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Introduction to Non-Genetic Darwinism/Non-Biological Darwinism

Aims

 * 1) To Introduce the Evolution Algorythm
 * 2) To show how it has been used in Engineering and Computer Science
 * 3) To introduce the Idea of Self-Ordering or Self Organizing Systems
 * 4) To make the case that Evolution is just one type of Self Organizing Sysetm

The Evolution Algorithm, a Heuristic
When Computer Scientists and Artificial Intelligence experts looked at Evolution, they saw the fact that it was a self-organizing system, and wondered if they could catch the principles of it, in a special type of Algorythm called a Heuristic, or model of a natural system. In order to reuse the algorythm they wanted to abstract it down to its most basic structure, which ended up being three things, a Gradient against which to work, a Test that determined which population elements would go on to reproduce themselves, and a form of mutation, or recombination that could cause a perturbation in the general trend for the system to move in the direction of the gradient. Depending on how radical the test was, a portion of the population would be removed each generation, and the remainder would reproduce thus creating an opportunity for some elements of the population to move against the gradient. This made the Evolution Algorythm a hill climbing heuristic. What would happen is that most fatal combinations would be removed from the population over time, increasing the likelihood of survival of the remainder of the population because it would eventually diffuse from a position above the testing line. One interesting idea was that you could steer this algorythm by customizing the test so that it tested for different properties. The population would tend to drift in a direction away from the test, propelling it towards a goal.

Genetic Programming
One obvious method of using the Evolution Algorythm was in the development of an automatic prototyping system for programs. Called Genetic Programming because the programming language was rewritten to fit into the same general pattern as Gene encoding in DNA, The idea was to create a random population of program snippets, and Evolve them until they met some specification. Programs that failed to fit the specification would be eventually removed from the population, and an evaluation function would increase the likelihood that a good solution would reproduce and become part of the recombination of future generations. Since every algorythm would be evaluated, at the end of the process, a small population of really good combinations would exist that met or exceeded the specification. Of course it wasn't quite as simple as that, and the process required a major investment in processing time for the whole population to be evaluated, but the research indicated the feasibility of the approach.

Meta-Heuristics, Algorithms with an internal heuristic
Another less obvious way of using the Evolution algrorythm was to embed it into another algorythm, This mechanism called a Meta-Heuristic, because the actual algorythm included the heuristic within it, and therefore was a meta-algorythm or algorythm with an algorythm inside it, has been used most often for a search technique. Along with Evolution, other heuristics like Simulated Annealing, Neighborhood Algorythms Taboo Algorythms, and Greedy Algorythms have been used. These heuristics create a type of search that while it doesn't guarantee the best answer, comes to an answer faster than a brute force comparison search could. One place they are useful, is where speed is as important, or is more important than the accuracy of the search. One way of looking at Genetic Programming, is a search across most combinations of the genetic language for a solution that is good enough to pass the survival test. We promote it to Selection of a winning population by taking the Evaluation out of its context as a basis for the test, and using it to find the best solution in the population found. Evolution doesn't care which elements are the best, merely which follow the specifications as defined by the test used. But that doesn't mean we have to use it that way.

Meta-Heuristic Searches, and Self-Optimizing systems
One of the reasons we need Meta-Heuristics, is that some of the most natural functions of human processing, are hard to achieve computationally (As in with computers). One such difficult problem is the problem of optimizing a system, or changing it in a way that makes it more adaptive to its environment. As the number of elements you want to adapt increases, the size of the search space that must be searched increases logrythmically, It falls in a class of processing tasks that are labelled NP HARD, which if we translate it into English means that Even if we have a parallel processor with an infinite number of processor units, it would take a long time to execute. In the case of such a problem we can do one of three things, we can take the time up front, which slows down the processing of the rest of the system, as we use up all the processor cycles, We can spread the cost over time, by only doing a part of the optimization at any one pass, or we can cheat, and do a less accurate search and do it much faster than the classic brute force search could do. In some cases we might want to mix modes and do a faster but partial search thus spreading the cost over time, and speeding up the eventual results. Of course we won't necessarily get the best answer, but often all we need is an answer that is "Good Enough", and if it gets better over time, so much the better.

Similar Heuristics used in Meta-Heuristic Algorithms
The following Algorythms have been used in Metaheuristics.

 * Genetic Algorythms
 * Genetic Programming
 * Evolution
 * Ant Colony
 * Estimation of Distribution
 * Scatter Search
 * Variable Neighbor Search
 * Simulated Annealing
 * Tabu Search
 * Hybrids combining Breadth first Algorythms with later Depth first Algorythms

While each deserves it's own description, to do so would fall outside the scope of this course.

Why Meta-Heuristics are faster than and less accurate than brute force searches
The reason that Metaheuristics are faster than brute force searches, is simply because they only sample the search space, and do not search every element to find out if it is better then the next. They work best where there is some locality to the data, so that the best answers tend to aggregate in the same neighborhood. Where there is too much variation locally, they will miss the optimal local answer often, simply because they do not get a chance to compare it. However in the case where there is some locality of good answers, the search gradually gets more accurate in the area where there is the most chance of a good answer, and as a result, will while not always getting the most optimal answer, at least get a good one.

Auto-Prototyping in a Darwinian World
Once Auto-prototyping was proven to work in Genetic Programming, other engineering disciplines wanted in on the act, and other design languages were created that could be used in a Genetic Programming Environment to design other types of systems.

Digital DNA
The idea of Digital DNA, is that most digital circuits are made today as cells in a Field Programmanble Gate Array (FPGA), Since there are only a certain number of different cell designs, a Genetic Language could be developed that would create a chip to specifications in much the same way that programs were generated in Genetic Programming.

Printer of the Month Club, Marketing of Arbitrary Models
While I can't prove it, I suspect that of the reams of cheap printers being manufactured, many models are simply members of a Genetic Design population based on a much smaller set of actual printer mechanisms. Because the designs are so variable, and the production equipment is so flexible that it can do multiple different products at once, the result is that a model lifespan is much shorter on the design end, than it is on the actual user end. I jokingly call it the printer of the month club, because it seems whenever I drop into the store, a new design is being sold on a discount because it is being discontinued by the manufacturer, and a new line is being brought out.

Self-Ordering Systems
Although little understood, the concept of self-organization may be an important driving force in evolution. Self-organization occurs in many types of systems; phase transitions such as crystallization or the alignment of molecules to form a cell membrane, for example, exhibit self-organizing properties. Self-Ordering or Self-Organizing systems may still be somewhat alien to our understanding of how the world works. It is partly because of the existence of systems such as these that we need to rethink Darwinism, and the 2nd Law of Thermodynamics. Self-Ordering or Self-Organizing systems show the ability to organize into greater order, or into entities that are greater than either chance or the sum of their parts would indicate. A heuristic called simulated annealing, is often used in the same way that the Evolution Algorithm is used in meta-heuristics. It is based on a popular technique for softening metal, the technique, called annealing, calls for reheating the metal, while it is cooling so as to remove flaws in the crystal structure, and grow larger crystals. The result is a more malleable piece of metal. The similarities in these two Algorithms, may indicate a common principle in how they work. That such a common principle is involved in all Self-Organizing Systems, has not yet been proven.

The role of Darwinism in Self-Ordering Systems
If we limit Darwinism to Genetics, then there is no role of Darwinism in self-ordering systems, it is simply a special example of one. But if we look a little closer at Darwinism, we see that it does not need to be as tightly defined as all that. What Darwin Discovered was that Natural Forces acting on a population, result in Self-Ordering behavior. He called this principle Natural Selection, because he noted that it acted to select some elements of the population, to breed and others not to, in a way that seemed similar to how animal husbandry selected to create a breed. The factor that he thought made the difference was survival. Perhaps however, this is just the most obvious factor. Any Factor which acts to select between one individual and the next in the propagation of that factor in the population will work. Even I joke, bad breath.

Taking apart the Evolution Algorythm
If we look at the evolution algorythm we see that the main element in direction is the test, if we change the specifics of the test, we change the eventual characteristics of the population. So why do we need the gradient and the mutation rate? Those are needed to build the search background in which the test searches. Essentially there is a near infinite number of combinations that can be formed within the Universe, However not every combination is stable enough to survive. The gradient determines how quickly a combination will degrade back to its more simple elements, and the Test, determines which combinations will degrade soonest. The Mutation rate is simply a way of sampling a wider range of combinations. The more recombination and mutation happens the wider the diversity of the population, and incidentally the more combinations can be tested.

