphate versus Oxygen or Phosphate versus Nitrate. Some
cases are difficult to decide and you may want to look at several
plots simultaneously. Start with quality control of Nitrate and
Silicate, then do the other variables if time permits.
3. Exclude Questionable and Bad data by applying data quality filters
and redo the Nitrate and Silicate sections. Any improvement?
4. Apply visual data quality control to your own dataset.

--
*

Ocean Data View – Presentations and Exercises

Exercise 6:

1. Open the xbt_noQF collection from exercise 5 again.
2. Define the Vertical Derivative of Temperature as a derived variable
and create a window layout with one large plot for temperature
profiles and another window with d/dz Temperature versus depth.
3. In the latter window click on points with very large positive or
negative d/dz Temperature values to identify spikes. Manually edit the
quality flags of the bad data.

--
*

12.) Configure XWindow

Ubuntu uses the Xorg XWindow system for the underlying graphics engine
that drives the Gnome Desktop. Once the Gnome desktop is installed
along with Xorg you need to configure Xorg to work with your hardware
and the resolution you have chosen. Luckily Xorg has made this quite
easy to do. First do:

$ cd
$ sudo Xorg -configure

This should create the file xorg.conf.new. To finalize configuring
your X Server do:

$ sudo cp xorg.conf.new /etc/X11/xorg.conf

Now type:

$ gdm

and your Gnome desktop environment should start. You can log in with
your username and password.

--
*

11.) Install Gnome 2.x

It is actually quite simple to install a graphical desktop on Ubuntu.
By default Ubuntu uses the Gnome desktop. If you wish to use KDE with
Ubuntu there is a separate version of the Ubuntu distribution called
Kubuntu that you can find at www.ubuntu.com.

We have configured your workshop lab so that the files for Gnome are
on a local machine. The installation requires over 400MB of files to
download and over 1GB of total space. Downloading will not take long,
but unpacking and installing will take some time.

The Gnome desktop comes with the Ubuntu meta-package called "ubuntu-desktop".

$ sudo apt-get install ubuntu-desktop

This will now take quite some time. Feel free to go to lunch if it is
time do to that. If you are around when this install prompts you to
pick a default resolution for your Gnome desktop, then you should
choose: 1280x1024.

--
*

10.) Install Apache Web Server and PHP

During the week we will be using the Apache Web server (version 2.x)
as well as the PHP scripting language. In order to install these now
you can simply do:

$ sudo apt-get install apache2 libapache2-mod-php5

If you are wondering how to find something like "libapache2-mod-php5"
here is what your instructor did:

$ sudo apt-cache search apache | grep php

The output was:

libapache2-mod-suphp - Apache2 module to run php scripts with the
owner permissions
php-auth-http - HTTP authentication
php-config - Your configuration's swiss-army knife
php5-apache2-mod-bt - PHP bindings for mod_bt
suphp-common - Common files for mod suphp
libapache2-mod-php5 - server-side, HTML-embedded scripting language
(apache 2 module)
php5-cgi - server-side, HTML-embedded scripting language (CGI binary)

Reading the descriptions made it apparent that the version of PHP
needed was in the "libapache2-mod-php5" package.

To test whether or not our Apache install has worked we can use the
text-based "lynx" web browser. This is not installed by default, so
first we must do:
$ sudo apt-get install lynx

Once the install completes type:

$ lynx localhost

and you should see the default apache2 directory listed. Press "Q" to
quit from lynx. PHP has already been configured to load and Apache has
been reconfigured to execute files with extensions of ".php", but
because the PHP module was installed after Apache in the command above
you must reload/restart the Apache web server for the new
configuration to take affect. There are multiple ways to do this, but
one that is easy to remember is:

$ /etc/init.d/apache2 restart

Go ahead and do this now.

Your instructor will tell you whether to go ahead with the next two exercises.

--
*

9.) So, you wanna be root...

As you have noticed Ubuntu prefers that you do your system
administration from a general user account making use of the sudo
command.

If you must have a root shell to do something you can do this by typing:

$ sudo bash

This is useful if you have to look for files in directories that would
otherwise be unavailable for you to see. Remember, be careful. As root
you can move, rename or delete any file or files you want.

What if you really, really want to log in as root? OK, you can do this
as well. First you would do:

$ sudo passwd root

Then you would enter in a root password - definitely picking something
secure and safe, right?! Once you've set a root password, then you can
log in as root using that password if you so desire. That's a
controversial thing to do in the world of Ubuntu and Debian Linux.

