dataset of sciMindData

description::
"overview of dataset:
A dataset is a structured collection of data that is organized and stored in a way that makes it accessible and useful for various purposes, such as analysis, research, machine learning, and more. Datasets come in various forms and serve different applications. Here is an overview of datasets, including their types and uses:

1. **Structured Datasets**:
- Structured datasets are organized in a tabular format, often resembling a spreadsheet with rows and columns.
- They are commonly used in databases and are suitable for applications like data analysis, business intelligence, and traditional machine learning tasks.
- Examples include customer databases, financial records, and government census data.

2. **Text Datasets**:
- Text datasets consist of unstructured text documents, such as articles, books, emails, and social media posts.
- These datasets are used in natural language processing (NLP) tasks, such as sentiment analysis, text classification, and language modeling.
- Examples include the Wikipedia text corpus and social media comments.

3. **Image Datasets**:
- Image datasets contain collections of images, often labeled or annotated.
- They are essential for computer vision tasks, including image classification, object detection, and image generation.
- Examples include the MNIST dataset for handwritten digit recognition and the ImageNet dataset.

4. **Audio Datasets**:
- Audio datasets consist of audio recordings, such as speech, music, and environmental sounds.
- They are used in applications like speech recognition, music classification, and sound analysis.
- Examples include the Common Voice dataset for speech recognition and the Million Song Dataset for music analysis.

5. **Time Series Datasets**:
- Time series datasets capture data points at regular intervals over time.
- They are essential for forecasting, trend analysis, and anomaly detection.
- Examples include financial market data, weather measurements, and IoT sensor data.

6. **Graph Datasets**:
- Graph datasets represent data as networks of nodes and edges.
- They are used in graph analytics, network analysis, and social network research.
- Examples include social networks like Facebook and academic citation networks.

7. **Geospatial Datasets**:
- Geospatial datasets contain geographic and spatial information.
- They are used in applications such as geographic information systems (GIS), map generation, and location-based services.
- Examples include GPS data, satellite imagery, and census data with geographic coordinates.

8. **Biological Datasets**:
- Biological datasets include genetic, genomic, and proteomic data.
- They are used in bioinformatics for tasks like DNA sequencing, protein structure prediction, and drug discovery.
- Examples include the Human Genome Project dataset and protein structure databases like the Protein Data Bank.

9. **Custom Datasets**:
- Custom datasets are created for specific research or application needs.
- They can include data from various sources and formats, such as sensor data, surveys, and experiments.
- Examples can range from medical research datasets to datasets for testing autonomous vehicles.

Datasets are crucial for training and testing machine learning models, conducting research, and making data-driven decisions. Their quality, size, and relevance play a significant role in the success of data-driven projects. Additionally, data privacy and ethical considerations are essential when working with datasets, especially those containing sensitive information.
[{2023-10-25 retrieved} https://chat.openai.com/c/7e4c64d3-f397-4522-b956-b960cbf731ba]

name::
* McsEngl.dataset,
* McsEngl.sciMindData'dataset,

evolution of sciMindData

description::
"evolution of data science:
The field of data science has evolved significantly over the years, driven by advances in technology, the availability of large and diverse datasets, and the growing demand for data-driven insights in various industries. Here is a brief overview of the evolution of data science:
1. Early Roots (1960s-1980s):
- The early roots of data science can be traced back to statistics, computer science, and operations research.
- Researchers and analysts used traditional statistical methods and manual data processing to extract insights from data.
- The focus was primarily on descriptive statistics and hypothesis testing.
2. Emergence of Data Mining (1990s):
- Data mining emerged as a subfield of data science in the 1990s.
- It involved the use of algorithms and computational techniques to discover patterns, trends, and associations in large datasets.
- Data mining techniques, such as decision trees, clustering, and association rule mining, became popular.
3. Big Data Era (2000s-2010s):
- The proliferation of the internet and digital technologies led to the generation of vast amounts of data, giving rise to the "Big Data" era.
- Technologies like Hadoop and Spark were developed to handle and process large datasets.
- Data storage and processing capabilities expanded, enabling organizations to extract more value from their data.
4. Machine Learning and AI Integration:
- Machine learning (ML) and artificial intelligence (AI) became integral to data science.
- ML algorithms, including deep learning, natural language processing, and reinforcement learning, were applied to solve a wide range of problems.
- The use of neural networks and deep learning models led to breakthroughs in image recognition, speech recognition, and more.
5. Data Science as a Profession (2010s-Present):
- Data science became a recognized profession, and universities started offering formal data science programs.
- Data scientists were in high demand as organizations recognized the potential of data for decision-making and competitive advantage.
- The field saw the emergence of specialized roles, such as data engineers, data analysts, and machine learning engineers.
6. Ethical Considerations and Data Privacy:
- With the increased use of data, ethical concerns and data privacy issues became more prominent.
- Data science practitioners and organizations started to focus on responsible data handling, fairness in AI, and transparency in algorithms.
7. Integration with Business Strategy:
- Data science shifted from a purely technical endeavor to an integral part of business strategy.
- Companies leveraged data science to make informed decisions, improve customer experiences, and gain a competitive edge.
8. Advancements in Tools and Platforms:
- Data science tools and platforms, such as Python, R, Jupyter, and cloud-based services, continued to evolve, making it easier for practitioners to work with data.
- Automated machine learning (AutoML) tools also gained popularity, simplifying the model building process.
9. Interdisciplinary Nature:
- Data science became increasingly interdisciplinary, involving expertise from fields like domain knowledge, psychology, and ethics.
- Collaboration between data scientists and subject matter experts became essential for solving complex problems.
The evolution of data science is ongoing, and it continues to adapt to new technologies, methodologies, and challenges. As the world generates even more data, data science will likely play an even more critical role in shaping our understanding of the world and making data-driven decisions."
[{2023-10-09 retrieved} https://chat.openai.com/c/206229f5-f849-43b7-9436-2e95d6767e3e]

name::
* McsEngl.evoluting-of-sciMindData,
* McsEngl.sciMindData'evoluting,

area-of-study of sciCognitive

description::
* intelligence,
* sensing,
* thinking,
* memoring,
* emoting,
* communicating,
* learning,
* teaching,
* planning,
===
In any way, the essential questions of cognitive science seem to be: What is intelligence? and How is it possible to model it computationally?.
[http://en.wikipedia.org/wiki/Cognitive_science]

The problem of constructing a robot is a good test of progress in cognitive science. A robot needs
- perception,
- memory,
- knowledge,
- learning,
- planning and
- communicative abilities,
that is, exactly those capacities that cognitive science aims at understanding.
[Peter Gardenfors. Cognitive science: from computers to anthills as models of human thought]

