A Continuous Function Chart (CFC) is a graphic editor that can be used in conjunction with the STEP 7 software package or with other tools, such as CODESYS. It is used to create the entire software structure of the CPU from ready-made blocks. When working with the editor, you place blocks on function charts, assign parameters to them, and interconnect them. Interconnecting means, for example, that values are transferred from one output to one or more inputs during communication between the blocks. Continuous function charts are basically used for controlling continuous processes, where all the logic is executed and outputs are calculated in each PLC scan. Whereas in SFC, execution will be sequential as done is batch processes.
Ideonomy
Ideonomy is a combinatorial "science of ideas" developed by American independent scholar Patrick M. Gunkel (1947–2017). Specifically, Ideonomy is concerned with the systematic organization of ideas and the discovery of the rules behind how ideas combine, diverge, and transform. Gunkel defined ideonomy as "the science of the laws of ideas and of the application of such laws to the generation of all possible ideas in connection with any subject, idea, or thing." In his 1992 book A History of Knowledge, Charles Van Doren compared ideonomy to a "mining operation" that excavates meanings and thought to discover treasures hidden deep within language. Sources from the 1980s and 1990s demonstrate that ideonomy was useful to academic researchers in fields including biology, toxicology, and nursing/patient care. Beginning in the 2010s, academics in a wide range of fields including machine learning, marketing, computational modeling, and cybersecurity have relied on materials generated for ideonomy to provide methodological support for their research. == Etymology and definition == The word "ideonomy" combines the Greek roots ideo- (from idea, meaning pattern or form) and -nomy (from nomos, meaning law or custom). The suffix -nomy suggests the laws concerning or the totality of knowledge about a given subject, as in astronomy or taxonomy. In a note posted on the MIT ideonomy website, Gunkel states that the word was supposedly first coined by the French Encyclopedists to refer to a science of ideas. No evidence is provided for this statement, however. The concept bears some relationship to Antoine Destutt de Tracy's "ideology" (1796), which originally meant a systematic science of ideas before acquiring its modern political connotations. Gunkel provided several metaphorical descriptions of ideonomy: An "idea bank": a computer network enabling systematic exploration of infinite possible ideas A "kaleidoscope" that can exhibit all possible combinations and transformations of ideas A "prism" capable of diffracting any idea into its cognitive components A "gigantic microscope for magnifying the ideocosm" == History and development == In 1984, Gunkel received a five-year unsolicited grant from the Richard Lounsbery Foundation of New York to develop ideonomy. A June 1, 1987 article on the front page of The Wall Street Journal brought Gunkel and ideonomy to wider public attention. Some academics were interested in using ideonomy's techniques, including biologist Betsey Dyer, who published several contemporaneous peer-reviewed studies citing ideonomy. Academic researchers in the field of toxicology and nursing/patient care also used ideonomy. However, ideonomy's broadest contribution to date came beginning in the 2010s, as a list of personality traits generated for combinatorial matching was used by researchers in artificial intelligence to code human emotions for machine-learning tasks, develop computational models related to personality, develop a measurement framework for influencer-brand recommender systems, and aid information awareness/cybersecurity assessment. == Methodology == The foundational empirical method of ideonomy involves the systematic creation of extensive lists. Gunkel's apartment reportedly contained thousands of lists on every conceivable topic. Gunkel termed each list an "organon," which he described as expanding through "combination, permutation, transformation, generalization, specialization, intersection, interaction, reapplication, recursive use, etc. of existing organons." The ideonomic process follows a progressive structure. The ideonomist begins with a simple list of examples of a particular idea, concept, or thing. The list need not be exhaustive. By studying this list, the ideonomist isolates and identifies