Academic literature on the topic 'Technical jargon'

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Journal articles on the topic "Technical jargon"

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Parrot, Fred. "Jargon or dialect?" English Today 2, no. 3 (1986): 19–22. http://dx.doi.org/10.1017/s0266078400002157.

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When is ‘jargon’ simply necessary technical language, and when does technical language blur into unnecessary jargon? Teachers of English can help their students not only to deal with questions like these, but also to identify authentic bafflegab when they come across it.
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Hirst, Russel. "Scientific Jargon, Good and Bad." Journal of Technical Writing and Communication 33, no. 3 (2003): 201–29. http://dx.doi.org/10.2190/j8jj-4yd0-4r00-g5n0.

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Scientific and technical jargon—specialized vocabulary, usually Latinate—plays a vital role in scientific and technical communication. But its proper use continues to be a point of discussion because of our concern with audience adaptation, rhetorical exigence, rhetorical purpose, and ethics. We've focused on teaching students—and on convincing scientists, engineers, and other writers/speakers—to gear their specialized language to the recipients of their communication, to the occasion calling for their communication, to what they wish to accomplish through their communication, and to the ethical goals of safety, helpfulness, empowerment, and truth. These are exactly the sorts of things we should be doing. My contribution to this conversation is a reinforce ment and, I hope, an extension of the argument that we should also be teaching and convincing students and professionals: 1) to fully appreciate what makes jargon either good or bad; 2) to carefully distinguish jargon usage from other aspects of scientific and technical style; and 3) to recognize that in every context, even in communication among experts, jargon should be used judiciously—that is, in the most helpful, least taxing way.
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Willoughby, Shannon D., Keith Johnson, and Leila Sterman. "Quantifying scientific jargon." Public Understanding of Science 29, no. 6 (2020): 634–43. http://dx.doi.org/10.1177/0963662520937436.

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When scientists disseminate their work to the general public, excessive use of jargon should be avoided because if too much technical language is used, the message is not effectively conveyed. However, determining which words are jargon and how much jargon is too much is a difficult task, partly because it can be challenging to know which terms the general public knows, and partly that it can be challenging to ensure scientific accuracy while avoiding esoteric terminology. To help address this issue, we have written an R script that an author can use to quantify the amount of scientific jargon in any written piece and make appropriate edits based on the target audience.
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Lindsley, Ogden R. "FROM TECHNICAL JARGON TO PLAIN ENGLISH FOR APPLICATION." Journal of Applied Behavior Analysis 24, no. 3 (1991): 449–58. http://dx.doi.org/10.1901/jaba.1991.24-449.

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Ammons, Douglas. "Psychology of the Scientist: LXXIII. Miscommunication in Technical Writing." Perceptual and Motor Skills 86, no. 2 (1998): 418. http://dx.doi.org/10.2466/pms.1998.86.2.418.

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Parés-Casanova, P. M. "Anatomic Veterinary Usage: Discussion Around Technical Nomenclature Vs. Jargon." Journal of Veterinary Medical Education 48, no. 3 (2021): 240–41. http://dx.doi.org/10.3138/jvme-2020-0151.

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This article discusses some examples of problems derived from the mix of technical jargon and anatomical veterinary words and makes recommendations regarding their use according to specific situations.
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Ammons, Douglas. "Psychology of the Scientist: LXXV. Miscommunication in Technical Writing-Concatenations." Perceptual and Motor Skills 86, no. 2 (1998): 570. http://dx.doi.org/10.2466/pms.1998.86.2.570.

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Ammons, Douglas. "Psychology of the Scientist: LXXIV. Miscommunication in Technical Writing-Acronyms." Psychological Reports 82, no. 2 (1998): 526. http://dx.doi.org/10.2466/pr0.1998.82.2.526.

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Technical writing often uses jargon that can obfuscate meaning. A common form is the acronym, which replaces descriptive words with alphanumeric slang and creates highly similar terms with different meanings.
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Zupan, Simon, and Marko Štefanič. "Military Jargon in the Slovenian Translation of Hostile Waters." ELOPE: English Language Overseas Perspectives and Enquiries 11, no. 1 (2014): 165–77. http://dx.doi.org/10.4312/elope.11.1.165-177.

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The article examines Slovenian translations of military jargon in the non-fiction novel Hostile Waters. In the introductory part, jargon is presented as a linguistic category as well as its main features in the novel. Next, select examples from the original text are compared to their Slovenian equivalents. The focus is on collocations and lexically dense nominal phrases. The comparison finds that most translation shifts in the target text occur because of incorrect interpretation of technical jargon expressions in the original. As a result, the target text reader perceives certain situations differently than the source text reader.
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Timms, Philip. "A Devil's dictionary for mental health." BJPsych Bulletin 41, no. 5 (2017): 244–46. http://dx.doi.org/10.1192/pb.bp.116.055442.