Below are some of the main topics that cognitive science is concerned with.
Artificial intelligence
Attention (is the selection of important information)
Language processing
Learning and development
Memory
Perception and action

name::
* McsEngl.sciCognitive'area-of-study,

rlnReferent of sciCognitive

description::
Critique of Cognitive Science
The claim that human minds work by representation and computation is an empirical conjecture and might be wrong. Although the computational-representational approach to cognitive science has been successful in explaining many aspects of human problem solving, learning, and language use, some philosophical critics such as Hubert Dreyfus (1992) and John Searle (1992) have claimed that this approach is fundamentally mistaken. Critics of cognitive science have offered such challenges as:
1. The emotion challenge: Cognitive science neglects the important role of emotions in human thinking.
2. The consciousness challenge: Cognitive science ignores the importance of consciousness in human thinking.
3. The world challenge: Cognitive science disregards the significant role of physical environments in human thinking.
4. The body challenge: Cognitive science neglects the contribution of the body to human thought and action.
5. The social challenge: Human thought is inherently social in ways that cognitive science ignores.
6. The dynamical systems challenge: The mind is a dynamical system, not a computational system.
7. The mathematics challenge: Mathematical results show that human thinking cannot be computational in the standard sense, so the brain must operate differently, perhaps as a quantum computer.

Thagard (2005) argues that all these challenges can best be met by expanding and supplementing the computational-representational approach, not by abandoning it.
[http://plato.stanford.edu/entries/cognitive-science/]

name::
* McsEngl.sciCognitive'rlnReferent,

scientist of sciCognitive

organization of sciCognitive

description::
European Institute of Cognitive Sciences and Engineering
http://www-eurisco.onecert.fr/

University of California, Los Angeles (UCLA)
http://www.lifesci.ucla.edu/cogsci/
Cognitive Science, as a discipline, is concerned with learning how animals (and machines) acquire knowledge, represent that knowledge, and how they manipulate those representations.

University of California, San Diego
http://cogsci.ucsd.edu/

University of Exeter
http://www.exeter.ac.uk/CogSci/

name::
* McsEngl.sciCognitive'organization,

info-resource of sciCognitive

description::
* Eysenck, M.W. ed. (1990). The Blackwell Dictionary of Cognitive Psychology. Cambridge, Massachusetts: Basil Blackwell Ltd.

name::
* McsEngl.sciCognitive'Infrsc,

WHOLE-PART-TREE of sciCognitive

whole-tree-of-sciCognitive::
* ,

part-tree-of-sciCognitive::
* cognitive-psychology,

law-of-excluded-middle of sciLogic

description::
"the Law of Excluded Middle (LEM) states that for any proposition, either the proposition or its negation is true, and there are no other possibilities."

name::
* McsEngl.LEM!=law-of-excluded-middle@sciLogic,
* McsEngl.law-of-excluded-middle@sciLogic,
* McsEngl.sciLogic'law-of-excluded-middle,

relation-to--mathematical-logic of sciLogic

description::
"mathematical logic vs logic
"Logic" is a broad term that encompasses various branches and systems of reasoning, while "mathematical logic" is a specific subfield of logic that applies mathematical techniques and formalism to the study of logical systems. Here's a comparison between the two:
1. Scope and Generality:
- Logic: Logic, in its most general sense, refers to the study of reasoning, argumentation, and inference. It includes informal reasoning, everyday reasoning, and various formal systems.
- Mathematical Logic: Mathematical logic is a specialized branch of logic that focuses on formal, mathematical representations of logical systems. It deals with the development of formal languages, axioms, and rigorous proofs.
2. Formalism and Symbolism:
- Logic: Logic can be studied informally, using natural language, or formally, using symbolic languages. Informal logic is often used in everyday discourse and argumentation.
- Mathematical Logic: Mathematical logic emphasizes the use of symbolic languages and precise mathematical notation. It employs formal systems, including propositional logic, first-order logic, and higher-order logics, to analyze and prove properties of logical systems.
3. Rigor and Proof:
- Logic: While informal logic focuses on practical reasoning and argument evaluation, it may not always provide a rigorous framework for formal proofs.
- Mathematical Logic: Mathematical logic places a strong emphasis on rigorous proof techniques. It uses formal proof systems, such as axiomatic systems and deduction rules, to establish the validity of logical statements.
4. Applications:
- Logic: Logic has broad applications in various fields, including philosophy, linguistics, computer science, and artificial intelligence. It is used for analyzing arguments, constructing valid reasoning, and making decisions.
- Mathematical Logic: Mathematical logic is primarily concerned with the foundations of mathematics, the study of formal languages, and the development of logical systems. It is often used as a tool to analyze and understand the structure of mathematical reasoning.
5. Subfields:
- Logic: Logic encompasses various subfields, including philosophical logic, modal logic, non-classical logic, and many others.
- Mathematical Logic: Mathematical logic is a specific subfield within logic that deals with the mathematical aspects of logical systems. It includes areas like model theory, proof theory, set theory, and computability theory.
In summary, while "logic" is a broad field that covers various forms of reasoning, "mathematical logic" is a specialized branch that applies mathematical rigor and formalism to the study of logical systems, with a particular focus on the foundations of mathematics and the development of formal languages and proofs. Mathematical logic is a subset of logic that uses mathematics as a tool to analyze and understand logic itself."
[{2023-10-08 retrieved} https://chat.openai.com/c/8b6e5141-94b7-476d-ab45-e00fab292d12]

name::
* McsEngl.mathLogic'relation-to-sciLogic,
* McsEngl.sciLogic'relation-to-mathLogic,

