OpenLLM-France/wikimedia huggingface dataset:
# Nissan 240SX Performance Modification/KA24DE and KA24E/Header/header reviews

Several Brands of Headers are Available for the 240SX Including:

## OBX

EGR inlet on the headers didn't line up well with the EGR pipe running from behind the engine. Had to extend the o2 sensor wire. The steering bolt scraps the headers causing car to "catch" briefly. Irritating, but you can notice a reasonable power gain.

## Greddy

The Greddy header is a 4-1 short header design that bolts to a secondary "downpipe" that ends at the location of the stock cat. These headers are prone to cracking, welds giving up around the collector and the flange on the head. These headers are very loud.

## DC Sports

Dyno Results of the DC Sports 4-2-1 Header on the KA24DE.
# Nissan 240SX Performance Modification/KA24DE and KA24E/Turbocharging

Turbocharging the KA24DE can produce great numbers if the tuning is right. It's easy to attain over 200rwhp with a basic turbo kit like the kind sold on ebay. Advanced tuning and internal engine modification has produced KA24 240SX's with 500+ horsepower.
For a budget turbo build, you can get a t25 turbo and 5-7 psi without having to upgrade the injectors or MAF. You will need a fuel management unit (rising rate fuel pressure regulator). A new fuel pump is recommended. You also need to be careful with the ignition timing. MSD's Boost Timing Master can retard the timing appropriately.
The stock internals have been proven to be quite resilient. There are individuals who have made 350+ rwhp on totally stock internals. Here are a few quick limits on stock KA24DE internals. Keep in mind these are NOT EXACT NUMBERS. True limits can vary signifigantly from motor to motor. Before undergoing any turbocharging project, it is a VERY good idea to do a compression test. The closer to 180 psi you are the better. Anything below 150 and you generally want to start to think twice about a turbo project. So without further adue...
...Ofcourse, all of this is dependent upon a good tune. As long as your compression, timing, and air/fuel ratios are all within spec, and the motor is not continually beaten upon, you can expect extended performance out of these parts. Many individuals are making 300+ hp on stock blocks with 200k+ miles on them! For a wealth of information on turbochared tuning for KA24DE and KA24E motors, visit the forums at KA-T.org.
# Logic for Computer Scientists/Modal Logic

6.1 Modal Logic 6.2 Syntax 6.3 Kripke Semantics 6.4 Axiomatics 6.5 Multimodal Logics – An example
6.6 Modal Logic Tableaux
6.7 Translation Method
6.8 Temporal Logics
# Logic for Computer Scientists/Modal Logic/Multimodal Logics – An example
## Multimodal Logics - An example

If modal logics is to be used for expressing the knowledge of various agents, one needs operators which are parametrised: we introduce box operators $[a]$, which are paramtrised by arbitrary terms $a$; the same holds for $\langle a\rangle$, the parametrised diamond operator.
   A king, wishing to know which of his three advisers is the wisest, paints a white spot on each of their foreheads, tells them the spots are black or white and that there is at least one white spot, and asks them to tell him the color of their own spots. After a time the first wise man says, "I do not know whether I have a white spot." The second, hearing this, also says he does not know. The third (truly!) wise man then responds, "My spot must be white."
The following is a formalisation using the abbreviation $\square F$, which stands for arbitrary nested paramtrised $$-operators. If we had only 2 wise man $A$ and $B$, $\square F$ would stand for $[A]F\land [B]F\land [A][B]F\land [B][A]F\land [A][A]F\cdots$. Hence in general $\square F$ stands for "$F$ is generally known".
$B\neq A\land C\neq A\land C\neq B$ the three wise are different
$\square (w(A)\lor w(B)\lor w(C))$ it is generally known, that one of them has a white spot
$\square (\forall x,y:x\neq y\to (\lnot w(x)\to [y]\lnot w(x)))$ it is generally known, that if someone has no white spot, the others know it
$[C][B]\lnot [A]w(A)$ C knows, that B knows, that A does not know the colour of his spot
$[C]\lnot [B]w(B)$ C knows, that B does not know the colour of his spot
The theorem to be proven is "$C$ knows that he has a white spot":
$[C]w(c)$
If we had a theorem prover for modal logics we could apply it to the above specified problem in order to obtain a proof and hence an explanation for the theorem.
In the rest of this section we will introduce two methods for the definition of such a theorem prover: A direct tableau calculus and a translation method in to first order classical predicate logic.
# Logic for Computer Scientists
## Table of Contents