types. This categorical analysis then reveals missing items, allowing the primary list to be improved and refined. Gunkel emphasized that list items must not only cover genuine categories of nature but also be formulated in ways that yield the largest possible number of syntactically coherent possibilities when combined. The core technique of ideonomy is "ideocombinatorics"—the systematic intersection and combination of items from different lists to generate novel composite concepts. Gunkel developed computer programs to automate this process. For example, combining a list of 230 Universal Elementary Shapes (pits, pyramids, trenches, hemispheres, needles) with a list of 74 Types of Order (recurrence, identity, likeness of parts) yields 17,020 possible "shapes of order." These combinations, when phrased as questions ("Can there be pits of recurrence?"), could suggest new categories of phenomena worthy of investigation. The computer-generated output is typically repetitive and often meaningless. However, with sufficient frequency, the combinations yield results that are unexpectedly interesting and fruitful. In one documented case, Gunkel's programs generated 45,540 questions about toxins for microbiologist David Bermudes. One question—"Can hierarchies of cell process be used as a basis for classifying toxic action?"—prompted Bermudes to develop a novel approach to classifying biological toxins by the type of molecule they attack, rather than by chemical structure or physiological system affected. According to one contemporaneous account of ideonomy, "Gunkel takes for his field all fields and all ideas about anything. He uses a computer to generate lists of words and phrases and by juxtaposition reviews the resultant patterns for novel ideas. The computer is ideal for this task because the mind would rebel at the formidable processing task ideonomy involves. What we have here is computer generated originality." == Applications == Gunkel and his supporters identified several practical applications for ideonomic methods: Scientific research: Biologist Betsey Dyer of Wheaton College published research crediting ideonomy for helping to generate ideas. Medical science: When Austin pathologist Michael T. O'Brien was presented with the ideonomically-generated question "Can arteries have rashes?", he initially dismissed it as nonsense. Upon reflection, he realized that large arteries are supplied with blood by tiny vessels that might become inflamed and dilated, analogous to skin vessels in a rash—a phenomenon potentially worth researching. Analogical thinking: Harvard law professor Robert Clark used ideonomic analogies to write a research paper comparing plant structure with human hierarchies. Artificial intelligence: Douglas Lenat, a researcher at Microelectronics and Computer Technology Corporation (MCC) in Austin, suggested that Gunkel's lists enumerating types of human mistakes could help design AI systems capable of recognizing and correcting their own errors. == Reception and criticism == Ideonomy received mixed reactions from the academic and scientific communities. Prominent supporters included: Edward Fredkin, former director of MIT's computer science laboratory, who praised Gunkel's "provocative ideas on artificial intelligence." Marvin Minsky, AI scientist and MIT professor, who described ideonomy as "perhaps the most extensive study of ways to generate ideas." Frederick Seitz, president emeritus of Rockefeller University, who noted Gunkel's "encyclopedic scope" Robert C. Clark, Harvard law professor, who called Gunkel "the most intelligent person I ever met" However, skeptics questioned whether ideonomy constituted a genuine science. Fredkin himself noted that Gunkel "pours out about 60 ideas a minute, and 59 of them are bad," though he added that "even with one good idea out of 60, it's still an amazing accomplishment." Douglas Lenat observed that brainstorming with Gunkel was "a bit like being hit over the head by the muse with a sledgehammer" and that "he puts people off." Gunkel himself acknowledged that ideonomy was in its infancy and might seem "absurdly utopian." His planned magnum opus on ideonomy remained incomplete, and was posted on an MIT website thanks to faculty advisor Whitman Richards. Gunkel wrote: "Pioneering in a completely new field, yes in a new science, is almost unreal. It is heartbreaking, it is pitiable, it is almost inhuman. Honestly, it is a hell. There is nothing heroic about it." == Related concepts == Gunkel identified several historical precedents for ideonomic thinking: Gottfried Wilhelm Leibniz (1646–1716): The philosopher's work on a universal characteristic (characteristica universalis) and calculus of reasoning Peter Mark Roget (1779–1869): Creator of Roget's Thesaurus, which organized concepts into a systematic taxonomy Dmitri Mendeleev (1834–1907): Developer of the periodic table, demonstrating how combining lists of element families could reveal previously unseen connections Fritz Zwicky (1898–1974): The Caltech astrophysicist whom Gunkel called the "grandfather of ideonomy" for his development of "morphological research"—systematic exploration of all possible solutions t