evoluting of sciLogic

description::
"The evolution of logic is a complex and fascinating history that spans thousands of years. Logic is the systematic study of valid inference and reasoning, and its development has been influenced by various cultures and thinkers throughout history. Here's a brief overview of the key milestones in the evolution of logic:
1. Ancient Mesopotamia and Egypt (circa 2000-1000 BCE):
- The earliest recorded use of logic can be traced back to the ancient civilizations of Mesopotamia and Egypt. These cultures developed basic forms of reasoning and argumentation, often in the context of legal and administrative texts.
2. Ancient Greece (circa 6th-4th centuries BCE):
- The ancient Greeks made significant contributions to the development of logic. Pre-Socratic philosophers, such as Parmenides and Heraclitus, engaged in philosophical debates that involved logical reasoning.
- However, it was Aristotle (384-322 BCE) who laid the foundation for Western logic. His works, particularly "Organon," introduced formal systems of deduction, including syllogisms, which are still studied in logic today.
3. Hellenistic and Roman Periods (circa 3rd century BCE - 5th century CE):
- The Stoics, a school of Hellenistic philosophy, developed propositional logic and introduced the idea of valid inference based on conditional statements.
- Roman philosophers like Cicero furthered the study of rhetoric and argumentation, contributing to the practical use of logic in persuasion and debate.
4. Medieval Period (5th century - 15th century CE):
- Logic became an essential part of scholastic philosophy during the Middle Ages. Scholars like Boethius and Thomas Aquinas integrated Aristotelian logic into Christian theology.
- The development of modal logic, which deals with necessity and possibility, was a significant achievement during this period.
5. Renaissance and Early Modern Period (15th century - 18th century CE):
- The Renaissance witnessed a resurgence of interest in classical texts, including Aristotle's works on logic.
- In the 17th century, philosophers like René Descartes, Baruch Spinoza, and Gottfried Wilhelm Leibniz contributed to the development of formal logic, mathematical logic, and the philosophy of mind.
6. 19th and 20th Centuries:
- The 19th century saw the emergence of symbolic logic, pioneered by George Boole and Augustus De Morgan. Boole's algebraic approach laid the groundwork for Boolean logic, which is fundamental in computer science.
- In the late 19th and early 20th centuries, logicians like Bertrand Russell and Alfred North Whitehead attempted to create a comprehensive logical foundation for mathematics in their "Principia Mathematica."
- The development of predicate logic and mathematical logic, along with contributions from Kurt Gödel, Alan Turing, and others, led to significant advances in the understanding of formal systems and computability.
7. Contemporary Logic:
- Contemporary logic includes a wide range of specialized branches, including modal logic, non-classical logic (e.g., fuzzy logic and paraconsistent logic), and many-valued logic.
- Computer science and artificial intelligence have also significantly influenced the development and application of logic in areas like automated reasoning and theorem proving.
The evolution of logic continues today with ongoing research in various branches of formal and informal logic. Logic remains a fundamental tool in mathematics, philosophy, computer science, and many other fields, playing a crucial role in shaping our understanding of reasoning and inference."
[{2023-10-08 retrieved} https://chat.openai.com/c/eeb84e42-1830-4bae-a865-29bf7bcd70c8]

name::
* McsEngl.evoluting-of-sciLogic,
* McsEngl.sciLogic'evoluting,

GENERIC-SPECIFIC-TREE of sciLogic

generic-tree-of-sciLogic::
* sciMind,
* ... entity,
* McsEngl.sciLogic'generic-tree,

specific-tree-of-sciLogic::
* formal-logic,
* fuzzy-logic,
* infinite-valued-logic,
* informal-logic,
* many-valued-logic,

* McsEngl.sciLogic.specific-tree,

sciLogic.formal

description::
"overview of formal-logic:
Formal logic is the study of the rules of reasoning. It is a branch of philosophy that deals with the principles of valid inference. Formal logic is abstract in that it does not deal with the content of statements, but only with their logical form. This means that formal logic can be used to evaluate the validity of arguments regardless of their subject matter.

Formal logic is based on the idea that there are certain rules that govern how we can move from one statement to another. These rules are designed to ensure that our conclusions follow logically from our premises.

There are two main types of formal logic: deductive logic and inductive logic. Deductive logic is concerned with arguments that are logically valid, meaning that if the premises are true, then the conclusion must also be true. Inductive logic is concerned with arguments that are probable, meaning that if the premises are true, then the conclusion is likely to be true, but not necessarily.

Some of the key concepts in formal logic include:
* Propositions: Statements that are either true or false.
* Arguments: Groups of propositions that are used to support or refute a claim.
* Validity: The property of an argument that guarantees that the conclusion is true if the premises are true.
* Soundness: The property of an argument that is both valid and has true premises.
* Fallacies: Errors in reasoning that can lead to false conclusions.

Formal logic is a powerful tool that can be used to improve our thinking and to identify errors in reasoning. It is a valuable skill for anyone who wants to be able to think critically and to make rational decisions.

Here are some examples of how formal logic can be used:
* To evaluate the validity of arguments: We can use formal logic to determine whether or not an argument is valid, regardless of the content of the argument. This can be useful for evaluating the arguments of politicians, advertisers, and other people who are trying to persuade us to believe something.
* To identify fallacies: Formal logic can help us to identify fallacies, which are errors in reasoning that can lead to false conclusions. This can be useful for avoiding making mistakes in our own thinking and for identifying the mistakes of others.
* To develop new theories: Formal logic can be used to develop new theories in a variety of fields, including philosophy, mathematics, and computer science. For example, formal logic is used to develop computer programs and to verify their correctness.

Formal logic is a complex and challenging subject, but it is also a very rewarding one. It can help us to think more clearly and to reason more effectively."
[{2023-10-12 retrieved} https://bard.google.com/chat/4291ca3c09aa3d45]

name::
* McsEngl.formal-logic!⇒logicFormal,
* McsEngl.logicFormal,
* McsEngl.sciLogic.formal!⇒logicFormal,
* McsEngl.sciLogicFormal!⇒logicFormal,
* McsEngl.sciLogicFormal!=formal-logic,
* McsEngl.sciMind.formal-logic!⇒logicFormal,

sciLogic.informal

description::
"overview of informal-logic:
Informal logic, also known as non-formal logic or everyday logic, is the branch of logic that deals with arguments and reasoning outside of the strict formalism found in symbolic or mathematical logic. It focuses on the way people naturally engage in reasoning and argumentation in everyday life, rather than adhering to rigid rules and symbols.

Here is an overview of informal logic:
1. Argument Analysis: Informal logic is primarily concerned with analyzing arguments. An argument consists of a set of statements where one statement (the conclusion) is claimed to be supported by others (the premises). Informal logicians examine the structure and content of arguments to assess their validity and soundness.

2. Fallacies: One of the central aspects of informal logic is the study of fallacies. Fallacies are common errors in reasoning that can make arguments appear persuasive but are, in fact, logically flawed. Examples of fallacies include ad hominem attacks, slippery slope arguments, and appeal to emotions.

3. Critical Thinking: Informal logic promotes critical thinking skills, encouraging individuals to evaluate arguments and evidence critically. It involves recognizing biases, assumptions, and hidden premises in arguments and questioning the validity and relevance of these elements.

4. Rhetoric: Informal logic is closely related to the study of rhetoric, which focuses on persuasive communication. Rhetorical devices and strategies are often employed in everyday arguments and discussions, and informal logicians examine how these techniques can influence reasoning.

5. Language and Ambiguity: Informal logic acknowledges the importance of language and the potential for ambiguity and vagueness in communication. It deals with issues related to semantics, definitions, and linguistic clarity, as misunderstandings in language can lead to flawed reasoning.

6. Real-World Application: Informal logic is highly practical and applicable to everyday life. It is used in various contexts, including legal arguments, political debates, advertising, and critical analysis of news and media. Informal logic helps people become more discerning consumers of information and better decision-makers.

7. Informal Fallacies: Informal logic classifies fallacies into categories, such as fallacies of relevance (e.g., red herring, ad populum), fallacies of presumption (e.g., begging the question, false dilemma), and fallacies of ambiguity (e.g., equivocation, amphiboly).