Simplicity of the Algorythm versus Complexity of Genetic Evolution
Are we talking about the same thing, when we talk about the Evolution Algorythm and Natural Selection? Perhaps not, The Evolution algorythm is much simpler than genetics. However, partly that is because DNA is a language similar to the languages we have developed for Genetic programming and Digital DNA. We can use the Evolution Algorythm to interpret our genetic languages, or we can use it as a search algorythm in an arbitrary data field. The Algorythm works both ways, and in just this manner, Darwinism might work to explain self-organization that does not involve Genetics.

Assignment

 * Consider the Lazy God Hypothesis, that God Invented Self-Organization so he could spend more time on the golf course. Is this more or less absurd than the idea that the Universe that we know, is built from many different systems that self-organized naturally, without the need for genetics, and other systems that evolved because genetics was possible? Write a short essay on the subject.

 * Consider that you want to design a program that does the Tower of Hanoi problem where you have different sized rings on three different "Towers" and you have to sort them from largest to smallest but can only move one ring at a time. You have a Genetic Programming system ready to help you, how would you describe the problem to the Genetic Programming System so it could evolve you a program that could do the project. Look up the Algorythm for solving the puzzle in an algorythm book, does this help?

 * On the last problem, one of the things you have to do, is decide the size of the population base on which you will base your Genetic Programming Attempt. The wider the population the more sure you are that you will come up with a solution, but the longer it will take to do all the tests to see if each element in the population will survive to go on to the next generation. Considering that it takes a millisecond per element to test, and you are going to try 10,000 generations just to be sure that you get a really good implementation, and you have a Cluster computer that is 80% efficient and has 8 nodes each running at 2 ghz. How big can your population be, if you want the program to terminate sometime next week?

 * Go Down to your local Computer Store and look for the cheapest printer on the shelf. Wait a month and go down to the same store and look for the cheapest printer again, is it the same printer? or has someone replaced it with a different model. Look closely at the specifications, do you think the actual mechanism the printer is built on has changed, or are they just mixing and matching a lot of different parameters to make the printer seem different?

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Introduction to Non-Genetic Darwinism/Darwin's Theory of Natural Selection

Aims

 * 1) To learn the fundamental principles of Darwin's theory of natural selection.
 * 2) To understand the historical context of this theory.
 * 3) To provide a window into Darwin's thought processes.
 * 4) To understand that natural selection is by no means the only cause of evolution.

Lesson
Darwin was one of many early contributors to evolutionary biology. His observations on the natural world, ranging over a span of decades, have been preserved in his books about subjects ranging from barnacles to island erosion. It is generally agreed that his most important work was the development of the theory of natural selection, at around the same time as fellow naturalist Alfred Russell Wallace was developing a similar theory.

The theory of natural selection arose within a rich historical context. The concept of evolution, i.e. that species are not fixed but change over time, predated Darwin. In fact, Charles Darwin's grandfather Erasmus Darwin was a prominent advocate of the theory of evolution. The mechanism of how evolution occurred, however, remained unknown. Darwin's theory of natural selection was one of many hypotheses about this mechanism, and was not widely accepted by evolutionary biologists until the 20th century.

Darwin's theory of natural selection is:
 * 1) There is variation in traits.
 * 2) Some specific variations in traits cause individuals to produce a larger number of surviving offspring.
 * 3) These variations are heritable.
 * 4) The end result is that specific trait variations increase in prevalence in a population.
 * 5) Eventually, most or all individuals in a population may have the more successful variations. As a result, the physical characteristics of the population may change.

A competing theory, of the inheritance of acquired characters, had been proposed in ancient times. It is commonly associated with another naturalist named Lamarck, a contemporary of Erasmus Darwin and a prominent champion of the theory. Inheritance of acquired characteristics was discredited much later, by the discovery of the mechanisms of heredity (these were not known until the 20th century!) and by laboratory studies showing that acquired characteristics were not inherited.

Natural selection is by no means the only cause of evolution. For example, migration can lead to new characteristics being introduced into a population. Drift is the chance inheritance of a particular characteristic by a disproportionate number of individuals in a given generation. (As you might imagine, the effect of drift is particularly strong in small populations.) These factors were little appreciated until the Modern Evolutionary Synthesis of the 20th century, a scientific movement that did not begin until decades after Darwin's death.

However, in the end of the twentieth century, several scientists, one of them Steven Jay Gould, proposed that the Modern Evolutionary Synthesis and its unification of Mendelian Genetics and Darwinian Natural selection was not enough to account for several phenomena of biology, like niche construction, epigenetic inheritance and particularly, the deep homology which several phyla of animals exhibit. Gould, along with other evolutionary biologists called for an "Extended Synthesis," a theoretical framework that would again unite Ecology with Embryology and the previous Neodarwinist Modern Synthesis. This has been met by criticisms by several biologists like Richard Dawkins, and even among philosophers of science like Daniel Dennett.

Assignment

 * 1) On the Internet or elsewhere, find an example of a trait that may have changed due to natural selection.
 * 2) *In the "Examples of Natural Selection" section of this module, write:
 * 3) *#What trait did you find and what source did you find it in? Provide a link to the source.
 * 4) *#How and why may the trait have changed due to natural selection?
 * 5) *#Devise an alternative scenario for another reason the trait may have changed, which also involves natural selection.
 * 6) *#Devise a second alternative scenario for how the trait may have changed, due to factors other than natural selection.
 * 7) *#Comment on the relative likeliness of each of your three scenarios.
 * 8) Read: Darwinism Must Die So That Evolution May Live (an article from the New York Times)
 * 9) *Write a paragraph or two about whether you agree or disagree with the thesis presented in this article, and why or why not. You are encouraged to consult other sources.  Be sure to provide ORIGINAL INSIGHTS and not merely regurgitate this lesson or the article.
 * 10) Here is a link to Darwin's book on the theory of natural selection, On the Origin of Species by Means of Natural Selection.
 * 11) *Read Chapter 1 of this book, "Variation Under Domestication."
 * 12) *Write ~1 paragraph about how Darwin's observations, as presented in this chapter, may have helped him develop the theory of natural selection. Keep in mind that these were not Darwin's only observations.  Darwin intensively studied non-domesticated species as well, both at home in England and during the several years he spent circumnavigating the world.

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license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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User talk:Koolanu123

 Hello Koolanu123, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! --AFriedman 05:13, 15 February 2009 (UTC)

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title: User talk:Koolanu123


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User talk:Smokecycle

 Hello Smokecycle, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! --AFriedman 05:14, 15 February 2009 (UTC)

 Hi! My name is WikiversityJack. I am a pumpkin and a Wikiversity mascot. My aunt told me that fun + learning = Wikiversity. Let me know if I can help you out.

title: User talk:Smokecycle


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Talk:Enterprise Resource Planning/Accounting

=Suggestion to change=
 * This is for the expert accountants out there to figure out for me as IANA - I am no accountant. I think the Project Accounting section should be Financial Management so that we can include all that plus taxation and compliance.
 * Anyway i am preparing a new TOC for application to national ministry of higher education for MQA acceptance which will qualify it as a full 2 year Masters program and qualified for scholarship locally.
 * TOC in progress here: http://red1.org/aaa/Asia-eU-TOC.pdf
 * --Izamryan 16:25, 22 February 2009 (UTC)
 * I agree with the first point made above --> there is a good rationale for expanding the scope of this module to include tax, compliance (annual audits!), developing reports for business owners (management accounts). And for people in the manufacturing and professional services industries, a topic on job costing may be useful as well.


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User:Ankushchadda

My name is Ankush Chadda. I am a UIET, kurukshetra, Haryana,India .I am currently doing Btech in Computer Science. I am new here( in wikiversity) but am amazed by the awesome work people are doing here.I mean really there is so much to learn, to comprehend and to contibute and share.

I will give my total contribution to the forum.

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User:Egm6936.s09

EGM 6936 Seminar, Spring 2009 Prof. Loc Vu-Quoc

University of Florida

 Licensing info ,  Interesting news

EGM 6611 Continuum Mechanics, Fall 2003

Curling of paper sheet using shell theory

Solid shell in cartesian coordinates

A thermal problem

Dimensional analysis tutorial (Bucky wiki)

Nondimensionalization (French thesis) (Bucky wiki)

Dimensional analysis (equations in French thesis)

Test Word macros

Ground water mound problem

Elastic stability

Dynamic stability

Writing tools

Other courses

title: User:Egm6936.s09


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User:Egm6936.s09/Dimensional analysis

Write equations from the French thesis here.