Grid-oriented storage
Grid-oriented Storage (GOS) was a term used for data storage by a university project during the era when the term grid computing was popular. == Description == GOS was a successor of the term network-attached storage (NAS). GOS systems contained hard disks, often RAIDs (redundant arrays of independent disks), like traditional file servers. GOS was designed to deal with long-distance, cross-domain and single-image file operations, which is typical in Grid environments. GOS behaves like a file server via the file-based GOS-FS protocol to any entity on the grid. Similar to GridFTP, GOS-FS integrates a parallel stream engine and Grid Security Infrastructure (GSI). Conforming to the universal VFS (Virtual Filesystem Switch), GOS-FS can be pervasively used as an underlying platform to best utilize the increased transfer bandwidth and accelerate the NFS/CIFS-based applications. GOS can also run over SCSI, Fibre Channel or iSCSI, which does not affect the acceleration performance, offering both file level protocols and block level protocols for storage area network (SAN) from the same system. In a grid infrastructure, resources may be geographically distant from each other, produced by differing manufacturers, and have differing access control policies. This makes access to grid resources dynamic and conditional upon local constraints. Centralized management techniques for these resources are limited in their scalability both in terms of execution efficiency and fault tolerance. Provision of services across such platforms requires a distributed resource management mechanism and the peer-to-peer clustered GOS appliances allow a single storage image to continue to expand, even if a single GOS appliance reaches its capacity limitations. The cluster shares a common, aggregate presentation of the data stored on all participating GOS appliances. Each GOS appliance manages its own internal storage space. The major benefit of this aggregation is that clustered GOS storage can be accessed by users as a single mount point. GOS products fit the thin-server categorization. Compared with traditional “fat server”-based storage architectures, thin-server GOS appliances deliver numerous advantages, such as the alleviation of potential network/grid bottle-necks, CPU and OS optimized for I/O only, ease of installation, remote management and minimal maintenance, low cost and Plug and Play, etc. Examples of similar innovations include NAS, printers, fax machines, routers and switches. An Apache server has been installed in the GOS operating system, ensuring an HTTPS-based communication between the GOS server and an administrator via a Web browser. Remote management and monitoring makes it easy to set up, manage, and monitor GOS systems. == History == Frank Zhigang Wang and Na Helian proposed a funding proposal to the UK government titled “Grid-Oriented Storage (GOS): Next Generation Data Storage System Architecture for the Grid Computing Era” in 2003. The proposal was approved and granted one million pounds in 2004. The first prototype was constructed in 2005 at Centre for Grid Computing, Cambridge-Cranfield High Performance Computing Facility. The first conference presentation was at IEEE Symposium on Cluster Computing and Grid (CCGrid), 9–12 May 2005, Cardiff, UK. As one of the five best work-in-progress, it was included in the IEEE Distributed Systems Online. In 2006, the GOS architecture and its implementations was published in IEEE Transactions on Computers, titled “Grid-oriented Storage: A Single-Image, Cross-Domain, High-Bandwidth Architecture”. Starting in January 2007, demonstrations were presented at Princeton University, Cambridge University Computer Lab and others. By 2013, the Cranfield Centre still used future tense for the project. Peer-to-peer file sharings use similar techniques.
