OpenLLM-France/wikimedia huggingface dataset:
# Object Oriented Programming

Object-Oriented Programming (OOP) is a model of programming that uses Objects as representation of data and the data's properties. Objects can be defined as fields of data with unique properties, or attributes and methods (functions). At its heart, object-oriented programming is a mindset which respects programming as a problem-solving dilemma on a grand scale that requires careful application of abstractions and subdividing problems into manageable pieces. Compared to procedural programming, a superficial examination of code written in both styles would reveal that object-oriented code tends to be broken down into vast numbers of small pieces, with the hope that each piece will be trivially verifiable. OOP was one step towards the holy grail of software re-usability, although no new term has gained widespread acceptance, which is why "OOP" is used to mean almost any modern programming distinct from systems programming, assembly programming, functional programming, or database programming. Modern programming would be better categorized as "multi-paradigm" programming, and that term is sometimes used. This book is primarily aimed at modern, multi-paradigm programming, which has classic object oriented programming as its immediate predecessor and strongest influence.
Historically, "OOP" has been one of the most influential developments in computer programming, gaining widespread use in the mid 1980s. Originally heralded for its facility for managing complexity in ever-growing software systems, OOP quickly developed its own set of difficulties. Fortunately, the ever evolving programming landscape gave us "interface" programming, design patterns, generic programming, and other improvements paving the way for more contemporary Multi-Paradigm programming. While some people will debate endlessly about whether or not a certain language implements "Pure" OOP—and bless or denounce a language accordingly—this book is not intended as an academic treatise on object oriented programming or its theory.
Instead, we aim for something more pragmatic: we start with basic OO theory and then delve into a handful of real-world languages to examine how they support OO programming. Since we obviously cannot teach each language, the point is to illustrate the trade-offs inherent in different approaches to OOP.
Although OOP is quite complex to beginners it becomes easy when you first fully understand what pillars the concept of OOP is built on.

## Table Of Contents

* Introduction To OOP
* Classes
** Properties
** Methods
** Constructors
*** Lifetime Management
** Getters and Setters
** Static vs Dynamic
** Private vs Public
* Encapsulation
* Inheritance
** Subclasses
** Superclasses
* Polymorphism
* Abstraction
* Advanced Concepts
** State
** Interfaces
* See also
# Object Oriented Programming/Abstraction Barrier
## Abstraction Barrier
### Data Abstraction

The idea behind data abstraction is to identity certain ways or operations data is expressed and manipulated. Once this is done, the goal is then to use only those operations when it comes to manipulating data. Restricting the use of operations, makes it much easier to change the representation of abstract data without changing a program's behavior.

### Examples

A few examples of abstraction barriers can be found in this table from composing programs.com, which demonstrates how different types of abstraction barriers can handle rational numbers.
| Parts of the program that... | Treat rationals as... | using only |
| Use rational numbers to perform computation | whole data values | add_rational, mul_rational, rationals_are_equal, print_rational |
| Create rationals or implement rational operations | numerators and denominators | rational, numer, denom |
| Implement selectors and constructor for rationals | two-element lists | list literals and element selection |
The last column 'using only' demonstrates an extraction barrier being enforced. The functions are called from higher levels then, implented through a lower level of abstraction.

## Abstraction Barrier Violations

Violations occur when a program that has the ability to use a higher level function, instead uses one in a lower level. An example would be referring directly to numerators and denominators in your code rather then just using mul_rational, a function that does not assume anything about how a rational number is implemented.
Composingprograms.com examples -
Correct: code is simple and makes no assumptions on rational number implementation.
Incorrect: violates one abstraction barrier, by directly referring to numerators and denominaters making code more complex then needed.
Also incorrect due too making extra assumption rationals are represented as two element list. This would be considered two abstraction barrier violations.
In short, abstraction barriers make programs easier to maintain and modify, by using as few functions as possible to represent operations so fewer changes will be needed to be made to the code in order to change it's representation. Remember simplicity is better then complexity, especially when it comes to abstraction barriers.

## References

* ↑ , Data Abstraction
# Nuclear Medicine/Infection

Nuclear Medicine:TOC
Infection

## Immunocompromised Infection

* (Please add info)

## Post Transplant/Operative
### Cardiac

* Scintography of post heart transplant infections have not been extensively studied, however, Gallium, Indium-111 WBC, and Tc99m HMPAO have been used.
** PMID 16485569 -- "Value of radionuclide studies in cardiac transplantation." (Flotats A, Carrió I. 2005 Ann Nucl Med. 2006 Jan;20(1):13-21.)