Ref: Nordstrom et al., Influence of sheet dimensions on curl of paper, Journal of Pulp and Paper Science, Vol.24, No.1, pp.18-25, Jan 1998.

Eq.(2) in Nordstrom et al. (1998) can be written as follows:

\displaystyle \boldsymbol \epsilon =  \epsilon_{\alpha \beta} \boldsymbol e_\alpha \otimes \boldsymbol e_\beta  \,\,\,(1) $$ where $$\displaystyle \boldsymbol e_\alpha$$ is the cartesian basis vector along the coordinate axis $$\displaystyle x_\alpha$$, for $$\displaystyle \alpha = 1, 2, 3$$.

Using Kirhoff's assumptions and encountering effects of large deformation, components of Green Lagrange tensor are given as below:

\displaystyle \varepsilon _{\alpha \beta } = \frac{1}{2}\left( {u_{\alpha ,\beta }  + u_{\beta ,\alpha }  + w_{,\alpha } w_{,\beta } } \right) + z\kappa _{\alpha \beta }  = \varepsilon _{\alpha \beta }^M  + \varepsilon _{\alpha \beta }^B  \,\,\,(2) $$

We divided components of strain tensor into membrance strain and bending strain as:

\displaystyle \varepsilon _{\alpha \beta }^M = \frac{1}{2}\left( {u_{\alpha ,\beta }  + u_{\beta ,\alpha }  w_{,\alpha } w_{,\beta } } \right)\,\,\,(3) $$

\displaystyle \varepsilon _{\alpha \beta }^B = z\kappa _{\alpha \beta }   \,\,\,(4) $$ Thus

\displaystyle {\boldsymbol{\varepsilon = }}\varepsilon _{\alpha \beta } {\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta   = \frac{1}{2}\left( {u_{\alpha ,\beta }  + u_{\beta ,\alpha }  + w_{,\alpha } w_{,\beta } } \right){\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta   + z\kappa _{\alpha \beta } {\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta   = \varepsilon _{\alpha \beta }^M {\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta   + \varepsilon _{\alpha \beta }^B {\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta   = {\boldsymbol{\varepsilon }}^{\boldsymbol{M}}  + {\boldsymbol{\varepsilon }}^{\boldsymbol{B}}  = {\boldsymbol{\varepsilon }}^{\boldsymbol{M}}  + z{\boldsymbol{\kappa }}  \,\,\, (5) $$

with

\displaystyle {\boldsymbol{\varepsilon }}^{\boldsymbol{M}} = \varepsilon _{\alpha \beta }^M {\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta \,\,\, (6) $$

\displaystyle {\boldsymbol{\varepsilon }}^{\boldsymbol{B}} =  z{\boldsymbol{\kappa }}  = \varepsilon _{\alpha \beta }^B {\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta  \,\,\,(7) $$

\displaystyle {\boldsymbol{\kappa }} = \kappa _{\alpha \beta } {\boldsymbol{e}}_\alpha  \otimes {\boldsymbol{e}}_\beta   =  - w_{,\alpha \beta } {\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta    \,\,\,   (8) $$

where $${\boldsymbol{\varepsilon }}^{\boldsymbol{M}}$$,$${\boldsymbol{\varepsilon }}^{\boldsymbol{B}}$$, and $${\boldsymbol{\kappa}}$$ are membrance induced tensor, bending induced tensor, and curvature respectively.

Hooke's Law describes relation of stress and strain has the following form:

\displaystyle {\boldsymbol{\sigma = C}}:{\boldsymbol{\varepsilon }} \,\,\, (9) $$

where stress tensor

\displaystyle \sigma _{\alpha \beta } {\boldsymbol{e}}_\alpha  \otimes {\boldsymbol{e}}_\beta \,\,\,(10) $$

and stiffness tensor

\displaystyle {\boldsymbol{C}} = C_{\alpha \beta \gamma \delta } {\boldsymbol{e}}_\alpha  \otimes {\boldsymbol{e}}_\beta   \otimes {\boldsymbol{e}}_\gamma   \otimes {\boldsymbol{e}}_\beta \,\,\,(11) $$

or stress-strain relation written in component forms is:

\displaystyle \sigma _{\alpha \beta } = C_{\alpha \beta \gamma \delta } \varepsilon _{\gamma \delta } \,\,\,(12) $$

The membrance forces are:

\displaystyle {\boldsymbol{N}} = \int {{\boldsymbol{\sigma }}dz{\boldsymbol{ = }}\int {{\boldsymbol{C}}:{\boldsymbol{\varepsilon }}dz = } } N_{\alpha \beta } {\boldsymbol{e}}_\alpha  \otimes {\boldsymbol{e}}_\beta \,\,\ (13) $$

or

\displaystyle {\boldsymbol{N}} = \int {{\boldsymbol{C}}:\left( {{\boldsymbol{\varepsilon }}^{\boldsymbol{M}} + {\boldsymbol{\varepsilon }}^{\boldsymbol{B}} } \right)dz}  = \int {{\boldsymbol{C}}:{\boldsymbol{\varepsilon }}^{\boldsymbol{M}} dz}  + \int {{\boldsymbol{C}}:{\boldsymbol{\varepsilon }}^{\boldsymbol{B}} dz = } \left( {\int {{\boldsymbol{C}}dz} } \right):{\boldsymbol{\varepsilon }}^{\boldsymbol{M}}  + \left( {\int {{\boldsymbol{C}}zdz} } \right):{\boldsymbol{\kappa }} = {\boldsymbol{A}}:{\boldsymbol{\varepsilon }}^{\boldsymbol{M}}  + {\boldsymbol{B}}:{\boldsymbol{\kappa }} \,\,\, (14) $$

where

\displaystyle {\boldsymbol{A}} = \left( {\int {{\boldsymbol{C}}dz} } \right) = A_{\alpha \beta \gamma \delta } {\boldsymbol{e}}_\alpha  \otimes {\boldsymbol{e}}_\beta   \otimes {\boldsymbol{e}}_\gamma   \otimes {\boldsymbol{e}}_\beta   = \left( {\int {C_{\alpha \beta \gamma \delta } dz} } \right){\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta   \otimes {\boldsymbol{e}}_\gamma   \otimes {\boldsymbol{e}}_\beta \,\,\,(15) $$

\displaystyle {\boldsymbol{B}} = \left( {\int {{\boldsymbol{C}}zdz} } \right) = B_{\alpha \beta \gamma \delta } {\boldsymbol{e}}_\alpha  \otimes {\boldsymbol{e}}_\beta   \otimes {\boldsymbol{e}}_\gamma   \otimes {\boldsymbol{e}}_\beta   = \left( {\int {C_{\alpha \beta \gamma \delta } zdz} } \right){\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta   \otimes {\boldsymbol{e}}_\gamma   \otimes {\boldsymbol{e}}_\beta \,\,\,(16) $$

Thus,

\displaystyle N_{\alpha \beta } = A_{\alpha \beta \gamma \delta } \varepsilon _{\gamma \delta }^M  + B_{\alpha \beta \gamma \delta } \kappa _{\alpha \beta } \,\,\,(17) $$

Similarly, bending moments are:

\displaystyle {\boldsymbol{M}} = \int {{\boldsymbol{\sigma }}zdz{\boldsymbol{ = }}\int {{\boldsymbol{C}}:{\boldsymbol{\varepsilon }}zdz = } } M_{\alpha \beta } {\boldsymbol{e}}_\alpha  \otimes {\boldsymbol{e}}_\beta \,\,\,(18) $$ or