8. Informal vs. Formal Logic: Informal logic differs from formal logic, which uses mathematical symbols and strict rules of inference. While formal logic is more abstract and precise, informal logic is concerned with the less structured and more natural way people engage in argumentation.

Overall, informal logic is a valuable tool for enhancing critical thinking skills, identifying errors in everyday reasoning, and engaging in more productive discussions and debates. It equips individuals with the ability to think critically, recognize fallacies, and communicate effectively in various personal and professional contexts."
[{2023-10-12 retrieved} https://chat.openai.com/c/7cdcdac2-b79e-464e-9356-12201ec61fa4]

name::
* McsEngl.informal-logic!⇒sciLogicFormalNo,
* McsEngl.sciLogic.informal!⇒sciLogicFormalNo,
* McsEngl.sciLogicFormalNo,
* McsEngl.sciLogicFormalNo!=informal-logic,

sciLogic.classical

description::
"Classical logic (or standard logic[1][2] or Frege-Russell logic[3]) is the intensively studied and most widely used class of deductive logic.[4] Classical logic has had much influence on analytic philosophy.

Characteristics
* Each logical system in this class shares characteristic properties:[5]
* Law of excluded middle and double negation elimination
* Law of noncontradiction, and the principle of explosion
* Monotonicity of entailment and idempotency of entailment
* Commutativity of conjunction
* De Morgan duality: every logical operator is dual to another

While not entailed by the preceding conditions, contemporary discussions of classical logic normally only include propositional and first-order logics.[4][6] In other words, the overwhelming majority of time spent studying classical logic has been spent studying specifically propositional and first-order logic, as opposed to the other forms of classical logic.

Most semantics of classical logic are bivalent, meaning all of the possible denotations of propositions can be categorized as either true or false."
[{2023-10-12 retrieved} https://en.wikipedia.org/wiki/Classical_logic]

name::
* McsEngl.classical-logic,
* McsEngl.logicClassical,
* McsEngl.sciLogic.classical,

sciLogic.non-classical

description::
"overview of non-classical-logic:
Non-classical logics are formal systems that differ in a significant way from standard logical systems such as propositional and predicate logic. There are several ways in which this is done, including by way of extensions, deviations, and variations. The aim of these departures is to make it possible to construct different models of logical consequence and logical truth.

One of the most important distinctions between classical and non-classical logics is their treatment of truth. Classical logic is based on the principle of bivalence, which states that every proposition is either true or false. Non-classical logics, on the other hand, may reject bivalence in favor of a many-valued logic, in which propositions can take on a range of truth values, or they may develop alternative notions of truth altogether.

Another important distinction is their treatment of contradiction. Classical logic adheres to the law of non-contradiction, which states that no proposition can be both true and false at the same time. Non-classical logics, on the other hand, may reject the law of non-contradiction, or they may develop alternative ways of dealing with contradiction.

There are many different kinds of non-classical logic, each with its own unique features. Some examples include:
* Modal logic: Modal logic is a family of logics that are used to reason about possibility and necessity. It is often used in philosophy and artificial intelligence.
* Intuitionistic logic: Intuitionistic logic is a logic that is based on the idea of constructive proof. It is often used in mathematics and computer science.
* Paraconsistent logic: Paraconsistent logics are logics that can tolerate contradiction. They are often used to reason about inconsistent knowledge bases.
* Many-valued logic: Many-valued logics are logics that allow for more than two truth values. They are often used to reason about uncertainty and ambiguity.
* Fuzzy logic: Fuzzy logic is a logic that is based on the idea of degrees of truth. It is often used in artificial intelligence and control systems.

Non-classical logics have a wide range of applications in philosophy, mathematics, computer science, and other fields. For example, modal logic is used to reason about the meaning of language, intuitionistic logic is used to prove mathematical theorems, paraconsistent logic is used to reason about inconsistent legal codes, many-valued logic is used to reason about uncertainty in medical diagnosis, and fuzzy logic is used to control robots.

Here are some examples of how non-classical logics can be used:
* Modal logic can be used to reason about the meaning of the sentence "It is possible that I will win the lottery." Classical logic cannot reason about possibility and necessity, so it cannot distinguish between the sentence "It is possible that I will win the lottery" and the sentence "I will win the lottery." Modal logic, on the other hand, can make this distinction.
* Intuitionistic logic can be used to prove the mathematical theorem known as the "fan theorem." The fan theorem is a theorem about the geometry of triangles. It is difficult to prove the fan theorem using classical logic, but it can be easily proven using intuitionistic logic.
* Paraconsistent logic can be used to reason about inconsistent legal codes. Legal codes often contain contradictions, but they still need to be interpreted and applied. Paraconsistent logic can be used to reason about inconsistent legal codes in a way that is consistent with the law.
* Many-valued logic can be used to reason about uncertainty in medical diagnosis. Medical diagnosis often involves reasoning about incomplete or conflicting information. Many-valued logic can be used to reason about uncertainty in medical diagnosis in a way that is more realistic than classical logic.
* Fuzzy logic can be used to control robots. Fuzzy logic can be used to control robots in a way that is more robust to noise and uncertainty than classical logic.

Non-classical logic is a rapidly developing field of research with a wide range of applications. It is an essential tool for anyone who wants to understand the nature of reasoning and the foundations of knowledge."
[{2023-10-12 retrieved} https://bard.google.com/chat/2dbfea1c0f6ded89]

name::
* McsEngl.logicClassicalNo,
* McsEngl.non-classical-logic,
* McsEngl.sciLogic.non-classical,

sciLogic.finite-value

description::
·

name::
* McsEngl.finite-value-logic,
* McsEngl.sciLogic.finite-value,

sciLogic.infinite-value

description::
"In logic, an infinite-valued logic (or real-valued logic or infinitely-many-valued logic) is a many-valued logic in which truth values comprise a continuous range. Traditionally, in Aristotle's logic, logic other than bivalent logic was abnormal, as the law of the excluded middle precluded more than two possible values (i.e., "true" and "false") for any proposition.[1] Modern three-valued logic (ternary logic) allows for an additional possible truth value (i.e., "undecided")[2] and is an example of finite-valued logic in which truth values are discrete, rather than continuous. Infinite-valued logic comprises continuous fuzzy logic, though fuzzy logic in some of its forms can further encompass finite-valued logic. For example, finite-valued logic can be applied in Boolean-valued modeling,[3][4] description logics,[5] and defuzzification[6][7] of fuzzy logic."
[{2023-10-13 retrieved} https://en.wikipedia.org/wiki/Infinite-valued_logic]

name::
* McsEngl.infinite-value-logic,
* McsEngl.sciLogic.infinite-value,

descriptionLong::
"overview of infinite-value-logic:
Infinite-Value Logic, also known as Multi-Valued Logic or Many-Valued Logic, is a type of mathematical and philosophical framework for dealing with logical reasoning that extends beyond the traditional binary (true/false) or ternary (true/false/unknown) logic systems. In Infinite-Value Logic, there are more than two truth values, often an infinite number of them, allowing for a more nuanced and flexible representation of information and reasoning. Here's an overview of some key aspects of Infinite-Value Logic:

1. Many Truth Values: Infinite-Value Logic goes beyond the two classical truth values of "true" and "false." It includes various intermediate truth values, which can represent degrees of truth, uncertainty, or relevance.