Predictor–corrector method
In numerical analysis, predictor–corrector methods belong to a class of algorithms designed to integrate ordinary differential equations – to find an unknown function that satisfies a given differential equation. All such algorithms proceed in two steps: The initial, "prediction" step, starts from a function fitted to the function-values and derivative-values at a preceding set of points to extrapolate ("anticipate") this function's value at a subsequent, new point. The next, "corrector" step refines the initial approximation by using the predicted value of the function and another method to interpolate that unknown function's value at the same subsequent point. == Predictor–corrector methods for solving ODEs == When considering the numerical solution of ordinary differential equations (ODEs), a predictor–corrector method typically uses an explicit method for the predictor step and an implicit method for the corrector step. === Example: Euler method with the trapezoidal rule === A simple predictor–corrector method (known as Heun's method) can be constructed from the Euler method (an explicit method) and the trapezoidal rule (an implicit method). Consider the differential equation y ′ = f ( t , y ) , y ( t 0 ) = y 0 , {\displaystyle y'=f(t,y),\quad y(t_{0})=y_{0},} and denote the step size by h {\displaystyle h} . First, the predictor step: starting from the current value y i {\displaystyle y_{i}} , calculate an initial guess value y ~ i + 1 {\displaystyle {\tilde {y}}_{i+1}} via the Euler method, y ~ i + 1 = y i + h f ( t i , y i ) . {\displaystyle {\tilde {y}}_{i+1}=y_{i}+hf(t_{i},y_{i}).} Next, the corrector step: improve the initial guess using trapezoidal rule, y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle y_{i+1}=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.} That value is used as the next step. === PEC mode and PECE mode === There are different variants of a predictor–corrector method, depending on how often the corrector method is applied. The Predict–Evaluate–Correct–Evaluate (PECE) mode refers to the variant in the above example: y ~ i + 1 = y i + h f ( t i , y i ) , y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},y_{i}),\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.\end{aligned}}} It is also possible to evaluate the function f only once per step by using the method in Predict–Evaluate–Correct (PEC) mode: y ~ i + 1 = y i + h f ( t i , y ~ i ) , y i + 1 = y i + 1 2 h ( f ( t i , y ~ i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},{\tilde {y}}_{i}),\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},{\tilde {y}}_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.\end{aligned}}} Additionally, the corrector step can be repeated in the hope that this achieves an even better approximation to the true solution. If the corrector method is run twice, this yields the PECECE mode: y ~ i + 1 = y i + h f ( t i , y i ) , y ^ i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) , y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ^ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},y_{i}),\\{\hat {y}}_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )},\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\hat {y}}_{i+1}){\bigr )}.\end{aligned}}} The PECEC mode has one fewer function evaluation than PECECE mode. More generally, if the corrector is run k times, the method is in P(EC)k or P(EC)kE mode. If the corrector method is iterated until it converges, this could be called PE(CE)∞.
Documentalist
A documentalist is a professional, trained in documentation science and specializing in assisting researchers in their search for scientific and technical documentation. With the development of bibliographical databases such as MEDLINE, documentalists were professionals who searched such databases on the behalf of users. When the field of documentation changed its name to information science, the terms information specialist or information professional often replaced the term documentalist.
Sketch Engine
Sketch Engine is a corpus manager and text analysis software developed by Lexical Computing since 2003. Its purpose is to enable people studying language behaviour (lexicographers, researchers in corpus linguistics, translators or language learners) to search large text collections according to complex and linguistically motivated queries. Sketch Engine gained its name after one of the key features, word sketches: one-page, automatic, corpus-derived summaries of a word's grammatical and collocational behaviour. Currently, it supports and provides corpora in over 100 languages. == History of development == Sketch Engine is a product of Lexical Computing, a company founded in 2003 by the lexicographer and research scientist Adam Kilgarriff. He started a collaboration with Pavel Rychlý, a computer scientist working at the Natural Language Processing Centre, Masaryk University, and the developer of Manatee and Bonito (two major parts of the software suite). Kilgarriff also introduced the concept of word sketches. Since then, Sketch Engine has been commercial software, however, all the core features of Manatee and Bonito that were developed by 2003 (and extended since then) are freely available under the GPL license within the NoSketch Engine suite. == Features == A list of tools available in Sketch Engine: Word sketches – a one-page automatic derived summary of a word's grammatical and collocational behaviour Word sketch difference – compares and contrasts two words by analysing their collocations Distributional thesaurus – automated thesaurus for finding words with similar meaning or appearing in the same/similar context Concordance search – finds occurrences of a word form, lemma, phrase, tag or complex structure Collocation search – word co-occurrence analysis