--
*

8.) Create the locate database

One of the easiest ways to find files on your system is to use the
locate command. For details, as usual, read the man pages:

$ man locate

We assume you are familiar with this command, but building the locate
database is a bit different on different Linux and Unix versions.

Locate uses a hashed database of f filenames and directory paths. the
command searches the database instead of the file system to find
files. While this is much is much more efficient it has two downsides:
1. If you create the locate database as root then users can see files
using locate that they otherwise would not be able to see. This is
considered a potential security hole.
2. The locate command is only as precise as the locate database. If
the database has not been recently updated, then newer files will be
missed. Many systems use an automated (cron) job to update the locate
database on a daily basis.

To create an initial locate database, or update the current one do:

$ sudo updatedb

Once this process completes (it may take a few minutes) try using the command:

$ locate ssh

Quite a few files go past on the screen. To find any file with "ssh"
in it's name or it's path and which has the string "conf" you can do:

$ locate ssh | grep conf

Read about "grep" using "man grep" for more information. The locate
command is very powerful and useful. For a more exacting command you
can consider using "find". This is harder to use and works by
brute-force. As usual do "man find" for more information.

--
*

Use the ip tool

The ip command is a powerful network debugging tool available to you
in Ubuntu. You may have already used this tool in other Linux
distributions. But, if not, start by reading:

$ man ip

As you can see this tool is designed to, "show/manipulate routing,
devices, policy routing and tunnels."

For instance, if you are wondering what your default route is (or are)
you can simply type:

$ ip route

This is actually short for "ip route show". Maybe you are wondering
out which interface packets will go to a certain address? A quick way
to find out is:

$ ip route get 128.223.32.35

Clearly you can substitute any IP address you wish above. This is
useful for boxes that have multiple network interfaces defined.

Maybe you want to be able to sniff packets coming across an interface
on your box. To do this you may wish to place your interface in
promiscuous mode. Often this requires updating a kernel parameter.
With the ip command you can do:

$ sudo ip link set eth0 promisc on

Note the use of "sudo" here as setting an interface requires admin
privileges. Now you can snoop the packets on the eth0 interface by
doing:

$ sudo tcpdump -i eth0

Be sure to read the man page for tcpdump if you want further information.

--
*

Learn how to control services

6.) Learn how to control services

The first thing to remember is that if you install a new service, say
a web server (Apache), then Ubuntu will automatically configure that
service to run when you reboot your machine and it will start the
service immediately!

This is quite different from the world of Red Hat, Fedora, CentOS,
etc. In order to configure and control services the core tool
available to you is update-rc.d. This tool, however, may not be the
easiest to use. Still, you should read and understand a bit about how
this works by doing:

$ man update-rc.d

There are a couple of additional tools available to you that you can
install. These are sysvconfig and rcconf. Both of these are
console-based gui tools. To install them do:

$ sudo apt-get install sysvconfig rcconf

Did you notice that we specified two packages at the same time? This
is a nice feature of apt-get. Try both these commands out. You'll
notice that the sysvconfig command is considerably more powerful.
Please don't make any changes to your services at this time.

$ sudo rcconf
$ sudo sysvconfig

Finally, there is a nice Bash script that has been written which
emulates the Red Hat chkconfig script. This is called rc-config. We
have placed this script on our "noc" box. Let's download the script
and install it for use on your machine:

$ cd
$ wget http://noc/workshop/scripts/rc-config
$ chmod 755 rc-config
$ sudo mv rc-config /usr/local/bin

At this point the script is installed. You should be able to just run
the script by typing:

$ rc-config

Try viewing all scripts and their status for all run-levels:

$ rc-config -l

Now trying viewing the status of just one script:

$ rc-config -ls anacron

You can see how this script works, if you understand enough of bash
scripts, by taking a look at it's code:

$ less /usr/local/bin/rc-config

--
*

Install libc, gcc, g++ and make

Two items missing from a default Debian/Ubuntu installation are gcc
and make plus their associated bits and pieces. This can be quite
disconcerting if you are used to compiling software under other
versions of Linux. Luckily there is an easy way to install all the
bits and pieces you need to use gcc and/or make. Simply do:

$ sudo apt-get install build-essential

and respond with a "Y" when asked if you "...want to continue". Once
the installation process finishes you should have both gcc and make
installed on your machine.

This is an example of installing software using a "meta-package." If
you type in the command:

$ sudo apt-cache showpkg build-essential

You will see a descriptive list of all the various pieces of software
that are installed for this package.