2. Fuzzy Logic: One of the most well-known systems of Infinite-Value Logic is fuzzy logic, developed by Lotfi Zadeh in the 1960s. Fuzzy logic allows for degrees of truth between 0 (completely false) and 1 (completely true) and is particularly useful for dealing with vague or imprecise information.

3. Applications: Infinite-Value Logic is applied in various fields, including artificial intelligence, control systems, decision-making, and linguistics. Fuzzy logic, in particular, has found applications in fields like fuzzy control systems and fuzzy expert systems.

4. Representation of Uncertainty: Infinite-Value Logic is useful for modeling and managing uncertainty in information. It can handle situations where a statement can be partially true, partially false, or somewhere in between, making it a valuable tool for real-world problem-solving.

5. Algebra and Operators: Infinite-Value Logic often employs different algebraic structures and operators compared to classical logic. It uses functions and operators to combine and manipulate truth values in a more granular way. For example, conjunction and disjunction operations can be generalizations of classical "AND" and "OR" operators.

6. Mathematical Framework: Infinite-Value Logic is a mathematical framework with formal rules and principles that allow for precise reasoning and inference. This framework can include propositional and predicate calculus adapted to handle multiple truth values.

7. Fuzzy Sets: In fuzzy logic, the concept of fuzzy sets plays a central role. A fuzzy set is a generalization of a classical set, where elements have degrees of membership, allowing for a more nuanced representation of relationships.

8. Rule-Based Systems: Many applications of Infinite-Value Logic involve rule-based systems, where rules express conditions and conclusions in a fuzzy or multi-valued logic format. These systems are particularly useful for decision support and expert systems.

9. Handling Complex Systems: Infinite-Value Logic is well-suited for dealing with complex systems where simple binary true/false decisions may not suffice. It provides a framework for managing and processing information in a more natural and adaptable manner.

Infinite-Value Logic, particularly in the form of fuzzy logic, has proven to be a valuable tool for handling uncertainty and imprecision in various real-world applications. It allows for a more nuanced and flexible approach to reasoning and decision-making, making it a key component in the field of artificial intelligence, control systems, and beyond."
[{2023-10-13 retrieved} https://chat.openai.com/c/dd611348-f01b-45b0-81eb-fdf401aa4e3d]

sciLogic.Aristotelian

description::
"overview of Aristotelian-logic:
Aristotelian logic is a system of logic developed by the ancient Greek philosopher Aristotle. It is the earliest known formal system of logic, and it has had a profound influence on Western thought. Aristotelian logic is based on the syllogism, which is a three-part argument that consists of two premises and a conclusion. The premises are statements that are assumed to be true, and the conclusion is a statement that follows logically from the premises.

There are many different types of syllogisms, but they all share the same basic structure. The first premise states a relationship between two classes of things, such as "All men are mortal." The second premise states that a particular thing belongs to one of the classes, such as "Socrates is a man." The conclusion then states that the particular thing must also belong to the other class, such as "Therefore, Socrates is mortal."

Aristotelian logic also includes a number of other concepts, such as the law of non-contradiction, the law of excluded middle, and the square of opposition. The law of non-contradiction states that it is impossible for something to be both true and false at the same time. The law of excluded middle states that everything must either be true or false, there is no third option. The square of opposition is a diagram that shows the relationships between different types of propositions.

Aristotelian logic has been used for centuries to study and debate a wide range of topics, including philosophy, mathematics, and law. It is still taught in many schools and universities today, and it remains an important tool for critical thinking and reasoning.

Here is an example of a simple syllogism:
* Premise 1: All men are mortal.
* Premise 2: Socrates is a man.
* Conclusion: Therefore, Socrates is mortal.

This syllogism is valid, meaning that the conclusion follows logically from the premises. If the premises are true, then the conclusion must also be true.

Aristotelian logic is a powerful tool for reasoning and critical thinking. It can help us to identify logical fallacies, evaluate the strength of arguments, and form our own well-reasoned opinions.

Here are some of the benefits of studying Aristotelian logic:
* It can help you to think more clearly and rationally.
* It can help you to identify logical fallacies in your own thinking and in the thinking of others.
* It can help you to evaluate the strength of arguments and to form your own well-reasoned opinions.
* It can help you to communicate your ideas more effectively.

Aristotelian logic is a valuable skill for anyone who wants to be able to think critically and reason effectively."
[{2023-10-12 retrieved} https://bard.google.com/chat/67839da50bcddd4f]

name::
* McsEngl.Aristotelian-logic,
* McsEngl.logicAristotelian,
* McsEngl.sciLogic.Aristotelian,
* McsEngl.term-logic,
* McsEngl.traditional-logic,

descriptionLong::
"overview of term-logic:
Term logic, also known as traditional logic or Aristotelian logic, is a formal system of logic that was developed by the ancient Greek philosopher Aristotle. It is one of the foundational systems of classical logic and is distinct from modern predicate logic, which is more widely used in contemporary mathematics and philosophy.

Here's an overview of term logic:
1. Propositions: Term logic primarily deals with propositions, which are statements that can be either true or false. These propositions are formed by combining terms, which are words or phrases that represent objects, individuals, or concepts.

2. Categorical Syllogisms: Term logic is particularly concerned with categorical syllogisms. A categorical syllogism is a deductive argument consisting of three propositions: two premises and a conclusion. These propositions are categorized into one of four standard forms, each of which has specific rules for validity. The four forms are based on the quantity and quality of terms (universal or particular, affirmative or negative).

3. Four Categorical Propositions: In term logic, propositions are categorized into four basic forms, represented by letters:
- A propositions: Universal affirmative statements (e.g., "All humans are mortal").
- E propositions: Universal negative statements (e.g., "No birds are mammals").
- I propositions: Particular affirmative statements (e.g., "Some dogs are friendly").
- O propositions: Particular negative statements (e.g., "Some cars are not electric").

4. Square of Opposition: The square of opposition is a diagram that illustrates the relationships between the four basic categorical propositions. It shows how the truth values of one proposition relate to the truth values of other propositions (contradiction, contrariety, subcontrariety, and subalternation).

5. Syllogistic Rules: Term logic includes a set of rules and principles for determining the validity of categorical syllogisms. These rules include the laws of conversion, obversion, and contraposition, which are used to transform and evaluate syllogisms.