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SummaryClinical psychiatry, for all its emphasis on scientific rigour, is mediated mainly by words rather than by numbers. As with other professional areas, it has developed its own set of jargon words and phrases. Many of these are not the technical terms traditionally seen as jargon, but standard English words and phrases used in an idiosyncratic way. They therefore go unnoticed as jargon, while enfeebling our communications. I have used the template of Ambrose Bierce's The Devil's Dictionary to highlight some examples, with the aim of helping us all to talk, write and, perhaps, think more clearly.
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Dissertations / Theses on the topic "Technical jargon"

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Poni, Tsepo. "Grade 10 english second language pupils` difficulties with paradoxical jargon and technical terms commonly used in their mathematics curriculum." Thesis, 2014.

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This study investigates, the extent of the difficulty that Grade 10 English second language learners have with some of the mathematics terms which are commonly used in their curriculum.A selection of twenty technical terms and thirteen paradoxical jargon terms as extracted from four different textbooks used for their syllabus. A multiple-choice test was constructed and given to them. A huge and disturbing percentage of the learners writing the test could not choose the correct meaning of both the technical and paradoxical jargon terms tested. Although the learners performed better on the paradoxical jargon section than on the technical section of the test, the difference was shown to be not significant. These results have serious implications for the teaching and learning of mathematics.
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Books on the topic "Technical jargon"

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Green, Jonathon. Dictionary of jargon. Routledge & Kegan Paul, in association with Methuen, 1987.

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Janich, Nina. Fachliche Information und inszenierte Wissenschaft: Fachlichkeitskonzepte in der Wirtschaftswerbung. Narr, 1998.

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Ickler, Theodor. Die Disziplinierung der Sprache: Fachsprachen in unserer Zeit. Narr, 1997.

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Merkow, Mark S. Breaking through technical jargon: A dictionary of computer and automation acronyms. Van Nostrand Reinhold, 1990.

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Seminario de Lengua Española in Memóriam José Perona (2009 Universidad de Murcia). Nuevas claves para el estudio de la lengua española: Usos especializados en la comunicación. Edited by Díez de Revenga Torres, Pilar, Puche Lorenzo Miguel Angel, and Perona Sánchez José D. Universidad de Murcia, Servicio de Publicaciones, 2011.

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Deutsche Naturwissenschaftssprachen: Historische und kritische Studien. Narr, 1986.

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Roelcke, Thorsten. Fachsprachen. 3rd ed. Erich Schmidt, 2010.

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Zhang, Dingxian. Komplexe lexikalische Einheiten in Fachsprachen: Eine Untersuchung am Beispiel der Fachsprachen der Umformtechnik und der Fertigungstechnik. J. Groos, 1990.

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Poll, L. van de. Bibliografie van lijsten met Nederlandstalige vakterminologie. Stichting Bibliographia Neerlandica, 1988.

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Tenner, Edward. Tech speak, or, How to talk high tech: An advanced post-vernacular discourse modulation protocol. Crown, 1986.

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Book chapters on the topic "Technical jargon"

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"ABBREVIATIONS OF TECHNICAL TERMS." In Computer Jargon Explained. Elsevier, 1989. http://dx.doi.org/10.1016/b978-1-4831-3553-3.50072-4.

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Parikh, Mahendra, and Nithya Gogtay. "Demystification of Technical Jargon." In ABC of Research Methodology and Applied Biostatistics—A Primer for Clinicians and Researchers. Jaypee Brothers Medical Publishers (P) Ltd., 2009. http://dx.doi.org/10.5005/jp/books/10026_15.

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Heye, Dennie. "You speak the technical jargon." In Characteristics of the Successful 21st Century Information Professional. Elsevier, 2006. http://dx.doi.org/10.1016/b978-1-84334-145-1.50014-9.

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Kukulska-Hulme, Agnes. "Explanation and Assistance." In Language and Communication. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195108385.003.0015.