\displaystyle {\boldsymbol{M}} = \int {{\boldsymbol{C}}:\left( {{\boldsymbol{\varepsilon }}^{\boldsymbol{M}} + {\boldsymbol{\varepsilon }}^{\boldsymbol{B}} } \right)zdz}  = \int {{\boldsymbol{C}}:{\boldsymbol{\varepsilon }}^{\boldsymbol{M}} zdz}  + \int {{\boldsymbol{C}}:{\boldsymbol{\varepsilon }}^{\boldsymbol{B}} zdz = } \left( {\int {{\boldsymbol{C}}zdz} } \right):{\boldsymbol{\varepsilon }}^{\boldsymbol{M}}  + \left( {\int {{\boldsymbol{C}}z^2 dz} } \right):{\boldsymbol{\kappa }} = {\boldsymbol{B}}:{\boldsymbol{\varepsilon }}^{\boldsymbol{M}}  + {\boldsymbol{D}}:{\boldsymbol{\kappa }} \,\,\,(19) $$

where

\displaystyle {\boldsymbol{D}} = D_{\alpha \beta \gamma \delta } {\boldsymbol{e}}_\alpha  \otimes {\boldsymbol{e}}_\beta   \otimes {\boldsymbol{e}}_\gamma   \otimes {\boldsymbol{e}}_\beta   = \left( {\int {C_{\alpha \beta \gamma \delta } z^2 dz} } \right){\boldsymbol{e}}_\alpha   \otimes {\boldsymbol{e}}_\beta   \otimes {\boldsymbol{e}}_\gamma   \otimes {\boldsymbol{e}}_\beta \,\,\, (20) $$

whence

\displaystyle M_{\alpha \beta } = B_{\alpha \beta \gamma \delta } \varepsilon _{\gamma \delta }^M  + D_{\alpha \beta \gamma \delta } \kappa _{\alpha \beta } \,\,\, (21) $$

Combining equations (14) and (19) into matrix form:

\displaystyle \left[ \begin{array}{l} {\boldsymbol{N}} \\ {\boldsymbol{M}} \\ \end{array} \right] = \left[ {\begin{array}{*{20}c} {\boldsymbol{A}} & {\boldsymbol{B}} \\ {\boldsymbol{B}} & {\boldsymbol{D}} \\ \end{array}} \right]:\left[ \begin{array}{l} {\boldsymbol{\varepsilon }}^{\boldsymbol{M}} \\ {\boldsymbol{\kappa }} \\ \end{array} \right] \,\,\, (22) $$

$$   \begin{align} G   & = x_1^{g_1} x_2^{g_2} \cdots x_q^{g_q} F ( \Pi_{q+1} \Pi_{q+2} \cdots \Pi_{p}) \\ (G) & =   L^{\rho} M^{m} T^{t} \Theta^{\theta} \\ (X_i) & =   L^{l_i} M^{m_i} T^{t_i} \Theta^{\theta_i} \end{align} $$

$$\begin{array}{|c|c|c|c|c|c|} & L & M & T & \Theta & \\ \hline G & l & m & t & \theta & \\ x_1 & l_1 & m_1 & t_1 & \theta_1 & e_1 \\ x_2 & l_2 & m_2 & t_2 & \theta_2 & e_2 \\ \vdots & \vdots & \vdots & \vdots & \vdots & \vdots \\ x_p & l_p & m_p & t_p & \theta_p & e_p \\ \end{array}$$

$$   \begin{align} G_n & =   x_1^{e_1} x_2^{e_2} \cdots x_p^{e_p} \\ l   & = l_i\,e_i \\ m   & = m_i\,e_i \\ t   & = t_i\,e_i \\ \theta & =   \theta_i\,e_i \\ l_j\,e_j & =  l - (l_k\,e_k) \\ m_j\,e_j & =  m - (m_k\,e_k) \\ t_j\,e_j & =   t- (t_k\,e_k) \\ \theta_j\,e_j & =  \theta - (\theta_k\,e_k) \\ e_j & =  f_j (l,m,t,\theta,e_5,e_6, \cdots,e_p) = g_j (l,m,t,\theta) + h_j (e_5, \cdots,e_p) \\ \end{align} $$

$$  \begin{align} G_n & = x_1^{g_1+h_1} x_2^{g_2+h_2} x_3^{g_3+h_3} x_4^{g_4+h_4} x_5^{e_5}x_6^{e_6} \cdots x_p^{e_p} \\ & = x_1^{g_1} x_2^{g_2} \cdots x_4^{g_4} \left \{ x_1^{h_1 (e_5, e_6, \cdots, e_p)}, x_2^{h_2} x_3^{h_3} x_4^{h_4} x_5^{e_5} \cdots x_p^{e_p} \right \} \\ & = x_1^{g_1} x_2^{g_2} x_3^{g_3} x_4^{g_4} \left \lbrace (x_1 x_2 x_3 x_4 x_5)^{e_5} (x_1 x_2 x_3 x_4 x_6)^{e_6} \cdots (x_1 x_2 x_3 x_4)^{e_p} \right \rbrace \end{align} $$

$$  \begin{align} G_n = x_1^{g_1(lmt)} x_2^{g_2(lmt)} x_3^{g_3(lmt)} x_4^{g_4(lmt)} \pi_5 (x_1 x_2 x_3 x_4 x_5) \cdots \pi_p (x_1 x_2 x_3 x_4 x_p) \end{align} $$

$$  \begin{align} G_n = x_1^{g_1} x_2^{g_2} x_3^{g_3} x_4^{g_4} \end{align}

$$ $$  \begin{align} \pi_5 = L^{o} M^{o} T^{o} \Theta^{o} \end{align} $$

$$  \begin{align} G   = \sum_{n} G_n \end{align} $$

$$  \begin{align} F  = \sum_{n} (\pi_5 \pi_6 \cdots \pi_p)_n \end{align} $$

$$  \begin{align} G_n = x_1^{g_1} x_2^{g_2} x_3^{g_3} x_4^{g_4} F(\pi_5 \pi_6 \cdots \pi_p) \end{align} $$

$$\begin{array}{|c|c|c|c|c|} & L & M & T & \\ \hline G & l& m & t & \\ \rho & -3 & +1 & 0 & e_1 \\ u & +1 & 0 & -1 & e_2 \\ L & +1 & 0 & 0 & e_3 \\ \mu & -1 & +1 & -1 & e_4 \\ g & +1 & 0 & -2 & e_5 \\ \sigma & 0 & 1 & -2 & e_6 \\ \end{array}$$

$$  \begin{align} l   & = - 3 e_1 + e_2 + e_3 - e_4 + e_5 \\ m  & = e_1 + e_4 + e_6 \\ t   & = - e_2 - e_4 - 2 e_5 - 2 e_6 \\ e_1 & = m - e_4 - e_6 \\ e_2 & = - t - e_4 - 2 e_5 - 2 e_6 \\ e_3 & = l + 3 m + t - e_4 + e_5 - e_6 \\ \end{align} $$

$$  \begin{align} G  & = \rho^{m - e_4 - e_6} u^{- t - e_4 - 2 e_5 - 2 e_6} L^{l + 3 m + t - e_4 + e_5 - e_6} \mu^{e_4} g^{e_5} \sigma^{e_6} \\ & = \rho^{m } u^{- t} L^{l + 3 m + t} \left ( \frac{\mu}{u L \rho} \right )^{e_4} \left ( \frac{gL}{u^{2}} \right )^{e_5} \left ( \frac{\sigma}{\rho u^{2} L} \right )^{e_6} \end{align} $$

$$  \begin{align} R  & = \rho \frac{u^{2}}{2} S F (Re, Fr, Wb) \\ Re  & = \frac{uL\rho}{\mu}, \quad Fr = \frac{u}{\sqrt{gL}} \quad and \quad Wb = \frac{\rho u^{2} L}{\sigma} \end{align} $$

$$  \begin{align} k_d & = \frac{d_2}{d_1} = const \\ u_2 & = k_u\,u_1 \\ t_2 & = k_t\,t_1 \\ u_2 & = \frac{d(d_2)}{d(t_2)} \\ k_u\,u_1 & = \frac{k_d}{k_t} \frac{d(d_1)}{d(t_1)} \\ u_1 & = \frac{d(d_1)}{d(L_1)} \\ k_u & = \frac{k_d}{k_t} \\ k_Y & = \frac{k_u}{k_t} = \frac{k_d}{k_t^{2}} = \frac{k_u^{2}}{k_d} \\ k_F & = k_m\,k_Y = k_G = k_p = k_\nu \\ \end{align} $$