6. Limited Expressive Power: One of the limitations of term logic is its relatively limited expressive power compared to modern predicate logic. It is primarily designed to analyze categorical relationships, and it doesn't provide the flexibility to represent complex relationships or quantifiers used in modern mathematical and logical reasoning.

7. Historical Significance: Term logic was the dominant form of logic for centuries, with Aristotle's works serving as a foundation for Western philosophical and logical thought. It was later supplanted by predicate logic, developed by logicians like George Boole and Augustus De Morgan, which is more versatile and capable of representing more complex relationships.

Despite its historical importance, term logic is mostly of interest to historians of philosophy and classical logic. Contemporary formal logic relies more on predicate logic and its mathematical foundations for reasoning and problem-solving."
[{2023-10-14 retrieved} https://chat.openai.com/c/5b4e936e-785b-4f3a-9e6d-3d0784b7306b]

sciLogic.deductive

description::
"overview of deductive-logic:
Deductive logic is a system of reasoning that allows us to draw conclusions from premises that are assumed to be true. It is a top-down approach to reasoning, where we start with general statements and work toward specific conclusions.

Deductive arguments are typically represented in the following form:
* Premise 1: All humans are mortal.
* Premise 2: Socrates is a human.
* Conclusion: Therefore, Socrates is mortal.

In this example, the premises are two general statements that are assumed to be true. The conclusion is a more specific statement that is derived from the premises.

If all of the premises in a deductive argument are true, and the argument is valid (i.e., the conclusion follows logically from the premises), then the conclusion must also be true. This is known as the principle of validity.

Deductive logic is used in many different fields, including mathematics, philosophy, law, and science. It is a powerful tool for thinking critically and making sound decisions.

Here are some examples of deductive logic in everyday life:
* If it is raining, then the ground is wet. The ground is wet. Therefore, it is raining.
* If I study hard, then I will pass the exam. I studied hard. Therefore, I will pass the exam.
* All dogs are mammals. My pet is a dog. Therefore, my pet is a mammal.

Deductive logic can also be used to identify fallacies in arguments. A fallacy is an error in reasoning that can lead to false conclusions. Some common fallacies include:
* Ad hominem: Attacking the person making the argument instead of the argument itself.
* Straw man: Misrepresenting the other person's argument in order to make it easier to attack.
* Begging the question: Assuming the conclusion of the argument in the premises.

By understanding deductive logic, we can better evaluate the arguments that we encounter in everyday life and avoid being misled by fallacies.

Here are some of the types of deductive logic:
* Syllogism: A syllogism is a deductive argument that consists of two premises and a conclusion. The most common form of syllogism is the categorical syllogism, which is made up of three categorical propositions (e.g., all A are B, all C are A, therefore, all C are B).
* Modus ponens: Modus ponens is a rule of inference that allows us to derive a conclusion from two premises. The premises must be in the following form: if P, then Q; and P is true. The conclusion is then Q.
* Modus tollens: Modus tollens is another rule of inference that allows us to derive a conclusion from two premises. The premises must be in the following form: if P, then Q; and Q is false. The conclusion is then not P.

Deductive logic is a powerful tool for thinking critically and making sound decisions. By understanding the different types of deductive logic, we can better evaluate the arguments that we encounter in everyday life."
[{2023-10-13 retrieved} https://bard.google.com/chat/95347a88b1428e6f]

name::
* McsEngl.deductive-logic,
* McsEngl.logicDeductive,
* McsEngl.sciLogic.deductive,

sciLogic.dialectical

description::
"dialectical logic overview:
Dialectical logic is a system of thought that emphasizes the interconnectedness and dynamic nature of reality. It was developed by the German philosopher Georg Wilhelm Friedrich Hegel (1770-1831) and later adapted by Karl Marx and other thinkers.

Dialectical logic is based on the following key principles:
* Change is constant. Reality is not static, but is constantly changing and evolving.
* Everything is interconnected. Nothing exists in isolation, but rather is interconnected with everything else.
* Opposites are interdependent. Opposites are not mutually exclusive, but rather are complementary and necessary for each other's existence.
* Negation of the negation. Development occurs through a process of negation, in which an existing thesis is negated by its opposite (antithesis), and this negation is then negated itself, resulting in a new synthesis that incorporates elements of both the thesis and antithesis.

Dialectical logic has been used to analyze a wide range of phenomena, including history, society, nature, and human thought. It has been particularly influential in the fields of Marxism, critical theory, and feminist theory.

Here is an example of how dialectical logic can be used to analyze a real-world phenomenon:
Thesis: Capitalism is the best economic system.
Antithesis: Capitalism is exploitative and unsustainable.
Synthesis: A new economic system that incorporates the positive aspects of capitalism (e.g., efficiency and innovation) while eliminating its negative aspects (e.g., inequality and environmental destruction).

This is just a simple example, and there are many different ways to apply dialectical logic to different situations. However, it gives a basic idea of how dialectical logic can be used to understand the complex and ever-changing world around us.

Dialectical logic vs. formal logic
Dialectical logic is often contrasted with formal logic, which is a system of thought that emphasizes the logical consistency of arguments. Formal logic is based on a set of fixed rules and principles, and it is used to evaluate the validity of arguments.
Dialectical logic, on the other hand, is more concerned with the process of developing and refining ideas. It is not bound by the same rigid rules as formal logic, and it is often used to explore new and challenging ideas.
Dialectical logic is not intended to replace formal logic, but rather to supplement it. Formal logic is essential for ensuring the logical consistency of arguments, but dialectical logic is necessary for developing and refining ideas.

Conclusion
Dialectical logic is a powerful tool for understanding the complex and ever-changing world around us. It is based on the principles that change is constant, everything is interconnected, opposites are interdependent, and development occurs through a process of negation of the negation.
Dialectical logic has been used to analyze a wide range of phenomena, including history, society, nature, and human thought. It has been particularly influential in the fields of Marxism, critical theory, and feminist theory.
Dialectical logic is often contrasted with formal logic, but it is not intended to replace it. Rather, dialectical logic is meant to supplement formal logic by providing a framework for developing and refining ideas."
[{2023-10-11 retrieved} https://bard.google.com/chat/5afbf9325369fbaf]
[{2023-10-11 retrieved} https://bard.google.com/chat/5afbf9325369fbaf]

name::
* McsEngl.dialectical-logic,
* McsEngl.sciLogic.dialectical,

descriptionLong::
"overview of dialectical logic:
Dialectical logic, also known as dialectical reasoning or dialectics, is a method of argumentation and philosophical reasoning that has its roots in ancient Greek philosophy, particularly in the works of Plato and Aristotle. It's a form of discourse that seeks to explore and understand the complexities and contradictions within a given subject or issue. Dialectical logic is often associated with the philosophical tradition of Hegelian dialectics, which was developed by Georg Wilhelm Friedrich Hegel in the 19th century. Here is an overview of dialectical logic:

1. Thesis, Antithesis, and Synthesis: The core of dialectical logic is the triadic structure of thesis, antithesis, and synthesis. The process begins with a thesis, which represents a particular point of view or idea. This thesis then encounters its opposite, the antithesis, resulting in a clash of ideas and contradictions. Through this conflict, a synthesis emerges, which is a higher-level concept that reconciles the opposing viewpoints, preserving elements of truth from both the thesis and antithesis.