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It is easy to say that an explanation should be simple and that it should be written in familiar language. Advice of this sort is offered in many a technical writing handbook, often with limited elaboration. The concepts of familiarity and simplicity are not as straightforward as they appear to be, however. Users’ familiarity with particular terms can never really be predetermined because of the different ways we can “know” a word (as described in chapter 2). And what exactly is “simple” language? Hartley (1985), in his book on designing instructional text, makes several references to a writer’s conscious selection of words for a target text. Word length is mentioned (short, familiar words are easier to understand, although some long words, because of their frequent use, are quite familiar, e.g., “communication”), word type (concrete words and phrases are clearer than abstract ones), and ambiguity resulting from excessive use of abbreviations and acronyms. He also writes about the option of using readability formulas to check the suitability of a text for a given reader age group. Emden (1990) devotes a section to vocabulary choice in her handbook on writing for engineers and offers this advice: “Use words which the reader will understand”. She rightly points out the insidious danger in the use of technical language: “The reader may assume that he understands and the writer may assume that he is understood. Both may be understanding different meanings”. Sides (1984), cautioning about the use of jargon in papers and reports on computer technology, says this: “The issue of jargon is audience-dependent. Always use what the audience will understand”. This is sound advice, yet on reflection, it is so cursory that it is doubtful whether it can genuinely be fol lowed. The writer can strive to get to know the audience, and even think about providing definitions of terms, but there is still the matter of knowing how to select words or adapt one’s “jargon.” Use “fewer and simpler words,” advises Sides, referring to S. T. Coleridge as an authority on the matter.
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Bains, Sunny. "Audience and Explanation." In Explaining the Future. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198822820.003.0005.

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“Audience and Explanation,” which is the first of three chapters on communicating technical work, starts by focusing on the audience and how understanding their backgrounds and interests is critical to being able to report in a way that is meaningful to them. It consider their motivation for reading, what kinds of explanations will make sense to them, what kind of visualizations will be helpful, and the language that they will (or won’t) be able to follow. A section on jargon explains when to use a word without explanation, when to define, and when to avoid using a technical word altogether. There is also a discussion on the use of various kinds of figures (e.g. charts, photographs, and diagrams), how they are best used, captions, and how to avoid problems with copyright and plagiarism.
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Balaji, Seetharaman. "An Overview of Biological Data Mining." In Biotechnology. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8903-7.ch005.

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The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.
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Balaji, Seetharaman. "An Overview of Biological Data Mining." In Library and Information Services for Bioinformatics Education and Research. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1871-6.ch007.

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The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.
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Postal, Karen. "Disrupting Our Academic Communication Style." In Testimony That Sticks. Oxford University Press, 2019. http://dx.doi.org/10.1093/med-psych/9780190467395.003.0001.

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As scientists and clinicians, we have grown up in an academic tradition that tells us to communicate with jargon-filled, technical language and to be so fearful of making an error that reading from slides verbatim at a conference appears to be a sane communication choice. Our years of scientific training may actually result in a net loss of our ability to communicate clearly and simply about the neuroscience we love. Use of an academic communication style interferes with jurors’ ability to access our messages and also reduces our credibility, as we may unwittingly appear distant and condescending. This chapter shares how principles of improvisation can help us disrupt the inaccessible academic communication style we all learned in graduate school. Shedding jargon, giving ourselves permission to allow emotion to creep back into our language, freeing up our body language, and using vivid, clear language allows us to create moments of genuine, productive communication with jurors and other triers of fact. At its heart, improv challenges us to be human, connect with others in ways that are meaningful to others, not take ourselves so seriously, and share the joy/fun we experience in the field we chose.
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Reynolds, Matthew. "1. Crossing languages." In Translation: A Very Short Introduction. Oxford University Press, 2016. http://dx.doi.org/10.1093/actrade/9780198712114.003.0001.

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What is translation? Is translation any different from communication in general? ‘Crossing languages’ considers the range of ways of doing things with words that can be thought of as translation, including from one foreign language to another, but also less obvious examples such as from technical jargon into common language. Firstly, translation does not simply jump from one language to another—there is a no-man’s land called ‘translationese’, which is a blend of two languages. Secondly, all translation involves diplomacy, mediation, and the avoidance of conflict. Thirdly, every translation is a crowd translation, drawing on more than one source text and different people’s expectations. But this is just the beginning.
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Karel-Longuevergne, Diana. "Localization for E-Learning." In Cases on Globalized and Culturally Appropriate E-Learning. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61520-989-7.ch015.

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Localization and translation often go ‘hand in hand’, as explained in the first chapter, but as this author explains and illustrates, localization requires the skills of an experienced professional or company to address cultural nuances in e-learning that could, if not addressed, have a negative impact on learners, on learning, and in some cases, on business. In this chapter, you learn how names, characters, wordplay, technical jargon, geoculturalism, etc. in e-learning courses should be addressed so that users sense that the course was developed ‘locally’, for them. In addition, avoid the ‘technological pitfalls’ related to embedded text, audio and video synchronization, and so forth.
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Conference papers on the topic "Technical jargon"

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Meyers, Adam, Zachary Glass, Angus Grieve-Smith, Yifan He, Shasha Liao, and Ralph Grishman. "Jargon-Term Extraction by Chunking." In Proceedings of the COLING Workshop on Synchronic and Diachronic Approaches to Analyzing Technical Language. Association for Computational Linguistics and Dublin City University, 2014. http://dx.doi.org/10.3115/v1/w14-6002.