--
*

Update /etc/apt/sources.list

When using apt, apt-get, aptitude and/or synaptic there is a master
file that tells Ubuntu where to look for software you wish to install.
This file is /etc/apt/sources.list. You can update this file to point
to different repositories (third party, local repositories, remove the
cdrom reference, etc...). In our case we are now going to do this.
We'll edit this file and we are going to edit out any reference to the
Ubuntu 7.10 cdrom, which is left from the initial install.

To edit the file /etc/apt/sources.list do:

$ sudo vi /etc/apt/sources.list

In this file we want to comment out any references to the Ubuntu
cd-rom. You'll see the following lines at the top of the file:

#
# deb cdrom:[Ubuntu-Server 7.10 _Gutsy Gibbon_ - Release i386
(20071016)]/ gutsy main restricted

deb cdrom:[Ubuntu-Server 7.10 _Gutsy Gibbon_ - Release i386
(20071016)]/ gutsy main restricted

Update this by simply commenting out the one line (see your vi
reference sheet for help):

#
# deb cdrom:[Ubuntu-Server 7.10 _Gutsy Gibbon_ - Release i386
(20071016)]/ gutsy main restricted

# deb cdrom:[Ubuntu-Server 7.10 _Gutsy Gibbon_ - Release i386
(20071016)]/ gutsy main restricted

Now the apt command (apt-get) won't attempt to read your cd-rom drive
each time you install software.

Change your sources list:

We won't be doing this, but take a closer look at the file
/etc/apt/sources.list. You should see multiple entries along the line
of "http://CC.archive.ubuntu.com/" where the "CC" is a country code.
If you installed and said that your location was Taiwan, then the
entry would read, "http://tw.archive.ubuntu.com/", and so forth.

If you make changes to this file, then you should remember to run:

$ sudo apt-get update

To make sure that all your local repository lists are up to date.

--
*

Learn how to install software

This is a large topic. Your instructor should have discussed this with
you previously. In general you can use apt-get to install software,
clean up software installs, remove software and update your
repositories. You can use aptitude as a meta-installer to control apt.
The dpkg command extracts and installs individual Debian packages and
is called by apt. In addition, synaptic is a graphical interface to
apt that can be used in Gnome or KDE. Finally, apt-cache allows you to
view information about already installed packages.

We are going to concentrate on the apt-get method of software
installation. But you should spend some time reading about and
learning about how apt (in general), aptitude, dpkg, apt-cache, and
synaptic work. To do this read the man pages for each:

$ man dpkg
$ man apt
$ man apt-get
$ man aptitude
$ man apt-cache

You don't need to read each man page in detail as this could take a
while, but review them enough to understand the basics of each command
and how they differ.

After reading try a few commands:

$ dpkg
$ dpkg -help | more [space for next page, or CTRL-C to exit more screen]
$ apt-get | more
$ sudo apt-get check [what does the "check" option do?]
$ aptitude [Look around at what is installed.]
$ apt-cache | more
$ apt-cache stats
$ apt-cache search nagios2
$ apt-cache showpkg nagios2 | more

--
*

1	D
2	H
3	B
4	J
5	C
6	F
7	B
8	F
9	B
10	F
11	C
12	J
13	D
14	H
15	D
16	F
17	D
18	G
19	A
20	2
21	A
22	H
23	A
24	G
25	B
26	H
27	C
28	G
29	A
30	G
31	B
32	F
33	A
34	G
35	A
36	F
37	B
38	H
39	B
40	G

1	D
2	H
3	B
4	J
5	C
6	F
7	B
8	F
9	B
10	F
11	C
12	J
13	D
14	H
15	D
16	F
17	D
18	G
19	A
20	2
21	A
22	H
23	A
24	G
25	B
26	H
27	C
28	G
29	A
30	G
31	B
32	F
33	A
34	G
35	A
36	F
37	B
38	H
39	B
40	G

ค้นหาบล็อกนี้

2/19/2554

2/18/2554

2/17/2554

2/07/2554

2/05/2554

1	D
2	H
3	B
4	J
5	C
6	F
7	B
8	F
9	B
10	F
11	C
12	J
13	D
14	H
15	D
16	F
17	D
18	G
19	A
20	2
21	A
22	H
23	A
24	G
25	B
26	H
27	C
28	G
29	A
30	G
31	B
32	F
33	A
34	G
35	A
36	F
37	B
38	H
39	B
40	G