## Central Nervous System

* (Please add info)

## Cardiovascular

* Also see above "Post Transplant/Operative" - Cardiac

## Appendicitis

** ^ SAMPLE PMID 19426925 - "2008 Santa Fe Bone Symposium: update on osteoporosis." Lewiecki EM, Baim S, Bilezikian JP, Eastell R, LeBoff MS, Miller PD. (J Clin Densitom. 2009 Apr-Jun;12(2):135-57.)
Acronyms used:
* MDP - Medronate or
* 18F-FTG PET - (18)Fluorine-fluorodeoxyglucose Positron Emission Tomography
# Nuclear Medicine/Introduction

Template:Nuclear Medicine/TOC
Nuclear Medicine is a rapidly changing field with new advances being made daily. As a consequence, any textbook becomes out-of-date almost immediately after it is published. Thus, the WikiBook format is ideal for a textbook of nuclear medicine, as updates can be made constantly as new information becomes available.
This textbook is meant to be used by residents, medical students, and practicing physicians in radiation oncology.
At the creation of this textbook, the emphasis is placed on keeping the content up-to-date in terms of publishing the results of major trials. It is not meant to be an encyclopedic work. It is impossible to include the results of all trials relating to radiation oncology. Publishing every little detail of each and every trial would bog down this resource and make it difficult to use. Its main purpose is to be a useful reference for those wishing to gain knowledge about clinical nuclear medicine.
As it is a WikiWork, its purpose may change.
This book is dedicated to the patients who are the inspiration to those of us involved in this field, as well as to the pioneers in medicine, without whom this textbook could not exist.
If you wish to contribute to this wiki, please see the instructions to authors at the following pages:
* a working list of standards
* Talk page.

## Medical Disclaimer

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The medical information provided on Wikibooks is, at best, of a general nature and cannot substitute for the advice of a medical professional (for instance, a qualified doctor/physician, nurse, pharmacist/chemist, and so on). Wikibooks is not a doctor.
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Nothing on Wikibooks.org or included as part of any project of Wikimedia Foundation Inc., should be construed as an attempt to offer or render a medical opinion or otherwise engage in the practice of medicine.
# Nuclear Medicine/Vascular
## Mycotic Aneurism

* A mycotic aneurism is an infection of the arterial wall of an aneurism. It can be somewhat confusing but infections can happen in aneurisms or an infection of the artery can cause an aneurism.
^ - PMID 2919901 "In situ prosthetic graft replacement for mycotic aneurysm of the aorta." (Chan FY; Crawford ES; Coselli JS; Safi HJ; Williams TW Jr. Ann Thorac Surg 1989 Feb;47(2):193-203.)
# Nuclear Medicine/Vascular/Shunting
## Shunting

* An abnormal communication between arterial and venous circulation.

### Procedure

** ^ "Hepatopulmonary Syndrome: A Prospective Study of Relationships Between Severity of Liver Disease, Pa02 Response to 100% Oxygen, and Brain Uptake After 99mTc MAA Lung Scanning." (Krowka MJ, et al. Chest/118/3/Sept 2000.)
** ^ ISBN 0-323-02946-9 -- "Nuclear Medicine: The Requisites, Third Edition (Requisites in Radiology)." (Harvey A. Ziessman, Janis P. O'Malley MD, James H. Thrall MD. 2006. Mosby, Inc.)
Acronyms used:
# Nuclear Physics

This book is part of a series on Modern Physics:
Waves General Mechanics Special Relativity Quantum Mechanics Electrodynamics Nuclear Physics

## Atomic Nuclei

* Molecules - an Analogy
* Nuclear Binding Energies
* Radioactivity
* Nuclear Fusion and Fission
* Atomic Nuclei/Problems
# Nuclear Physics/Atomic Nuclei/Problems