$$  \begin{align} & E_1: \quad \rho_1 \frac{\partial{u_{1i}}}{\partial{t}} + \rho_1 u_{1j} \frac{\partial{u_{1j}}}{\partial{x_{1j}}} = - \frac{\partial{p1}}{\partial{x_{1i}}} + \rho_i g_{1i} + \frac{\partial{\tau_{1ij}}}{\partial{x_{1j}}} \\ & (g_{11} = 0, \quad g_{12} = 0, \quad g_{13} = -g) \\ & \underset{Initial}{\underset{\uparrow}{I_t + I_s}} = \underset{Pression}{\underset{\uparrow}{P}} + \underset{Gravity}{\underset{\uparrow}{G}} + \underset{Force}{\underset{\uparrow}{F}} \quad I_t = \rho \frac{\partial{u_1}}{\partial{t}}, \quad P = - \frac{\partial{p1}}{\partial{x_1i}} \\ & E_2: \quad k_{\rho} \frac{k_u}{k_t} I_t + k_{\rho} \frac{k_u^{2}}{k_d} I_s = \frac{k_p}{k_d} P + k_{\rho} k_{g} G + \frac{k_u k_\mu}{k_d^{2}} F \\ & \tau_{ij} = \lambda \frac{\partial{u_k}}{\partial{x_k}} \delta_{ij} + \mu \left ( \frac{\partial{u_i}}{\partial{x_j}} + \frac{\partial{u_j}}{\partial{x_i}} \right ) \\ \end{align} $$

$$  \begin{align} 1^{o}) I_t\,-\,I_s\,:  \frac{k_\rho k_u}{k_t} & = \frac{k_\rho k_u^{2}}{k_d} \\   k_u & = \frac{k_d}{k_t} \\   2^{o}) I_s\,-\,P\,: \frac{k_\rho k_u^{2}}{k_d} & = \frac{k_p}{k_d} \\ \frac{k_u^{2} k_\rho}{k_p} & = 1 \\ \end{align} $$

$$  \begin{align} \frac{u_1^{2} \rho_1}{P_1} & = \frac{u_2^{2} \rho_2}{P_2} \\ a^{2} & = \gamma \frac{p}{\rho} \\ k_a^{2} & = k_\gamma \frac{k_p}{k_\rho} \\ \frac{k_\gamma k_u^{2}}{k_a} & = 1 \\ \frac{u_1}{a_1} & = \frac{u_2}{a_2} \\ \end{align} $$

$$  \begin{align} 3^{o}) I_s\,-\,G\,:   k_\rho \frac{k_u^{2}}{k_d} & = k_\rho k_g  \\   \frac{k_u^{2}}{k_g k_d} & = 1 \\   4^{o})  I_s\,-\,F\,: k_\rho \frac{k_u^{2}}{k_d} & = \frac{k_u k_\mu}{k_d^{2}} \\ \frac{k_u k_\rho k_d}{k_\mu} & = 1 \\ \end{align} $$

$$  \begin{align} F_r & = \frac{u}{\sqrt{gD}} \,, \, R_e = \frac{\rho u d}{\mu} = \frac{u d}{\nu} \\ \rho \frac{dh}{dt} & = \frac{dp}{dt} + \frac{\partial}{\partial{x_i}} k \frac{\partial{T}}{\partial{x_i}} + \phi \\ \phi & = \lambda \left ( \frac{\partial{u_k}}{\partial{x_k}} \right )^{2} + \frac{u}{2} \left (  \frac{\partial{u_i}}{\partial{x_j}} + \frac{\partial{u_j}}{\partial{x_i}} \right )^{2} \\ E  & = P\, + \, T\, + \, D \\ \end{align} $$

$$  \begin{align} & 1^{o}) \quad E\,-\,P\,:  \quad k_\gamma = 1 \quad \gamma = \frac{C_p}{C_v} \\   & 2^{o}) \quad E\,-\,T\,: \quad \frac{k_\rho k_{C_p} k_u k_d}{k_k} = 1 \,, \, Pe = \frac{\rho C_p VD}{k} = \frac{C_p \mu}{k} \,. \, \frac{\rho VD}{\mu} = Pr \,. \, Re \\ & 3^{o}) \quad E\,-\,D\,:  \quad \frac{k_\rho k_{C_p} k_T k_d}{k_\mu k_u} = 1 \,, \, E_k = \frac{C_p T}{u^{2}} \\   \end{align} $$

$$  \begin{align} W_D & = \frac{\rho u^{2} d}{\sigma} \\ k_u & = \sqrt{k_d} \\ k_\mu & = k_u \,. \, k_\rho \,. \, k_d \\ k_d & = \left ( \frac{k_\mu}{k_\rho} \right )^{\frac{2}{3}} \\ \end{align} $$

title: User:Egm6936.s09/Dimensional analysis


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User talk:Brendonj

 Hello Brendonj, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

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And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! --AFriedman 16:30, 15 February 2009 (UTC)

 Hi , I am Percy. Nice to meet you. I love the beach, swimming, fishing, and I am currently doing some research at Wikiversity on oceanography. I am also learning to play piano. What do you like? Can I be your mascot?

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User talk:Romart

 Hello Romart, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may

 * Take a guided tour and learn to edit.
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 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
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 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
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 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! --mikeu talk 16:48, 15 February 2009 (UTC)

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Sandbox Server/Jan Luca

User:Jan Luca:

I want to host on the Sandbox-Server source-files(Open-Office-Draw etc.) and wanted to use for development my skript I hosted on the Toolserver. I can also help to expand the server with installing new software or make advertising in other Wikiversitys. Jan Luca 19:09, 15 February 2009 (UTC)

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Introduction to Non-Genetic Darwinism/Neural Selection

Aims

 * 1) To introduce Selectionism
 * 2) To explain and put into perspective Dr. Edelmans work in Neural Selection
 * 3) To Introduce the idea of a Self-Organizing Map

Selectionism: How neurons compete for output
In order to best understand this topic, I want to familiarize you with the state of Neural Networks in the 1970s. At that time David Marr was theorizing about the nature of the memory circuits in the brain, under the assumption that the complex connection structures involved in Natural Neural Networks could be converted into mathematical calculations, which would explain the nature of the neural function. Unexpectedly it was proven that there was too much individualization in the connections between even brains of the same species. There wasn't even equivalence between the mathematical functions as they were translated. Up until this time it had been expected that DNA would drive the placement of connections, and that therefore there would be some commonality. As Dr. Edelman points out in Neural Darwinism: The theory of Neural Group Selection, DNA does not have the combinational complexity to individually determine the placement of the billions of synapses in the brain. What was needed were some very simple laws, that determined the placement of synapses, and some way to explain how order was found at higher levels of organization. Dr. Edelman who had recently become a Nobel Laureate for his work on the immune system, recognized a pattern that was equivalent to what he had found in the immune system, a self-organizing system that took seemingly random connections and turned them into usable information. He called this pattern, Neural Darwinism, because he suspected that the mechanism, like the one he had found in the immune system was a form of Darwinism that did not depend on genetics.

Inhibitive networks
The first problem was to find a mechanism that worked as a form of selection. While I am not sure who came up with this idea first, Neural Network Designers that gave up on Neural Calculation but not on Neural Networks, came up with the idea of an Inhibitive Network that acted to select from among multiple options through inhibition of other solutions. The idea being that the answer that survived the selective process was the output of the neural network. An example of a Natural Neural Network that has similar properties is Layer one of the Cerebral Cortex, which Marr, labelled as Mossy Fiber Neurons, Inhibitive Neurons with a high degree of connection between them. This technique was called Selectionism, but it has it's detractors, especially since most neurons in the brain are not inhibitive in nature.

Group Formation in neurons
If Inhibitive Networks, resulted in neurons competing for something, what where they competing for? Scientists noted that Neurons in the Cerebral Cortex formed into clusters called groups and that within each group, a pattern of firing was noted where one neuron in the center was amplified, and the surrounding neurons around it were suppressed. From time to time, possibly because of fatigue, the neuron that was amplified would change, but the group as a whole would respond consistently. So the net effect of the competition was to be the neuron that was amplified instead of one of the neurons that were suppressed.

As well, Marr's work had suggested that different types of neurons worked together to form circuits that did specific jobs, for instance in A Theory of Cerebral Cortex Marr suggested that the mossy fiber neurons, some parallel connective neurons and the Pyramidal Neurons in Layer 2 and 3 of the cortex, modified by neurons he thought were mathematical functions such as addition and division, that populated layer 4 would be a structure he called a Codon, and which he claimed acted as if it were a self-classifying content addressable memory. I call these groups of heterogeneous Neurons, Heterogeneous Groups. Unlike Marr's theory, I suspect that these heterogeneous groups are to some extent still connected by opportunistic connections.

The combination of self-classifying content addressable memory and Neural Group formation is a useful concept and finds its way into my memory theory.

