T-norm fuzzy logics T-norm fuzzy logics are a family of non-classical logics, informally delimited by having a semantics which takes the real unit interval [0, 1] for the system of truth values and functions called t-norms for permissible interpretations of conjunction. They are mainly used in applied fuzzy logic and fuzzy set theory as a theoretical basis for approximate reasoning. T-norm fuzzy logics belong in broader classes of fuzzy logics and many-valued logics. In order to generate a well-behaved implication, the t-norms are usually required to be left-continuous; logics of left-continuous t-norms further belong in the class of substructural logics, among which they are marked with the validity of the law of prelinearity, (A → B) ∨ (B → A). Both propositional and first-order (or higher-order) t-norm fuzzy logics, as well as their expansions by modal and other operators, are studied. Motivation T-norm fuzzy logics impose certain natural constraints on the truth function of conjunction. if and or
The Computer Revolution/Databases/Database Models Hierarchical Databases Hierarchical databases are the oldest database models. Unlike other models, they do not have a well documented history. The hierarchical database was the first one developed and therefore was commonly used in the first mainframe database management systems.They were developed out of the 1950's and 60's Information Management Systems. Many banks and insurance companies, as well as government departments and hospitals ( for inventory and accounting systems) still use them today. The hierarchical database stores data in a series of records. As an example, we could have a tree representing a university department, with subtrees representing staff members, students, courses, and facilities. A hierarchical database has a very structured form, as it allows no links between layers in different branches of the tree. Advantages of the Database Shortcomings of the Database Currently this type of database is not utilized to its full potential. 1. 2. 3. 4. 5.
ATC/DDD Anatomical-therapeutic-chemical Classification with Defined Daily Doses ATC-Classification with Defined Daily Doses DIMDI publishes the annually updated official version of the German Anatomical Therapeutic Chemical (ATC)-Classification with defined daily doses (DDD) since January 1st, 2004. You can download a PDF file of the official German ATC-Classification (in German) for free: ATC/DDD as PDF file for free at downloadcenter Classification You can download an Excel file of the official ATC-Classification with DDD from the WldO website (on the right side below "Downloads") for free: ATC/DDD as Excel file for free at WIdO It is pointed out that in case of probable differences only the PDF file is binding which can be downloaded from the DIMDI website via the above mentioned link. ATC-Classification In the ATC-Classification substances are divided into different groups according to the organ or organ system which they affect and their chemical, pharmacological and therapeutic properties.A defined daily dose is assigned to each active substance. Legal Background
Defuzzification Defuzzification is the process of producing a quantifiable result in fuzzy logic, given fuzzy sets and corresponding membership degrees. It is typically needed in fuzzy control systems. These will have a number of rules that transform a number of variables into a fuzzy result, that is, the result is described in terms of membership in fuzzy sets. For example, rules designed to decide how much pressure to apply might result in "Decrease Pressure (15%), Maintain Pressure (34%), Increase Pressure (72%)". The simplest but least useful defuzzification method is to choose the set with the highest membership, in this case, "Increase Pressure" since it has a 72% membership, and ignore the others, and convert this 72% to some number. A common and useful defuzzification technique is center of gravity. Methods There are many different methods of defuzzification available, including the following: The maxima methods are good candidates for fuzzy reasoning systems. Notes See also
What are relational databases?" Databases have been a staple of business computing from the very beginning of the digital era. In fact, the relational database was born in 1970 when E.F. Codd, a researcher at IBM, wrote a paper outlining the process. Since then, relational databases have grown in popularity to become the standard. Originally, databases were flat. This means that the information was stored in one long text file, called a tab delimited file. Lname, FName, Age, Salary|Smith, John, 35, $280|Doe, Jane, 28, $325|Brown, Scott, 41, $265|Howard, Shemp, 48, $359|Taylor, Tom, 22, $250 You can see that you have to search sequentially through the entire file to gather related information, such as age or salary. With a relational database, you can quickly compare information because of the arrangement of data in columns. The "relational" part of the name comes into play because of mathmatical relations. Here are some interesting links:
DIMDI - ATC/DDD Anatomical-therapeutic-chemical Classification with Defined Daily Doses ATC-Classification with Defined Daily Doses DIMDI publishes the annually updated official version of the German Anatomical Therapeutic Chemical (ATC)-Classification with defined daily doses (DDD) since January 1st, 2004. You can download a PDF file of the official German ATC-Classification (in German) for free: ATC/DDD as PDF file for free at downloadcenter Classification You can download an Excel file of the official ATC-Classification with DDD from the WldO website (on the right side below "Downloads") for free: ATC/DDD as Excel file for free at WIdO It is pointed out that in case of probable differences only the PDF file is binding which can be downloaded from the DIMDI website via the above mentioned link. ATC-Classification In the ATC-Classification substances are divided into different groups according to the organ or organ system which they affect and their chemical, pharmacological and therapeutic properties.A defined daily dose is assigned to each active substance. Legal Background
Fuzzy associative matrix A fuzzy associative matrix expresses fuzzy logic rules in tabular form. These rules usually take two variables as input, mapping cleanly to a two-dimensional matrix, although theoretically a matrix of any number of dimensions is possible. Suppose a professional is tasked with writing fuzzy logic rules for a video game monster. In the game being built, entities have two variables: hit points (HP) and firepower (FP): This translates to: IF MonsterHP IS VeryLowHP AND MonsterFP IS VeryWeakFP THEN Retreat IF MonsterHP IS LowHP AND MonsterFP IS VeryWeakFP THEN Retreat IF MonsterHP IS MediumHP AND MonsterFP is VeryWeakFP THEN Defend Multiple rules can fire at once, and often will, because the distinction between "very low" and "low" is fuzzy. An implementation of this system might use either the matrix or the explicit IF/THEN form. There is no inherent pattern in the matrix. This does not mean a fuzzy system should be sloppy. Fuzzy associative memory
Choosing a Database Oracle, SQL Server, Microsoft Access, MySQL, DB2, Paradox. There are quite a variety of database products on the market today, making the selection of a platform for your organization's infrastructure a daunting project. Define Your Requirements Database management systems (or DBMSs) can be divided into two categories -- desktop databases and server databases. Generally speaking, desktop databases are oriented toward single-user applications and reside on standard personal computers (hence the term desktop). Server databases contain mechanisms to ensure the reliability and consistency of data and are geared toward multi-user applications. It's important to do a careful needs analysis before you dive in and commit to a database solution. The needs analysis process will be specific to your organization but, at a minimum, should answer the following questions: Who will be using the database and what tasks will they perform? Desktop Databases Server Databases Flexibility.