displaying the most frequent words (for a search word) which can be regarded as collocation candidates Word lists – generates frequency lists which can be filtered with complex criteria n-grams – generates frequency lists of multi-word expressions Terminology / Keyword extraction (both monolingual and bilingual) – automatic extraction of key words and multi-word terms from texts (based on frequency count and linguistic criteria) Diachronic analysis (Trends) – detecting words which undergo changes in the frequency of use in time (show trending words) Corpus building and management – create corpora from the Web or uploaded texts including part-of-speech tagging and lemmatization which can be used as data mining software Parallel corpus (bilingual) facilities – looking up translation examples (EUR-Lex corpus, Europarl corpus, OPUS corpus, etc.) or building a parallel corpus from own aligned texts Text type analysis – statistics of metadata in the corpus === Keywords and terminology extraction === Sketch Engine can perform automatic term extraction by identifying words typical of a particular corpus, document, or text. Single words and multi-word units can be extracted from monolingual or bilingual texts. The terminology extraction feature provides a list of relevant terms based on comparison with a large corpus of general language. This functionality is also available as a separate service called OneClick Terms with a dedicated interface. === SKELL === A free web service based on Sketch Engine and aimed at language learners and teachers is SKELL (formerly SkELL). It exploits Sketch Engine's proprietary GDEX (Good Dictionary Examples) scoring function to provide authentic example sentences for specific target words. Results are drawn from a special corpus of high-quality texts covering everyday, standard, formal, and professional language and displayed as a concordance. SKELL also includes simplified versions of Sketch Engine's word sketch and thesaurus functions. It has been suggested that SKELL can be used, for instance, to help students understand the meaning and/or usage of a word or phrase; to help teachers wanting to use example sentences in a class; to discover and explore collocates; to create gap-fill exercises; to teach various kinds of homonyms and polysemous words. SKELL was first presented in 2014, when only English was supported. Later, support was added for Russian, Czech, German, Italian and Estonian. == List of text corpora == Sketch Engine provides access to more than 800 text corpora. There are monolingual as well as multilingual corpora of different sizes (from one thousand words up to 85 billion words) and various sources (e.g. web, books, subtitles, legal documents). The list of corpora includes British National Corpus, Brown Corpus, Cambridge Academic English Corpus and Cambridge Learner Corpus, CHILDES corpora of child language, OpenSubtitles (a set of 60 parallel corpora), 24 multilingual corpora of EUR-Lex documents, the TenTen Corpus Family (multi-billion web corpora), and Trends corpora (monitor corpora with daily updates). == Architecture == Sketch Engine consists of three main components: an underlying database management system called Manatee, a web interface search front-end called Bonito, and a web interface for corpus building and management called Corpus Architect. === Manatee === Manatee is a database management system specifically devised for effective indexing of large text corpora. It is based on the idea of inverted indexing (keeping an index of all positions of a given word in the text). It has been used to index text corpora comprising tens of billions of words. Searching corpora indexed by Manatee is performed by formulating queries in the Corpus Query Language (CQL). Manatee is written in C++ and offers an API for a number of other programming languages including Python, Java, Perl and Ruby. Recently, it was rewritten into Go for faster processing of corpus queries. === Bonito === Bonito is a web interface for Manatee providing access to corpus search. In the client–server model, Manatee is the server and Bonito plays the client part. It is written in Python. === Corpus Architect === Corpus Architect is a web interface providing corpus building and management features. It is also written in Python. == Applications == Sketch Engine has been used by major British and other publishing houses for producing dictionaries such as Macmillan English Dictionary, Dictionnaires Le Robert, Oxford University Press or Shogakukan. Four of United Kingdom's five biggest dictionary publishers use Sketch Engine.
Algorithmic mechanism design
Algorithmic mechanism design (AMD) lies at the intersection of economic game theory, optimization, and computer science. The prototypical problem in mechanism design is to design a system for multiple self-interested participants, such that the participants' self-interested actions at equilibrium lead to good system performance. Typical objectives studied include revenue maximization and social welfare maximization. Algorithmic mechanism design differs from classical economic mechanism design in several respects. It typically employs the analytic tools of theoretical computer science, such as worst case analysis and approximation ratios, in contrast to classical mechanism design in economics which often makes distributional assumptions about the agents. It also considers computational constraints to be of central importance: mechanisms that cannot be efficiently implemented in polynomial time are not considered to be viable solutions to a mechanism design problem. This often, for example, rules out the classic economic mechanism, the Vickrey–Clarke–Groves auction. == History == Noam Nisan and Amir Ronen first coined "Algorithmic mechanism design" in a research paper published in 1999.