The Theory of Neural Group Selection
Ok, now we get into the hard part of understanding Dr. Edelman's theory, Dr. Edelman went on to suggest that through the medium of feedback and feedforward signals he called reentry, that joined Neural Groups, through a rich set of interconnections, a second layer of selection happened that caused the Neural Groups to self-organize into maps. He called this theory the Theory of Neural Group Selection (TNGS). Unfortunately he went on to extend this theory in unfortunate ways that have since been questioned, however the basic TNGS theory before he extended it is an important piece of the puzzle especially for understanding the Cerebral Cortex.

Self-Organizing Maps
The next clue to self-organization in maps, lies in the work of Teuvo Kohonen, a Finnish professor who published a book called Self-Organizing Maps. Essentially he showed how an Artificial Neural Network could be designed that acted like scientists thought parts of the cerebral cortex acted, and mapped high dimensional data into basically 2 dimensional maps without losing important topological connections. Unfortunately while we could predict that in the brain, the placement of the natural maps would be influenced by the placement of the major connections within the brain, the location of a specific map, remained highly individualized.

An interesting side effect of the use of these Self-Organizing Maps (SOMs) was the use of a device with an SOM in it, to map the location of particular signals in the brain, based on an array of contacts Embedded within the brain. This technique allowed scientists to connect robotic arms to monkey brains, and have the monkey move the robotic arm in order to give itself a treat. By connecting the monkey brain through the device, and having the device analyze the action of the specific brain, a mapping of that brains specific reactions to specific stimuli could be made despite individualization of the map locations. All that was needed was to embed the array in the right general area, and the device would learn the individual neural structure of the brain in that area.

Assignment

 * Consider that seemingly completely random connections at the neural levels can Learn information by Natural Selection, to form gradually more complete Maps of the Environment, and that at the level of gyrus and sulci, (The folds of the brain) we can map consistently across brains in the same species the same general functions, does this not suggest a self-organizing system? Should we expect incremental increases in the amount of order at each layer of self-organization?

 * Consider that the mechanisms by which the brain adds order, are different at different layers of self-organization, does this suggest a common thread to self-organization, or does it suggest that there are many different types of self-organization? or Both?

 * Consider, that we can read a monkeys motor signals to its arm, and drive a robotic arm with them, does this mean we can read the monkeys mind? Or are there other barriers to reading the mind, than merely reading the impulses of the Neural Groups in the Motor Cortex? Considering that the SOM learns to read the monkeys movements by comparing the signals to the movement of the monkeys arm, how would we for instance read the formation of words, in the Vocal areas?

 * Why do you think that we do not have computer implants that can read our minds? Are there ethical considerations? Technical Considerations? Health considerations? Recently a professor at a major university injected himself with a RFID tag, why was this controversial? Some Nationalities are producing RFID credit cards you inject into the body, why might this seem premature?

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Conservation Entrepreneurship/Communication

Edward Tufte has written 4 wonderful books on design and communication of information. The books themselves are works of art that exemplify Tufte's approach to integrating context, information and design. Like all good entrepreneurs, one of his major themes is basing your efforts on "the needs of your customers" (in this case, listeners or consumers of information).

Beautiful Evidence, Edward Tufte, 2006. Graphics Press LLC. The chapter on The Cognitive Style of PowerPoint: Pitching Out Corrupts Within, pp 156-185, makes a powerful argument against this controversial tool. He synthesizes here what he has published elsewhere (look on www.edwardtufte.com) – a damning indictment of the lack of rigorous thinking at NASA, as illustrated by the use of PowerPoint as a main communication tool in the crisis situation of the Columbia accident. He cites the Columbia Accident Investigation Board. Tufte's remedy is to replace PowerPoint presentations with "paper handouts showing words, numbers, data graphics, images together".

An interesting format of conference presentation, "Lightning Presentations" is presented here: http://www.insidehighered.com/advice/2010/08/30/hoelscher

Cialdini, Robert B. 2001. Influence: Science and Practice. This book reviews a vast literature of social psychology to explain how "influence professionals" use our "mental shortcuts" against us -- and how we can tap into this knowledge to be more effective at influencing others. The key factors are: reciprocation, consistency, social proof, liking, authority and scarcity. Recipricocity is a basic norm of human culture, and giving something before asking for a return favor is very powerful. Example: Hare Krishna in an airport gives you a flower, then asks for a donation. People also have a strong desire to look and be consistent with their words and deeds. Thus, securing an initial commitment -- especially if it is active and public -- is a powerful way to obtain agreement with a later request. The basic tactic is to start with a small request to ensure later compliance due to consistency pressure; e.g. signing a petition and then being requested for a donation. Social proof is the principle that people's decisions are influenced by what other people believe or do. Powerful imitative effects have been found in purchase decisions, charity donations, etc. -- especially in situations of uncertainty and when leaders and followers are similar. People prefer to say yes and buy from individuals they like. Liking is influenced by physical attactiveness, similarity, and repeated contact (especially when the contact occurs under positive circumstances). An example is using a beautiful model next to an automobile; empirical data shows that consumers deny influence but are in fact influenced by such association -- the model makes the car more desirable. The principle of authority was documented by Stanley Milgram's experiments with test subjects applying electroshock (simulated) to someone posing as a test subject partner, attributed to the authority role of the experimental leader. Research shows that people increase deference in response to titles, clothing, and automobiles. Finally, people assign more value to opportunities when they are less available, as exemplified by "limited offer" and "deadline" tactics.


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Introduction to Non-Genetic Darwinism/Physics of Self-Organization

Physics of Self-Organization
Although some of the terms used in this article are engineering rather than physics terms, the subject is under review by physicists, as a result of recent work on Quantum Gravity.

AIMS

 * 1) To Introduce the 2nd Law of Thermodynamics
 * 2) To build an understanding of what it means
 * 3) To note exceptions or special interpretations of the law
 * 4) To build an new understanding of Entropy
 * 5) To Introduce the idea of an Energy/Entropy Balance
 * 6) To show that Self-organization may not be an exception to Entropy

2nd Law of Thermodynamics
'' Any Process that starts in an equilibrium state, and ends in another will go in a direction that causes the entropy of the process plus the environment to increase. ''

The Concept of Equilibrium States
Equilibrium is a state of zero net force. There are two types of equilibria, Stable, and Unstable. A Stable Equilibrium, is where, when you put a force against the mechanism it returns to the state of equilibrium, and oscillates across the equilibrium point until the energy associated with the force is all used up, at which time it remains at the equilibrium point, and an Unstable equilibrium is a state where when you add force to the state, the system accelerates away from the equilibrium state.The examples of these two different states can both be illustrated by the action of a pendulum There are two equilibrium points that are important, the equilibrium point where the pendulum is at the bottom of its swing, and the equilibrium point where the pendulum is at the top of its swing, and it actually stops in space for a split second. The top of the swing is an unstable equilibrium, and the point where the pendulum is perpendicular to the ground at the bottom of the swing, is the stable equilibrium. You can tell because the pendulum always ends up in the stable equilibrium when it quits moving.

History of the 2nd Law
The 2nd Law of Thermodynamics was developed as a result of the development of the Steam engine. It attempted to predict why some types of mechanisms resulted in reversible processes while others resulted in irreversible processes. The answer was that moving from one state to another involved work, and therefore, movement to a more entropic state, reversible processes added energy to overcome the entropy, while irreversible processes stayed in the more entropic state because they had already used the available energy in getting there.

The 2nd Law of Thermodynamics is why physicists do not believe in perpetual motion devices, unless there is an energy source, no process can trend towards greater entropy perpetually.

The Problem with the Law
While the law does exactly what it claims and describes the case of a process moving from one equilibrium state to another, It does not well describe the case where energy is added to a system, or energy is taken away from a system. Furthermore, it doesn't describe the case where entropy flows from one system to another. Worse, at the time it was developed we didn't know about quantum uncertainty, so it was thought that equilibrium was the standard case. Now we are not so sure that Equilibrium even exists. Perhaps what we think of as equilibrium is only a temporary balance between energy going into the system and energy leaving the system. Inside the system much can be happening, just to transfer the energy from the location where it comes into the process to the location where it leaves the process. We just think it is Equilibrium because we don't see the dynamics which are below our abiltiy to observe with the naked eye.