DIMDI - ICD-10-GM Version 2016 Übersicht über die Kapitel Kapitel IBestimmte infektiöse und parasitäre Krankheiten(A00-B99) Dieses Kapitel gliedert sich in folgende Gruppen: Kapitel IINeubildungen(C00-D48) Kapitel VIKrankheiten des Nervensystems(G00-G99) G00-G09Entzündliche Krankheiten des ZentralnervensystemsG10-G14Systematrophien, die vorwiegend das Zentralnervensystem betreffenG20-G26Extrapyramidale Krankheiten und BewegungsstörungenG30-G32Sonstige degenerative Krankheiten des NervensystemsG35-G37Demyelinisierende Krankheiten des ZentralnervensystemsG40-G47Episodische und paroxysmale Krankheiten des NervensystemsG50-G59Krankheiten von Nerven, Nervenwurzeln und NervenplexusG60-G64Polyneuropathien und sonstige Krankheiten des peripheren NervensystemsG70-G73Krankheiten im Bereich der neuromuskulären Synapse und des MuskelsG80-G83Zerebrale Lähmung und sonstige LähmungssyndromeG90-G99Sonstige Krankheiten des Nervensystems Kapitel XVSchwangerschaft, Geburt und Wochenbett(O00-O99)
Cognitive map Overview Cognitive maps serve the construction and accumulation of spatial knowledge, allowing the "mind's eye" to visualize images in order to reduce cognitive load, enhance recall and learning of information. This type of spatial thinking can also be used as a metaphor for non-spatial tasks, where people performing non-spatial tasks involving memory and imaging use spatial knowledge to aid in processing the task. The neural correlates of a cognitive map have been speculated to be the place cell system in the hippocampus and the recently discovered grid cells in the entorhinal cortex. Neurological basis Cognitive mapping is believed to largely be a function of the hippocampus. Numerous studies by O'Keefe have implicated the involvement of place cells. Parallel map theory Generation The cognitive map is generated from a number of sources, both from the visual system and elsewhere. History The idea of a cognitive map was first developed by Edward C.
Home The Department for Education's register of educational establishments in England and Wales. Using the search box below select school type and location to quickly find establishments in your local area or use the advanced search page for further search criteria including establishments that are closed or planned to open in the future. From this page you are able to perform a simple search of all open establishments in England and Wales. If you leave a field blank, the default will include all establishments. The 'Show me' drop down list allows you to select the type of establishment you wish to see in your search results. You have the ability to search by location, and can set a radius of inclusion. When searching by establishment name, often it is best to search for a single word in a name. Church of England is generally referred to as CofE in EduBase. For more advanced searches, or to find closed schools, please use the Advanced search page Contacting EduBase Disclaimer
National drug databases - EU :: INFOlinks Explanatory note: ATC = database allows search according to ATC classification. Rx/OTC = database allows search according to Rx/OTC. Germany - Fachinformation or Summary of Product Characteristics and Package Leaflet, Public Assessment Reports (for medicines registered via NP or where BfArM is RMS). Lotfi A. Zadeh Lotfali Askar Zadeh (born February 4, 1921), better known as Lotfi A. Zadeh, is a mathematician, electrical engineer, computer scientist, artificial intelligence researcher and professor emeritus of computer science at the University of California, Berkeley. Life and career Zadeh was born in Baku, Azerbaijan SSR, as Lotfi Aliaskerzadeh, to an Iranian Azerbaijanis father from Ardabil, Rahim Aleskerzade, who was a journalist on assignment from Iran, and a Russian Jewish mother, Fanya Koriman, who was a pediatrician. The Soviet government at this time courted foreign correspondents, and the family lived well while in Baku. Zadeh attended elementary school for three years there, which he has said "had a significant and long-lasting influence on my thinking and my way of looking at things. In 1931, when Zadeh was ten years old, his family moved to Tehran in Iran, his father's homeland. Personal life and beliefs Work Fuzzy sets and systems 1965. Notes