* Preface
* Introduction
* Induction
* Propositional Logic
* Predicate Logic
* Modal Logic

## Authors

* Uli Furbach
# Logic for Computer Scientists/Modal Logic/Axiomatics
## Axiomatics

The simplest modal logics is called $K$ and is given by the following axioms:
* All classical tautologies (and substitutions thereof)
* Modal Axioms: All formulae of the form $\square (X\to Y)\to (\square X\to \square Y)$
and the inference rules
* Modus Ponens Rule: Conclude $Y$ from $X$ and $X\to Y$
* Necessitation Rule: Conclude $\square X$ from $X$
A $K$ derivation of $X$ from a set $S$ of formulae is a sequence of formulae, ending with $X$, each of it is an axiom of $K$, a member of $S$ or follows from earlier terms by application of an inference rule. A \defined{$K$ proof} of $X$ is a $K$ derivation of $X$ from $\emptyset$.
As an example take the $K$ proof of $(\square P\land \square Q)\to \square (P\land Q)$:
${\begin{matrix}&{\mbox{Tautology: }}&P\to (Q\to (P\land Q))\\&{\mbox{Necessitation: }}&\square (P\to (Q\to (P\land Q)))\\&{\mbox{Modal axiom:}}&\square (P\to (Q\to (P\land Q)))\to (\square P\to \square (Q\to (P\land Q)))\\&{\mbox{Modus ponens: }}&\square P\to \square (Q\to (P\land Q))\\&{\mbox{Modal axiom:}}&\square (Q\to (P\land Q))\to (\square Q\to \square (P\land Q))\\&{\mbox{Classical arg: }}&\square P\to (\square Q\to \square (P\land Q))\\&{\mbox{Classical arg: }}&(\square P\land \square Q)\to \square (P\land Q)\\\end{matrix}}$
There is a similar proof of the converse of this implication; hence it follows that in $K$ we have
$\square (P\land Q)\leftrightarrow (\square P\land \square Q)$
Note that distributivity over disjunction does not hold! (Why?)

## Extensions of K

Starting from the modal logic $K$ one can add additional axioms, yielding different logics. We list the following basic axioms: $K$ : $\square (X\to Y)\to (\square X\to \square Y)$
$T$ : $\square A\to A$
$D$ : $\square A\to \diamond A$
$4$ : $\square A\to \square \square A$
$5$ : $\diamond A\to \square \diamond A$
$B$ : $A\to \square \diamond A$
Traditionally, if one adds axioms $A_{1},\cdots ,A_{n}$ to the logic $K$ one calls the resulting logic $KA_{1}\cdots A_{n}$. Sometimes, however the logic is so well known, that it is referred to under another name; e.g. $KT4$, is called $S4$.
These logics can as well be characterised by certain classes of frames, because it is known that particular axioms correspond to particular restrictions on the reachability relation $R$ of the frame. If $\langle W,R\rangle$ is a frame, then a certain axiom will be valid on $\langle W,R\rangle$, if and only if $R$ meets a certain restriction. Some restrictions are expressible by first-order logical formulae where the binary predicate $R(x,y)$ represents the reachability relation:
${\begin{matrix}T:&{\mbox{Reflexive}}&\forall w{\text{:}}R(w,w)\\D:&{\mbox{Serial}}&\forall w\exists w':R(w,w')\\4:&{\mbox{Transitive}}&\forall s,t,u:(R(s,t)\land R(t,u))\to R(s,u)\\5:&{\mbox{Euclidean}}&\forall s,t,u:(R(s,t)\land R(s,u))\to R(t,u)\\B:&{\mbox{Symmetric}}&\forall w,w':R(w,w')\to R(w',w)\end{matrix}}$
# Logic for Computer Scientists/Modal Logic/Modal Logic
## Modal Logic