Introduction to Quantum Uncertainty
I am not going to explain Quantum Uncertainty to you here, it is too big a subject and too confusing for most, even advanced physics students have trouble understanding it. However I will describe it for you, and let others do the explanation. At a certain level of physics, the laws of Newtonian Mechanics begin to break down, any element smaller than this, begins to become uncertain in one manner or another, to the point where describing the location of an object below this threshold, involves probability rather than measurement. When we consider that our whole world of things that stay in place when you put them somewhere, is built on top of the Quantum Layer, the idea of Equilibrium is questionable. If you actually could reach equilibrium the quantum level elements that make up our stuff, would be predictable, not statistically measured. This draws into question the assertion made at Newtonian Physics Levels that an object is in equilibrium if no forces are applied to it. The second law of Thermodynamics is not Quantum-Safe because it demands equilibrium under this definition as part of its definition. Some scientists believe that there is a temperature, absolute zero, or 0 degrees Kelvin, at which quantum Uncertainty stops but that would preclude equilibrium at warmer temperatures.

Entropy
The last concept you need to understand when talking about the second law is the concept of Entropy. To understand this concept, let us restate it in terms of energy, as energy flows from the point of greatest potential to the point of lowest potential, entropy increases. Thus at a point where there is no differential in energy across the whole Universe, a state called the energy death of the Universe, Entropy will be at its highest point. Another way of looking at this, is that when energy is flowing from a high potential to a lower potential that is when the second law seems to be most correct. However there is another side to entropy, that of a breakdown in complexity that goes with the increase in entropy. When entropy increases supposedly the breakdown in complexity also increases. One interpretation of this, is that disorder increases with entropy.

Open Systems Theory, When is a Law not a Law?
It is this latter interpretation that makes life especially difficult for the second law of thermodynamics. Despite the fact that entropy is increasing at least in the environment around the life-form, it seems to decrease within the life-form itself during growth. The second law, doesn't deal well with growth, if it is interpreted in the loss of complexity manner. What it seems is that life somehow is increasing the entropy of the world around it, but reversing that trend within its own body. Now this is not illegal by any means, since it is the entropy of the environment plus its own body that determines if a process follows the second law, but it does seem to buck the trend. In fact anything that increases order seems to buck the trend described by the second law.

Scientists that are not sure, have decided that the best way to claim compliance to the second law, is to describe life in the terms of an Open System versus a closed system. In an open system, the environment can be involved in the determination in a closed system the environment can't. If the law didn't say plus environment, this would be called an exception to the law, however the law as stated today, does say plus the environment, so no exception is called for.

It is a tautology that, if there is but one exception to a law, then by definition it isn't a law. A law applies always.

Failure of the 2nd Law to predict Astronomical Features of the Universe
It is naturally interesting that within the last few years, researchers have noted a number of places where they have assumed that they can apply the second law, but when their research is done the answers they get back, are different than what has been observed. This suggests that either their observations are suspect, or the law as it is formulated, is not up to the task of predicting some of the effects that have been observed. As a result some scientists have put the second law on probation, and others are considering how to reformulate it to meet the needs of today's science.

The Red Shift
One of the places where the second law is not operating as expected, is in the Red Shift, if scientists are correct about what the red shift is, when an object is moving away from you, and you observe the frequency of the light that comes from it, you can expect that the frequency of the light will shift slightly towards the red end of the spectrum. Scientists use this theory to find known frequency spectra for particular elements in the spectral signatures of distant stars.

Now an interesting problem occurs when you can reach out and gather the spectra from more distant stars, and that is that the further away the star is, the deeper the frequency is shifted to the Red. What this seems to suggest, is that the Universe is expanding and the further away a star is from us, the faster it is moving away from us. As our telescopes get better, we can see galaxies further and further away, and detect deeper and deeper red shifts.

The problem comes from the fact that we can also detect from the dimness of stars how far the light has travelled to get to us. Distant galaxies are too dim for our current calculations of the distance they should be from us, even according to the red shift to be correct. Either there is some change in the speed that galaxies are moving now than they were then, that throws off our calculations, and indicates that the galaxy is expanding faster now than it did then, or all our calculations imply some sort of black energy that forces the Universe apart, something that is not found in our current physics.

The problem is that this doesn't jibe with our calculations of how fast the Universe should be cooling, and how quickly it should begin to collapse back into itself after the Big Bang. Either the Big Bang is wrong, or some laws including the second law of thermodynamics is wrong, or both. There could be a different interpretation there are so many other factors when we look at astronomical intergalactic distances, but the biggest things should follow the most basic laws more closely, if only because individual variation is lost in the distance.

Galaxy Formation
Another indication that the second law might be wrong, is in galaxy formation. Quite simply put, there are too many galaxies and in too many orientations for our understanding of the Universe to be correct. If the Second Law worked the way historically it has been said it does, there wouldn't be nearly the number of galaxies that we can now detect via the Hubble and its sister satellites. Somehow this concept of Entropy always resulting in a reduction in complexity must be wrong, not only in life-forms but in the formation of Galaxies as well. Either that, or the Universe is much older than we think it is, and it has gone through more than one Big Bang. One theory that is suggested is the Big Bounce theory, where the Universe periodically collapses in on itself and has another big bang. But that is a fairly new theory and not all the mathematics of it have been worked out.

Failure of the 2nd Law to predict Quantum Level Features of the Universe
One of the problems with the current formulation of the second law, lies in its assumptions of Equilibrium. What does Equilibrium mean, when it is applied to the Quantum Layer where the location of small elements is no longer determinable? Our physics assure us that nothing moves without a force being applied, yet if nothing ever stays still long enough to measure it accurately, how can we say that no force is being applied? And thus, does the concept of Equilibrium really have any validity? Quantum Uncertainty leaves us with the impression that at the quantum layer things are much more chaotic and dynamic than at the Newtonian layer on which science the original law was based.

Why does Quantum Chaos result in Order?
We are left with the impression that underneath the Newtonian layer of reality where Equilibrium makes sense, the Second law, doesn't make sense anymore, but somehow the order of our Newtonian Universe emerges from this chaotic under-layer, and then the second law makes sense. But if this were actually true, then the law as it is presently formulated is wrong. If only because it describes Any process, and Quantum processes still must be taken into account. It is not that Quantum Physicists have thrown away the second law, so much as they are using it without thought that it can't apply because in the quantum layer equilibrium doesn't make sense. But again we are stuck wondering why, when the quantum level is so complicated, and dynamic, order has precipitated out of the chaos, when entropy is supposed to break things down and reduce complexity.

One possible interpretation is that there is an intermediate state between energy and entropy at which order precipitates out of chaos. Called by some the Edge of Chaos, such a state would be a sort of equilibrium of its own. I assume that we can find this point by monitoring the Energy/Entropy Ratio. When the Energy entropy ratio is 1 we are at equilibrium, at some point around this ratio we will get order precipitating out of chaos, at some point below this ratio we will get a breakdown in order, and if we get too large a ratio, we will increase the dynamism of the system and get chaos.

Perhaps, the function of the quantum uncertainty increasing entropy is for quantum particles to become larger, better defined particles. To stop being uncertain bosons and anti-quarks, and become fairly certain subatomic particles, and then eventually particles, protons, neutrons, anti-neutrons, and so, as the scope of the microverse becomes larger. Maybe we live in a very uncertain universe, constantly decaying into certainty. The Big Bang started very small, and very uncertain. And resolved to become us and our observable universe. Perhaps the move towards always greater order at always greater magnitudes of physical size is an expression of entropy. Perhaps it has the unrecognized potential to create greater and greater completixity, at greater and greater size, until we all collapse back into a singularity of complete order, and then become the next universe. Perhaps the equilibrium sought is larger, rather than smaller.

Towards a new formulation of the 2nd Law
Perhaps we need to rethink the 2nd law, or perhaps we simply need to rethink how we deal with Entropy and what we expect it to do. Probably both, since the second law is based on the classical definition of entropy. If we described the terms of the 2nd law in terms of the Energy/Entropy balance instead of on equilibrium of forces, it might be more accurate. On the other hand, it might work as it is, if we redefine equilibrium as a state of balance between energy and entropy.

Open System versus Incremental/Decremental System: Which is the better description?
If it is the Energy/Entropy balance that is important, not the source of the energy, then the concept of an open system is really only important in that there is a source of energy outside the system. What is important is that in an Incremental or Decremental system, the meaning of Entropy as breaking down complex elements into simple ones with a release of energy, being dependent on the Energy/Entropy balance, is critical to whether complexity is increased or decreased within the system. Life must therefore increase the Energy/Entropy balance locally during growth.