2. Change and Development: Dialectical logic emphasizes change and development. It sees reality as dynamic and in a constant state of flux, with contradictions and conflicts being essential drivers of progress and growth. The synthesis of opposing ideas leads to a new thesis, creating a continuous cycle of development.

3. Negation of the Negation: Another key concept in dialectical logic is the "negation of the negation." This idea suggests that the resolution of a contradiction not only brings about a synthesis but also negates the initial contradiction itself. In other words, it implies a process of overcoming and transcending contradictions.

4. Historical and Societal Application: Hegel's dialectical method was often applied to historical and societal development. According to his philosophy, human history unfolds through a dialectical process where each stage of society and culture gives rise to its own contradictions and eventually leads to a new stage.

5. Critique and Analysis: Dialectical logic encourages critical thinking and analysis. It seeks to uncover underlying contradictions and tensions within concepts, ideas, or social structures, thereby offering a more profound understanding of complex issues.

6. Applicability: Dialectical logic has found applications in various fields, including philosophy, social sciences, and political theory. It has been used to analyze societal and historical changes, understand the evolution of ideas and philosophies, and explore contradictions in ethical and moral frameworks.

7. Criticism and Variations: Dialectical logic is not without criticism. Some argue that it can be overly abstract or prone to subjective interpretation. Additionally, various philosophers and thinkers have developed their own variations and interpretations of dialectical reasoning, adapting it to their specific philosophical or analytical frameworks.

In summary, dialectical logic is a method of philosophical reasoning that emphasizes the dynamic and evolving nature of reality, the resolution of contradictions, and the development of new ideas and concepts through a triadic process of thesis-antithesis-synthesis. It has had a significant impact on philosophy, social sciences, and various other fields of thought."
[{2023-10-11 retrieved} https://chat.openai.com/c/ccb50342-59f2-45b6-8dd1-34bd3c36c251]

sciLogic.fuzzy

description::
"Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.[1] By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.
The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Iranian Azerbaijani mathematician Lotfi Zadeh.[2][3] Fuzzy logic had, however, been studied since the 1920s, as infinite-valued logic—notably by Łukasiewicz and Tarski.[4]
Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or fuzzy sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and using data and information that are vague and lack certainty.[5][6]
Fuzzy logic has been applied to many fields, from control theory to artificial intelligence."
[{2023-08-31 retrieved} https://en.wikipedia.org/wiki/Fuzzy_logic]

name::
* McsEngl.fuzzy-logic,
* McsEngl.sciLogic.fuzzy,
* McsEngl.sciLogicFuzzy,

sciLogic.mathematical (link)

sciLogic.modal

description::
"Modal logic is a kind of logic used to represent statements about necessity and possibility. It plays a major role in philosophy and related fields as a tool for understanding concepts such as knowledge, obligation, and causation. For instance, in epistemic modal logic, the formula ◻P can be used to represent the statement that P is known. In deontic modal logic, that same formula can represent that P is a moral obligation. Modal logic considers the inferences that modal statements give rise to. For instance, most epistemic logics treat the formula ◻P → P as a tautology, representing the principle that only true statements can count as knowledge.
Modal logics are formal systems that include unary operators such as ◊ and ◻, representing possibility and necessity respectively. For instance the modal formula ◊P can be read as "possibly P" while ◻P can be read as "necessarily P". In the standard relational semantics for modal logic, formulas are assigned truth values relative to a possible world. A formula's truth value at one possible world can depend on the truth values of other formulas at other accessible possible worlds. In particular, ◊P is true at a world if P is true at some accessible possible world, while ◻P is true at a world if P is true at every accessible possible world. A variety of proof systems exist which are sound and complete with respect to the semantics one gets by restricting the accessibility relation. For instance, the deontic modal logic D is sound and complete if one requires the accessibility relation to be serial.
While the intuition behind modal logic dates back to antiquity, the first modal axiomatic systems were developed by C. I. Lewis in 1912. The now-standard relational semantics emerged in the mid twentieth century from work by Arthur Prior, Jaakko Hintikka, and Saul Kripke. Recent developments include alternative topological semantics such as neighborhood semantics as well as applications of the relational semantics beyond its original philosophical motivation.[1] Such applications include game theory,[2] moral and legal theory,[2] web design,[2] multiverse-based set theory,[3] and social epistemology.[4]"
[{2023-09-20 retrieved} https://en.wikipedia.org/wiki/Modal_logic]

name::
* McsEngl.modal-logic,
* McsEngl.sciLogic.modal,
* McsEngl.sciLogicModal,

sciLogic.philosophy

description::
"overview of philosophy-of-logic:
The philosophy of logic is a branch of philosophy that examines the nature, scope, and principles of logic. Logic is the study of valid reasoning and inference, and it plays a foundational role in various fields, including mathematics, science, linguistics, and philosophy itself. The philosophy of logic seeks to understand the underlying concepts and principles of logic and to address fundamental questions about the nature of reality, knowledge, language, and reasoning. Here is an overview of some key topics within the philosophy of logic:

1. **Nature of Logic:** Philosophers of logic examine what logic is, its foundations, and its relationship to other disciplines. They debate whether logic is a product of human thought or a fundamental aspect of reality.

2. **Truth and Validity:** Central to logic is the distinction between truth and validity. Philosophers explore the relationship between true statements and valid arguments, as well as the nature of truth and falsity itself.

3. **Logical Systems:** Different logical systems, such as classical logic, modal logic, and non-classical logics, are studied. Philosophers analyze the strengths and limitations of these systems and investigate their applicability in different contexts.

4. **Logical Paradoxes:** The philosophy of logic delves into paradoxes like the liar paradox, Russell's paradox, and Gödel's incompleteness theorems. These paradoxes challenge our understanding of truth, reference, and the limits of formal systems.

5. **Formal and Informal Logic:** Formal logic uses symbolic language to analyze arguments, while informal logic focuses on everyday reasoning. Philosophers discuss the relationship between these two approaches and their respective advantages and limitations.

6. **Epistemology of Logic:** This subfield explores questions related to the nature of knowledge and belief in logic. What justifies our confidence in the principles of logic, and how do we come to know logical truths?

7. **Semantics and Pragmatics:** Philosophers examine the relationship between the meaning of logical expressions and their use in natural language. This includes debates about the connection between formal logic and natural language.