Modal logic is concerned with the investigation of modalities in logics. Modalities are e.g. necessity, possibility. In classical propositional logics, propositions like "Tom is Married with Mary" may be $true$ or $false$; in real life however, the truth value of the above propositions obviously can change in time. In a different context the truth value may be different in different worlds: assume that Tom is dreaming about being married with Mary; hence in the world of his dreams the proposition may be true, while in real-life it may be false. In still another context, the truth value can depend whether it is considered under legal aspects: it is possible that Tom and Mary are legally married, while the catholic church is considering them to be single.
Modal logics had been studied extensively already during the first half of the 20th century by various logicians. The main breakthrough, however, was the establishment of a formal semantics of modal logic given be Kripke.
Propositional logics can be extended by modalities to describe belief, knowledge or temporal aspects. Hence it is very appropriate to use them with knowledge representation systems. Recently modal logics have been applied in verification contexts and as a means to describe the semantics of description logics.
As an example take the following puzzle:
   Assume 3 children (works for $n$ children as well), who are perfect reasoners, are always truthful, and always give an answer, if they know one. The children are playing outside, and may get muddy foreheads. Any child can see if the other children have muddy foreheads, but can't see his or her own forehead. At some point, an adult says to them: at least one of you has mud on your forhead. The adult now says: Do any of you know if you have mud on your forehead. No one answers. The adult repeats the question, and again no one answers. The 3rd time the adult asks the question, one or more of the children answer. How many of the children have muddy foreheads?
This puzzle can be solved by constructing a Kripke structure, which is a set of states together with links which express the accessability of states. There are 3 children, each can be muddy or not muddy and, hence, we have $2^{3}$ possible states. States can be represented by triples of booleans values, where a 1 in the nth position means that the nth child is muddy, and a 0 in the nth position means that the nth child is not muddy.
The Kripke structure can be defined as follows: consider, e.g. state (111). Since child 1 does not know whether or not he or she is muddy, as far as child 1 is concerned, he or she could be in state (011). For child 2 the state (011), however, is not accessible, since child 2 knows that child 1 is muddy.
A structure which is constructed as depicted above, can be used to solve the puzzle, by drawing the structure as it results after each speech action of the adult.
# Logic for Computer Scientists/Modal Logic/Modal Logic Tableaux
## Modal Logic Tableaux

In classical propositional logics we introduced a tableau calculus (Definition) for a logic $L$ as a finitely branching tree whose nodes are formulas from $L$. Given a set $\Phi$ of formulae from $L$, a tableau for $\Phi$ was constructed by a (possibly infinite) sequence of applications of a tableau rule schema:
$\rho {\frac {\psi }{D_{1}\mid D_{2}\mid \cdots \mid D_{n}}}$
where the premise $\psi$ as well as the denominators $D_{1},\cdots ,D_{n}$ are sets of formulae; $\rho$ is the name of the rule. We introduce $K$-tableau with the help of the following rules:
$(\land ){\frac {X;P\land Q}{X;P;Q}}$ $(\lor ){\frac {X;\lnot (P\land Q)}{X;\lnot P;\mid X;\lnot Q}}$
$(\bot ){\frac {X;P;\lnot P}{\bot }}$ $(\lnot ){\frac {X;\lnot \lnot P}{X;P}}$
$(\theta ){\frac {X;Y}{X}}$             $(K){\frac {\square X;\lnot \square P}{X;\lnot P}}$
A tableau for a set $X$ of formulae is a finite tree with root $X$ whose nodes carry finite formulae sets. The rules for extending a tableau are given by:
* choose a leaf node $n$ with label $Y$, where $n$ is not an end node, and choose a rule $\rho$, which is applicable to $n$;
* if $\rho$ has $k$ denominators then create $k$ successor nodes for $n$, with successor $i$ carrying an appropriate instance of denominator $D_{i}$;
* if a successor $s$ carries a set $Z$ and $Z$ has already appeared on the branch from the root to $s$ then $s$ is an end node.
A branch is called closed if its end node is carrying ${\bot }$, otherwise it is open.
As in the classical case, a formulae $A$ is a theorem in modal logic $K$, iff there is a closed $K$-tableau for the set $\lnot A$.
As an example take the formula $\square (p\to q)\to (\square p\to \square q)$: its negation is certainly unsatisfiable, because the formula is an instance of our previously given $K$-axiom.
Some remarks are in order:
* The $\theta$- rule, called the thinning rule, is necessary in order to construct the premisses for the application of the $K$-rule.
* Both can be combined by a new rule
$(K\theta ){\frac {Y;\square X;\lnot \square P}{X;\lnot P}}$
* In order to get tableau calculi for the other mentioned calculi, like $T$ or $K4$ one has to introduce additional rules:
$(T){\frac {X;\lnot \square P}{X;\square P;P}}$
which obviously reflects reflexivity of the reachability relation, or
$(K4){\frac {\square X;\lnot \square P}{X;\square X;\lnot P}}$
reflecting transitivity.
# Logic for Computer Scientists/Modal Logic/Kripke Semantics
## Definition 1