Decremental Systems within an Incremental System, Classical Entropy
If the Universe is expanding and energy in the Universe is increasing, then, why does entropy continue to break down complex structures into simpler ones and release energy? The answer lies in the relative energy/entropy balance in the local area, rather than the Universal Energy/Entropy balance. Because Entropy is not uniformly spread across the Universe, but depends on the processes within the universe to be created, the net effect is that some areas of the universe get lower Energy/Entropy balances than others. Within these local confines, with higher entropy, it is possible for the Entropy to cause the breakdown of complexity despite the fact that a foot further over the Energy/Entropy balance is different.

Assignment

 * Read up on the Big Bounce Theory, does it make sense?

Does this make sense in the Quantum Realm? What forces are being applied at the quantum level when an object seems to be at rest on the newtonian level of physics?
 * Consider Quantum Uncertainty, if you can't predict where something is going to be, can you say it hasn't moved? How does this fit with the idea of equilibrium where equilibrium states that no forces are being applied. You remember in basic science the law of inertia, where an object in motion stays in motion and an object at rest stays at rest until acted on by an outside force.

 * Consider the idea of order emerging from chaos. What possible mechanism could cause this? Would the Energy/Entropy balance be a factor?

 * In chemistry Entropy is a factor in whether or not certain chemicals will combine, Is this an indication that self-organization (chemicals combining) is a result of local entropy levels?

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User talk:Nickel3956~enwikiversity

 Hello Nickel3956, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
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 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! --AFriedman 22:24, 15 February 2009 (UTC)

 Hi! My name is WikiversityJack. I am a pumpkin and a Wikiversity mascot. My aunt told me that fun + learning = Wikiversity. Let me know if I can help you out.

Your account will be renamed
Hello,

The developer team at Wikimedia is making some changes to how accounts work, as part of our on-going efforts to provide new and better tools for our users like cross-wiki notifications. These changes will mean you have the same account name everywhere. This will let us give you new features that will help you edit and discuss better, and allow more flexible user permissions for tools. One of the side-effects of this is that user accounts will now have to be unique across all 900 Wikimedia wikis. See the announcement for more information.

Unfortunately, your account clashes with another account also called Nickel3956. To make sure that both of you can use all Wikimedia projects in future, we have reserved the name Nickel3956~enwikiversity that only you will have. If you like it, you don't have to do anything. If you do not like it, you can pick out a different name.

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Renamed
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title: User talk:Nickel3956~enwikiversity


text:
User talk:Mindytonti

 Hello Mindytonti, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! --AFriedman 22:24, 15 February 2009 (UTC)

 Hello , we are the Baah and Bahh twins and we'd both like to welcome you to Wikiversity. While you sit back and explore Wikiversity, enjoy some of our fresh goat milk. You can help us learn by creating new learning resources or help us by improving the guided tours and other exploration tools. We look forward to seeing you around. Baaaahbye for now.

title: User talk:Mindytonti


text:
Talk:Introduction to Non-Genetic Darwinism/Physics of Self-Organization

Please explain the concepts of equilibrium and quantum uncertainty, I think this would be very helpful to students. I've added relevant sections to the course outline. --AFriedman 02:58, 16 February 2009 (UTC)

No, I don't think so, what I can do, is some links to other sites that try and explain, but if I did more than lip service to Quantum Mechanics I would have to rewrite the course, and then it would be about Quantum Uncertainty rather than Non-Genetic Darwinism and self-organization. Quantum Uncertainty is so hard, to understand that Einstein once refused to consider it. I'll expand on the subject a little if you insist, but I certainly won't even try to explain it. I may believe in unit fields but I know my own limitations and explaining quantum physics lies outside of them.--Graeme E. Smith 07:21, 16 February 2009 (UTC)
 * I saw what you've written in those sections, and it looks about right for now. --AFriedman 14:47, 16 February 2009 (UTC)


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Social psychology (psychology)/Evaluation

Unit: Social Psychology/G (7125/6666), 2008/9 Unit convener: James Neill

This reflective statement and the accompanying student satisfaction results were the basis of a successful application for a Unit Convener Award, Faculty of Health, University of Canberra, 2009.

The teaching innovation involved achievement of high student satisfaction levels with teaching of social psychology using open access blogs and wikis to enhance student engagement, reflection, and sharing during 2007 and 2008.

Overview
During 2007/8, I was the unit convener for Social Psychology, a third-year undergraduate unit. I also opted to teach all the lectures and tutorials (enrolment ~70-80 students) in an effort to:

 * 1) Stimulate student engagement and raise educational quality,
 * 2) Ensure consistent quality of delivery, and
 * 3) Implement innovative use of student-oriented, experiential, educational technologies, particularly the use of student blogs (2007) and wiki-based (2008) e-portfolios.

Teaching and learning theory or research
Fundamental to the delivery of this unit was the use of interactive, experiential, educational technologies. The underlying idea was to enable students to explore, record, and share their interactions with the unit's learning activities openly and online. In this way, students' learning (as opposed to teachers' teaching) was made more prominent. The role of the instructor, in this approach, was to facilitate students in learning how to utilise blogging (2007) and wikis (2008) to demonstrate their engagement with the unit's learning activities. To see students' work, visit:

 * 2007: http://ucspace.canberra.edu.au/display/7125/List+of+all+blogs
 * 2008: http://en.wikiversity.org/wiki/Social_psychology_(psychology)/Participants

Implications of this teaching and learning innovation for the faculty
The 2007/8 teaching of this unit demonstrated two successful, interactive approaches to teaching and learning: student blog-based and wiki-based e-portfolio assessment exercises. Such practices offer examples that other teaching staff may consider, particularly with regard to addressing new Performance Expectations for Academic Staff, especially Step 18: Demonstrate the adoption of best practice use of educational technologies.

This new performance expectation represents a significant challenge for many staff because student-oriented pedagogies which utilise new educational technologies are under-developed at UC. Successful implementation of such approaches, like other innovative teaching practices, require a significant level of enthusiasm and facilitative expertise by instructional staff. Hopefully these demonstrably successful models and examples will be of value to others.

Student satisfaction
The Unit Satisfaction Survey (USS) results for these units revealed a consistently high level of student satisfaction with both approaches (see Table 1), with Overall and Average student ratings above 6 out of a possible 7 (compared to a faculty average of 5.6 and UC average of 5.5 in 2008). Students appear to have exhibited a preference for the 2007 (blog-based) approach than the 2008 (wiki-based) approach, probably because students found blogs simpler and easier to edit.

The full USS reports, including students' open-ended comments are available from:

 * 2007: http://ucspace.canberra.edu.au/download/attachments/753755/7126_6666_2007_USS_Results.rtf
 * 2008: http://ucspace.canberra.edu.au/download/attachments/54034478/7126_6666_2008_USS_Results.doc


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CIRCLE

course background:
We think that the basic objective of schools and universities is to prepare students for the knowledge society, i.e. to train their knowledge networking skills. Students should get used to search for information and to transform it into knowledge through individual and collaborative, participative techniques. In this context, we would like to stress the perspectives offered by various web 2.0 tools such as weblogs, wikis, video conferences and discussion boards. Thanks to all these different tools, people (students) from different countries and cultures can take part in the collective construction. Besides technological skills this implies, of course, a high degree of communicative competence social skills.

 * style="width:60%; background:#f5fffa; border-left:1px solid #cef2e0; padding-left:1.2em;" valign="top" |

course description:
Follow link for a brief course description: course description

specified period
13th Oct – 1st Dec (6 weeks)

project schedule
Project Schedule

first instructions
Follow link for first instructions: Instructions
 * }

working groups:
working groups

participants:
In order to sign up, please click on edit (here above) and replace "xxx" by your personal information

title: CIRCLE


text:
CIRCLE/schedule

Some day, this will be a learning project for students and teachers at CIRCLE, Lund University. The course Internet_and_Project_Competence of Michael Kratky will serve as role model.

title: CIRCLE/schedule


text: Category:Sites with an accompanying Moodle course


text:
User:Kuvaly

cs.wiki

title: User:Kuvaly


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Talk:Russian/Introduction

Good article but am I the only one to see interogation points ("?") from the "consonant stems" part until end ? (english words are watchable but I see loads of ???? so it's surprizing...) Which police has been used ? Noob power --Adulcar 15:37, 16 February 2009 (UTC)

point to moved alternate course page
There was a fork created with a bad pagename, Course:Introduction to Russian. I've moved this to Russian/Introduction/Old in case that there is content there which should be merged. If not, the page should be deleted. --Abd 15:40, 26 August 2011 (UTC)

title: Talk:Russian/Introduction