1. How many helium atoms are emitted by 1 gram of Argon?
2. The activity of a sample prepared decreases 1.5 times after 6.0 months. Find its half-life period.
3. Find the half life and mean life times of Co$^{55}$ if its activity is known to decrease 5.0% per day. (consider the decay product to be nonradioactive.)
4. The half lives of two nuclides are 20 days and 6 years respectively. Calculate their specific activities.
5. The half life of a radioactive cobalt isotope is 71.3 days. What fraction of this sample are decayed/left during/after 3 months?
6. How may alpha and beta disintegrations does U$^{238}$ undergo before forming a stable Pb$^{206}$?
7. If Ra$^{226}$ decays 5 alpha and 4 beta particles, what is the isotope formed?
This page or section of the Modern Physics book is a stub. You can help Wikibooks by expanding it.
# Linguistics/Glossary

Descriptivism - Observation of how language is used. This is generally the method used in linguistics. (Cf. prescriptivism)
Morpheme - An individual unit of meaning in a word. For example, the word uninterested may be analyzed as consisting of three morphemes: un-, interest, and -ed. (Morphemes are sometimes also considered to contain units which have no basic phonological representation, see reduplication, truncation).
Morphology - The study of how units of meaning are combined together to create words. The basic units of reference in morphology are morphemes.
Phone - A unit of sound or a gesture used to produce one in a language. Note that different phones may have the same underlying representation in a language (see phoneme).
Phoneme - A class of phones which contrast with other phonemes in a language. For instance, in English the phoneme /t/ is realized as a plosive at the beginning of a word (e.g. term) but as a flap between vowels in most positions (e.g. butter), while in other languages these two sounds are different phonemes.
Phonetics - The study of the different types of sounds used in human language and how they are produced and perceived. The basic units of representation in phonetics are phones.
Phonology - The study of how languages group phones together and use them to encode meaning. The basic units of representation in phonology are phonemes.
Prescriptivism - Ruling on how language should used. This is common in language instruction but is generally irrelevant to linguistics. (Cf. descriptivism)
Register - a different type of speech adopted by a language-user depending on context
Rounding - "puckering" of the lips while producing a phone, most commonly a vowel
# Linguistics/Language Acquisition

Language Acquisition is the study of how children learn languages.

## Phonology

Children generally develop phonemes that are cross-linguistically more common, before they develop rarer ones. For example, children tend to be slow to acquire the English "th" sounds, pronouncing them as "d" and "t" and the "a" sound in American English apple is likewise difficult. It may take as long as seven years for children to learn all of a language's phonemes.
Likewise, phonotactics may be difficult. The "str" cluster in "street" is rare cross-linguistically, and difficult for children to pronounce, and they will tend to pronounce it "sreet" at first.

## Lexicon

Children learning languages fall into two types. They either learn social words first, like "hi" and "more" or nouns, like "mama," "dada," and "doggy."
Children are also limited in their lexicon, and will extend words they know to cover similar cases. For example, the word "doggy" may be used to describe cats, cows, and tables--anything with four legs.

### Numbers

Children learn, 1, 2, and 3 in that order, and then suddenly learn to count.
# Linguistics/IPA Chart
## The International Phonetic Alphabet

The International Phonetic Alphabet is a system of phonetic notation which provides a standardized system of representing the sounds of spoken languages. It may be represented visually using charts, which are given here for reference:

### Consonants

Pulmonic consonants are those made by obstructing the glottis (the space between the vocal cords) or oral cavity (the mouth) and either simultaneously or subsequently letting out air from the lungs. Where symbols appear in pairs, the one to the right represents a voiced consonant. Shaded areas denote articulations judged as impossible.
View this table as an image. :
| | Bilabial | Labiodental | Dental | Alveolar | Postalveolar | Retroflex | Palatal | Velar | Uvular | Pharyngeal | Epiglottal | Glottal |
| Plosive | p b | | t d | t d | t d | ʈ ɖ | c ɟ | k g | q ɢ | | ʡ | ʔ |
| Nasal | m | ɱ | n | n | n | ɳ | ɲ | ŋ | ɴ | | | |
| Trill | ʙ | | r | r | r | | | | ʀ | | | |
| Tap or Flap | | | ɾ | ɾ | ɾ | ɽ | | | | | | |
| Fricative | ɸ β | f v | θ ð | s z | ʃ ʒ | ʂ ʐ | ç ʝ | x ɣ | χ ʁ | ħ ʕ | ʜ ʢ | h ɦ |
| Lateral fricative | | | ɬ ɮ | ɬ ɮ | ɬ ɮ | | | | | | | |
| Approximant | | ʋ | ɹ | ɹ | ɹ | ɻ | j | ɰ | | | ʢ | |
| Lateral approximant | | | l | l | l | ɭ | ʎ | ʟ | | | | |
Coarticulated consonants are sounds that involve two simultaneous places of articulation (are pronounced using two parts of the vocal tract). In English, the [w] in "went" is a coarticulated consonant, because it is pronounced by rounding the lips and raising the back of the tongue. Other languages, such as French and Swedish, have different coarticulated consonants.
View this table as an image :
| ʍ | Voiceless labialized velar approximant |
| w | Voiced labialized velar approximant |
| ɥ | Voiced labialized palatal approximant |
| ɕ | Voiceless palatalized postalveolar (alveolo-palatal) fricative |
| ʑ | Voiced palatalized postalveolar (alveolo-palatal) fricative |
| ɧ | Voiceless "palatal-velar" fricative |
Non-pulmonic consonants are sounds whose airflow is not dependent on the lungs. These include clicks (found in the Khoisan languages of Africa), implosives (found in languages such as Swahili) and ejectives (found in many Amerindian and Caucasian languages).
View this table as an image :
| Clicks | Clicks | Implosives | Implosives | Ejectives | Ejectives |
| ʘ | Bilabial | ɓ | Bilabial | ʼ | For example: |
| ǀ | Laminal alveolar ("dental") | ɗ | Alveolar | pʼ | Bilabial |
| ǃ | Apical (post-) alveolar ("retroflex") | ʄ | Palatal | tʼ | Alveolar |
| ǂ | Laminal postalveolar ("palatal") | ɠ | Velar | kʼ | Velar |
| ǁ | Lateral coronal ("lateral") | ʛ | Uvular | sʼ | Alveolar fricative |

### Vowels

Below is a chart depicting the vowels of the IPA. The IPA maps the vowels according to the position of the tongue. In places where vowels are paired, the right represents a rounded vowel (in which the lips are rounded) while the left is its unrounded counterpart.
# Optimal Classification/Rypka Method/Equations/Separatory/Characteristic/Empirical/Separation
## Separation Stages
### Initial separation

$S_{j}={\frac {\left[(G^{2})-\sum _{l=0}^{R}n_{l}^{2}\right]}{2}}$, where:
   * S$_j$ is the initial empirical separatory value for each characteristic, where,
       j = 0...C and is the index of the jth characteristic in the group and C is the number of characteristics in the group, and,
       l = 0...R and is the truth table value of the jth characteristic, where R is the truth table size, where:
             R = V$^0$, and,
            * V is the highest value of logic in the group and,
            * 0 is the target set exponent for a single characteristic, and,
   * G is the number of elements in the bounded class.

### Subsequent separation

$S={\frac {\left[(G^{2})-\sum _{l=0}^{R}n_{l}^{2}\right]}{2}}$, where:
   * S$_j$ is the initial empirical separatory value for each characteristic, where,
       l = 0...R and is the target set truth table index value, where R is the target set truth table size value, where:
             R = V$^K$, and,
            * V is the highest value of logic in the group and,
            * K is the number of characteristics in the target set, and,
   * G is the number of elements in the bounded class.

## Notes

* ↑ Must be applied initially to each characteristic. (The equations have been implemented in Mathcad and using the Visual Basic programming language. See Application_Example
# Optimal Classification/Rypka Method/Equations/Separatory/Characteristic/Empirical/Target Set
## Target Set Truth Table Value

$t_{i}=\sum _{j=0}^{K}v_{i,j}V^{(K-j)}$, where:
   * t$_i$ is the truth table value of the target set,
      * v$_{i,j}$ is the element's attribute value,
         * i is the ith element's index value, where,
             i = 0...G' where G is the number of elements in the bounded class, and,
         * j is the jth characteristic's index value, where,
             j = 0...K and where,
         * K is the number of characteristics in the target set,
            * V is highest value of logic in the group,
            * V$^{(K-j)}$ is the positional value of the jth characteristic.
$n_{t_{i}}=n_{t_{i}}+1$, where,
    n$_{ti}$ contains the multiset count for each truth table value.