A Kripke frame is a pair $\langle W,R\rangle$, where $W$ is a non-empty set (of possible worlds) and $R$ a binary relation on $W$. We write $wRw'$ iff $(w,w')\in R$ and we say that world $w'$ is accessible from world $w$, or that $w'$ is reachable from $w$, or that $w'$ is a successor of $w$.
A Kripke model is a triple $\langle W,R,V\rangle$, with $W$ and $R$ as above and $V$ is a mapping ${\mathcal {P}}\mapsto 2^{W}$, where ${\mathcal {P}}$ is the set of propositional variables. $V(p)$ is intended to be the set of worlds at which $p$ is true under the valuation $V$.
Given a model $\langle W,R,V\rangle$ and a world $w\in W$, we define the satisfaction relation $\models$ by:
${\begin{matrix}w\models p&{\mbox{iff }}&w\in V(p)\\w\models \lnot A&{\mbox{iff }}&w\not \models A\\w\models A\land B&{\mbox{iff }}&w\models A{\mbox{ and }}w\models B\\w\models A\lor B&{\mbox{iff }}&w\models A{\mbox{ or }}w\models B\\w\models A\to B&{\mbox{iff }}&w\not \models A{\mbox{ or }}w\models B\\w\models \Box A&{\mbox{iff }}&{\mbox{for all }}v\in W,(w,v)\not \in R{\mbox{ or }}v\models A\\w\models \diamond A&{\mbox{iff }}&{\mbox{there exists some }}v\in W,{\mbox{ with }}(w,v)\in R{\mbox{ and }}v\models A\\\end{matrix}}$
We say that $w$ satisfies $A$ iff $w\models A$ (without mentioning the valuation $V$). A formula $A$ is called satisfiable in a model $\langle W,R,V\rangle$, iff there exists some $w\in W$, such that $w\models A$. A formula $A$ is called satisfiable in a frame $\langle W,R\rangle$, iff there exists some valuation $V$ and some world $w\in W$, such that $w\models A$. A formula $A$ is called valid in a model $\langle W,R,V\rangle$, written as $\langle W,R,V\rangle \models A$ iff it is true at every world in $W$. A formula $A$ is valid in a frame $\langle W,R\rangle$, written as $\langle W,R\rangle \models A$ iff it is valid in all models $\langle W,R,V\rangle$.

## Lemma 1

The operators $\diamond$ and $\square$ are dual, i.e. for all formulae $A$ and all frames $\langle W,R\rangle$, the equivalence $\diamond A\leftrightarrow \lnot \square \lnot A$ holds.
# Logic for Computer Scientists/Modal Logic/Syntax
## Syntax

We assume the syntax of classical propositional logic as used in the chapters above. Additional we have the following two rules: If $A$ is a formula
${\begin{matrix}&&\diamond A{\mbox{ and}}\\&&\square A\end{matrix}}$
are formulae.
The symbols $\diamond$ and $\Box$ stand traditionally for possibility and necessity; in the context of temporal logic they stand for always and eventually, so that $\diamond A$ stands for $A$ is eventually true and $\Box A$ for $A$ is always true.
# OpenGL Programming/3D/Why OpenGL

OpenGL (Open Graphics Library) is a powerful and widely used graphics rendering API that is used by developers to create high-performance, interactive 3D graphics applications. There are many reasons why OpenGL is a popular choice for 3D programming:
* Cross-platform compatibility: OpenGL is available on a wide range of platforms, including Windows, Linux, macOS, and mobile platforms like iOS and Android. This makes it an attractive choice for developers who want to write applications that can run on multiple platforms.
* High-performance: OpenGL is designed to take advantage of hardware acceleration, which allows it to render graphics much faster than software-based rendering. This makes it ideal for creating complex 3D scenes that require real-time rendering.
* Flexibility: OpenGL is a low-level API that provides developers with a great deal of control over the graphics pipeline. This allows developers to create custom shaders and other advanced techniques to achieve specific visual effects.
* Wide community support: OpenGL has a large and active community of developers, which means that there are many resources available to help developers learn and use the API. This includes documentation, tutorials, and open-source libraries and tools.
Overall, OpenGL is a versatile and powerful tool for creating high-performance, interactive 3D graphics applications. Whether you're building a video game, a scientific visualization tool, or a virtual reality experience, OpenGL provides the flexibility and performance you need to bring your vision to life.
# Content Encoding Error
## Introduction

In some not so very rare occasions, OpenClinica-users can login to OpenClinica, add Subjects and some more activities, but when they try to start entering data, an error-message appears, that looks like:
Content Encoding Error (content_encoding_error)
Server response could not be decoded using encoding type returned by server. This is typically caused by a Web Site presenting a content encoding header of one type, and then encoding the data differently.