8. **Philosophy of Mathematics:** Logic is closely connected to mathematics. Philosophers explore the philosophical foundations of mathematics, including debates about the nature of mathematical objects, the existence of mathematical truths, and the relationship between logic and mathematics.

9. **Philosophy of Language:** Language and logic are intertwined. This area of study investigates the role of language in logical reasoning and the relationship between language, thought, and reality.

10. **Meta-logic:** Meta-logic deals with the study of logic itself. It explores questions about the structure and properties of logical systems, the nature of logical consequence, and the criteria for evaluating logical theories.

11. **History of Logic:** The history of logic is an important aspect of the philosophy of logic. It examines the development of logical thought from ancient times to the present, including the contributions of notable philosophers like Aristotle, Leibniz, Frege, and Tarski.

In summary, the philosophy of logic is a multifaceted field that addresses fundamental questions about the nature of reasoning, truth, and the relationship between language and reality. It is an essential discipline for anyone interested in understanding the foundations of rational thought and the role of logic in various academic and practical domains."
[{2023-10-14 retrieved} https://chat.openai.com/c/06410892-7f60-418a-b9d6-554cdf52c86f]

name::
* McsEngl.logicPhilosophy,
* McsEngl.philosophy-of-logic,
* McsEngl.sciLogic.philosophy,

sciLogic.quantum

description::
"overview of quantum logic:
Quantum logic is a mathematical formalism that is used to describe the propositions about quantum systems. It is a non-classical logic that is based on the structure of projection operators in a Hilbert space.

Classical logic is based on the Boolean algebra, which has two truth values: true and false. Quantum logic, on the other hand, is based on the orthomodular lattice, which has a richer structure. An orthomodular lattice is a partially ordered set with two operations: conjunction and negation. However, unlike the Boolean algebra, the conjunction operation in an orthomodular lattice is not distributive over the negation operation.

This difference in structure is necessary to account for the non-classical features of quantum mechanics, such as superposition and entanglement. For example, in classical mechanics, it is always possible to simultaneously determine the position and momentum of a particle. However, in quantum mechanics, this is not possible due to the uncertainty principle.

Quantum logic can be used to reason about the relationships between different quantum observables. For example, it can be used to show that certain pairs of observables, such as position and momentum, are incompatible. This means that it is impossible to simultaneously determine the values of both observables with certainty.

Quantum logic is also used in the design and implementation of quantum computers. Quantum computers rely on the ability to manipulate quantum superpositions of states. Quantum logic can be used to verify that quantum algorithms are correct and to design efficient implementations of these algorithms.

Here is an overview of some of the key concepts in quantum logic:
* Orthomodular lattice: A partially ordered set with two operations: conjunction and negation. The conjunction operation is not distributive over the negation operation.
* Projection operator: A linear operator on a Hilbert space that projects the state of the system onto a subspace of the Hilbert space.
* Quantum observable: A physical quantity that can be measured on a quantum system. Quantum observables are represented by projection operators.
* State of a quantum system: A vector in the Hilbert space of the system.

Quantum logic can be used to reason about the relationships between different quantum observables and states. For example, it can be used to show that certain pairs of observables are incompatible, or that certain states are orthogonal. Quantum logic can also be used to define new quantum observables and to design and implement quantum algorithms.

Quantum logic is a complex and challenging subject, but it is also a very powerful one. It provides a deep understanding of the foundations of quantum mechanics and has the potential to revolutionize many fields, including computing, cryptography, and communication."
[{2023-10-12 retrieved} https://bard.google.com/chat/75be085360bdce8]

name::
* McsEngl.logicQuantum,
* McsEngl.quantum-logic!=logicQuantum,
* McsEngl.sciLogic.quantum!=logicQuantum,

sign of sciSemiotics

description::
"A SIGN is a material object (phenomenon, event) used to take the place of some other object, property or relation and is employed to obtain, store, process and transmit communications (information, knowledge)"
[{1989} Getmanova, Logic, p25]

In semiotics, a sign is "something that stands for something else, to someone in some capacity". (Marcel Danesi and Paul Perron, "Analyzing Cultures".) It may be understood as a discrete unit of meaning, and includes words, images, gestures, scents, tastes, textures, sounds — essentially all of the ways in which information can be communicated as a message by any sentient, reasoning mind to another.
[http://en.wikipedia.org/wiki/Sign_%28semiotics%29]

name::
* McsEngl.sciSemiotics'sign,
* McsEngl.sign//sciSemiotics,

relation-to-sematics of sciSemiotics

description::
"semantics vs semiotics
Semantics and semiotics are both branches of linguistics that study meaning. However, they do so in different ways.
Semantics is the study of the meaning of words and phrases in a language. It is concerned with the relationship between linguistic expressions and their referents, or the things they refer to. For example, the word "dog" refers to a four-legged mammal that barks.
Semiotics is the study of signs and symbols. It is a broader field than semantics, and it includes the study of meaning in all kinds of communication systems, including language, art, music, and gesture. A sign is anything that stands for something else. For example, a traffic light is a sign that tells us whether to stop or go.
In other words, semantics is the study of meaning in language, while semiotics is the study of meaning in all kinds of communication systems.
Here is a table that summarizes the key differences between semantics and semiotics:
Semantics | Semiotics
The study of meaning in language | The study of signs and symbols
Concerned with the relationship between linguistic expressions and their referents | Concerned with the relationship between signs and their signifieds
A narrower field of study | A broader field of study
Focuses on language | Can be applied to any communication system
Here are some examples of how semiotics can be used to study meaning in different communication systems:
* In art, semiotics can be used to analyze the meaning of paintings, sculptures, and other works of art. For example, a semiotician might analyze the symbols used in a painting to understand the artist's message.
* In music, semiotics can be used to analyze the meaning of songs, operas, and other musical pieces. For example, a semiotician might analyze the lyrics of a song to understand the singer's message.
* In gesture, semiotics can be used to analyze the meaning of body language. For example, a semiotician might analyze the way people nod their heads to understand what they are trying to communicate.
Semiotics is a complex and interdisciplinary field of study that has applications in many different areas. It is a valuable tool for understanding how meaning is created and conveyed in different communication systems."
[{2023-08-30 retrieved} https://bard.google.com/]

name::
* McsEngl.sciSemantics'relation-to-semiotics,
* McsEngl.sciSemiotics'relation-to-sematics,

info-resource of sciSemiotics

description::
Chandler.Daniel, Semiotics: The Basics, Routledge, London, UK, 2002. ISBN 0-415-36375-6

Jakobson.Roman, "Language in Relation to Other Communication Systems", pp. 570-579 in Selected Writings, Volume 2, Mouton, The Hague, 1971. ISBN 9-027-93178-X

name::
* McsEngl.sciSemiotics'Infrsc,