## Notes

* ↑ As the characteristic with the greatest separatory value is moved to the next most significant position, K is incremented to expand the target set size from two characteristics to the number of characteristics in the group or the number of characteristics which result in 100% separation. For the initial target set with one characteristic the separatory value is computed individually for each characteristic in the group to find the initial characteristic with the highest separatory value.
* ↑ coefficient of association, $coa={\frac {n_{t_{i}}}{n_{R}}}$, see page 172 of the primary reference
# Optimal Classification/Rypka Method/Equations/Separatory/Characteristic/Theoretical
## Theoretical Separation
### The general identification equation

    $S_{j}={\frac {1-{V^{-j}}}{1-V^{-C}}}$, where:
      * S$_j$ is the theoretical separatory value per jth characteristic,
      * C is the number of characteristics in the group,
      * V is the highest value of logic in the group and
      * j is the jth characteristic index in the target set, where:
          j = 0..K and where:
            * K is the number of characteristics in the target set.

### Minimal number of characteristics to result in theoretical separation

    $t_{min}={\frac {\log G}{\log V}}$, where:
      * t$_{min}$ is the minimal number of characteristics to result in theoretical separation,
      * G is the number of elements in the bounded class and
      * V is the highest value of logic in the group.

## Notes

* ↑ See page 153, page 167, Fig. 2. & page 175 of the primary reference
* ↑ See page 157, Primary Schemes footnote of the primary reference
# Optimal Classification/Rypka Method/Equations/Separatory

* Element-related equations
* Characteristic-related equations
# Optimal Classification/Rypka Method/Equations/Separatory/Characteristic/Empirical

* Target set truth table values
* Separatory equation
# Optimal Classification/Rypka Method/Equations/Truth Table Size
## Truth Table Size-Related Equations

These equations derive the truth table size from the highest value of logic (V) and the highest number of characteristics (C} in the target set and in the group of the bounded class of elements which denote the range for the truth table values of the set and the group.

### Group size

    $Q=V^{C}$, where:
      * Q is the group size
      * V is the highest value of logic in the group,
      * C is the highest number of characteristics in the group.

### Target Set size

    $R=V^{K}$, where:
      * R is the target set size
      * V is the highest value of logic in the group,
      * K is the highest number of characteristics in the target set.

## Notes

* ↑ See page 158, Table II of the primary reference
* ↑ See page 172 of the primary reference.
* ↑ group - Columns C1 to C5 (0 to C, C=5)
* ↑ Target set - Columns C1 to C3 (0 to K, K=3)
# Optimal Classification/Rypka Method/Equations

* Truth Table Size-related Equations
* Separatory Equations
# Optimal Classification/Rypka Method/Examples/Application
#### Flag Recognition

Although the following example lacks an intuitive sense of optimization it serves as a good example of how optimization can reduce the number of queries. While the method of optimal classification is highly beneficial for reducing the number of queries required for manual identification, automated identification may be better served by use of a neural network.

##### Flag overlay grid

Designated areas (characteristics) for sampling background colors (states) of all flags (elements).

##### Systematic query

Starting with area "A" the query begins by asking for the color in this area of the flag. Suppose we have in our possession the flag of the Netherlands. The answer to the first query in regard to area "A" is RED which would remove 2/3 of the flags from further consideration. The next query for the color in area "B" would be RED which would serve to eliminate none of the remaining flags. In fact, since the colors in columns "D", "E" and "F" are the same for each remaining flag, we would not be able to eliminate any remaining flags until column "G" where the color BLUE would provide a unique answer to the final necessary query. Here all remaining flags except the flag of the Netherlands would be eliminated from further consideration. It would therefore take a minimum of seven queries using the systematic query method to establish the identity of the flag in our possession as belonging to the Netherlands.

##### Minimized query

The results of optimization are shown above and include a listing of the theoretical and empirical percentages. The original characteristic sequence is indexed in the bottom row. Starting with area "G" the query begins by asking for the color in this area of the flag. Suppose we have in our possession the flag of Ireland. The answer to this first query would be GREEN. The next query is for the color in area "F" to which we would answer ORANGE. Since no other flags have this combination of GREEN and ORANGE in these areas our query can end here. The method has minimized the number of necessary queries required to identify the flag by optimizing the order of characteristics. (Please note that there may be more than one minimal solution.)
# Optimal Classification/Rypka Method/Equations/Separatory/Characteristic

* Theoretical separation
* Empirical separation