## Cause

OpenClinica itself is not setup to use compression, but tomcat and/or Apache/IIS can be setup to do this. (See for an example this page) If you have not enabled compression then the problem could be in the firewall of your users. Especially TMG, Microsoft's Threat Management Gateway, tries to compress content in order to enhance the performance of InternetExplorer-users. However, this does not work in combination with OpenClinica.

## Solution: firewall-side

The best way to solve this is editing the settings of the firewall. Instructions on how to do this if your OpenClinica is behind an ISA firewall, which is the predecessor of TMG, can be found here. But the situation where the user is behind ISA/TMG is not yet described. However not all IT-departments of hospitals are willing to change their security policy.

## Solution: client-side

If the firewall settings can not be changed, you can disable this compression by clicking in InternetExplorer: tools, internet options, advanced and then scrolling down to HTTP and unchecking the box with Use HTTP 1.1 through proxy connections
# OpenGL

Wikibooks about OpenGL:
* OpenGL Programming: using OpenGL in C/C++, for OpenGL 2.x and later
* GLSL Programming: use of the OpenGL Shading Language (GLSL) in Unity 3 and Blender 2.5, with much information on lighting and texturing
# OpenClinica User Manual/uriencoding
## support for special characters
### editing server.xml

If you use OpenClinica out-of-the-box then you may experience some difficulties when it comes to special characters. For example you may have users like Adèle or Amélie and when you search for them they don't show up in the results. This has to do with the fact that tomcat is not handling the characters correctly.
To fix this you must edit the server.xml (and first you must make a copy of your server.xml, like server_20170223.xml).
The places to look for are the connectors and these can be http and / or ajp You change:
<Connector executor="tomcatThreadPool" port="8080" protocol="HTTP/1.1" connectionTimeout="20000" redirectPort="8443" />
into
<Connector executor="tomcatThreadPool" port="8080" protocol="HTTP/1.1" connectionTimeout="20000" redirectPort="8443" URIEncoding="UTF-8" />
and / or:
<Connector port="8009" protocol="AJP/1.3" redirectPort="8443" />
into
<Connector port="8009" protocol="AJP/1.3" redirectPort="8443" URIEncoding="UTF-8" />

### documentation

Documentation can be found at https://wiki.apache.org/tomcat/FAQ/CharacterEncoding
# Macroeconomics/Supply and Demand
## Supply and Demand: A Model of a Competitive Market

This chapter will focus on competitive markets, a market in which there are many buyers and sellers of the same good or service. The key feature of a competitive market is that no one individual's actions have a noticeable effect on the price at which the good or service is sold. When a market is competitive, its behavior is well described by the supply and demand model. Because many markets are comparative, the supply and demand model is very useful one indeed. There are five key elements in this model:
* The demand curve
* The supply curve
* The set of factors that cause the demand curve to shift and the set of factors that cause the supply curve to shift.
* The market equilibrium, which includes the equilibrium price and equilibrium quantity
* The way the market equilibrium changes when the supply curve or demand curve shifts
# Macroeconomics/Why Macroeconomics

Often, we want to measure the well being of a group of economic agents, who engage with each other. Often, such engagements occur within the bounds of a country or nation. Therefore, macroeconomics is usually concerned with economies of countries as a whole. How do we measure well being? Economists have tried to relate wealth to well being. Some correlation between wealth and well being does seem to exist, the relationship is not clear and not definite. However, macroeconomics is concerned with the measurement of wealth because it is easier to measure. It is assumed, that well being will be related to wealth.
Macroeconomics also deals with aggregates, and not individuals. Therefore, in order to understand the economy, we need to define the quantities we will be interested in. To begin with, let us look at the markets that are studied in macroeconomics.
* Market of current goods and services
* Market for future goods and services
* Market for factor inputs
* Market for finance
Each of these markets is an aggregate of smaller markets. For example, under the market of goods and services, there will be smaller markets for various industries.
After defining the markets, we move to measuring the aggregate income. Aggregate income is related (in fact is equal to) the aggregate goods and services produced. This measure is the GDP. More about this in the next chapter which deals specifically about measuring aggregate output.
# Madame Bovary

Welcome to the Wikibook of Gustave Flaubert's novel Madame Bovary. Included in this English translation will be passage by passage close analysis of the text.

### Part One

Chapter One