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Sexton, Thurston, and Mark Fuge. "Using Semantic Fluency Models Improves Network Reconstruction Accuracy of Tacit Engineering Knowledge." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98429.

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Abstract Human- or expert-generated records that describe the behavior of engineered systems over a period of time can be useful for statistical learning techniques like pattern detection or output prediction. However, such data often assumes familiarity of a reader with the relationships between entities within the system — that is, knowledge of the system’s structure. This required, but unrecorded “tacit” knowledge makes it difficult to reliably learn patterns of system behavior using statistical modeling techniques on these written records. Part of this difficulty stems from a lack of good models for how engineers generate written records of a system, given their expertise, since they often create such records under time pressure using shorthand notation or internal jargon. In this paper, we model the process of maintenance work order creation as a modified semantic fluency task, to build a probabilistic generative model that can uncover underlying relationships between entities referenced within a complex system. Compared to more traditional similarity-metric-based methods for structure recovery, we directly model a possible cognitive process by which technicians may record work-orders. Mathematically, we represent this as a censored local random walk over a latent network structure representing tacit engineering knowledge. This allows us to recover implied engineering knowledge about system structure by processing written records. Additionally, we show that our model leads to improved generative capabilities for synthesizing plausible data.
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Akay, Haluk, Maria Yang, and Sang-Gook Kim. "Automating Design Requirement Extraction From Text With Deep Learning." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-66898.

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Abstract Nearly every artifact of the modern engineering design process is digitally recorded and stored, resulting in an overwhelming amount of raw data detailing past designs. Analyzing this design knowledge and extracting functional information from sets of digital documents is a difficult and time-consuming task for human designers. For the case of textual documentation, poorly written superfluous descriptions filled with jargon are especially challenging for junior designers with less domain expertise to read. If the task of reading documents to extract functional requirements could be automated, designers could actually benefit from the distillation of massive digital repositories of design documentation into valuable information that can inform engineering design. This paper presents a system for automating the extraction of structured functional requirements from textual design documents by applying state of the art Natural Language Processing (NLP) models. A recursive method utilizing Machine Learning-based question-answering is developed to process design texts by initially identifying the highest-level functional requirement, and subsequently extracting additional requirements contained in the text passage. The efficacy of this system is evaluated by comparing the Machine Learning-based results with a study of 75 human designers performing the same design document analysis task on technical texts from the field of Microelectromechanical Systems (MEMS). The prospect of deploying such a system on the sum of all digital engineering documents suggests a future where design failures are less likely to be repeated and past successes may be consistently used to forward innovation.
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Noever, David, Josh Kalin, Matthew Ciolino, Dom Hambrick, and Gerry Dozier. "Local Translation Services for Neglected Languages." In 8th International Conference on Artificial Intelligence and Applications (AIAP 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110110.

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Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages. For projects with protected or personal data, translators for less popular or low-resource languages require specific compliance checks before posting to a public translation API. In these cases, locally run translators can render reasonable, cost-effective solutions if done with an army of offline, smallscale pair translators. Like handling a specialist’s dialect, this research illustrates translating two historically interesting, but obfuscated languages: 1) hacker-speak (“l33t”) and 2) reverse (or “mirror”) writing as practiced by Leonardo da Vinci. The work generalizes a deep learning architecture to translatable variants of hacker-speak with lite, medium, and hard vocabularies. The original contribution highlights a fluent translator of hacker-speak in under 50 megabytes and demonstrates a companion text generator for augmenting future datasets with greater than a million bilingual sentence pairs. A primary motivation stems from the need to understand and archive the evolution of the international computer community, one that continuously enhances their talent for speaking openly but in hidden contexts. This training of bilingual sentences supports deep learning models using a long short-term memory, recurrent neural network (LSTM-RNN). It extends previous work demonstrating an English-to-foreign translation service built from as little as 10,000 bilingual sentence pairs. This work further solves the equivalent translation problem in twenty-six additional (non-obfuscated) languages and rank orders those models and their proficiency quantitatively with Italian as the most successful and Mandarin Chinese as the most challenging. For neglected languages, the method prototypes novel services for smaller niche translations such as Kabyle (Algerian dialect) which covers between 5-7 million speakers but one which for most enterprise translators, has not yet reached development. One anticipates the extension of this approach to other important dialects, such as translating technical (medical or legal) jargon and processing health records or handling many of the dialects collected from specialized domains (mixed languages like “Spanglish”, acronym-laden Twitter feeds, or urban slang).
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Lakhanpal, Vikrant, and Robello Samuel. "Privacy-Focused Blockchain Solution for Production Royalty Management." In SPE Western Regional Meeting. SPE, 2021. http://dx.doi.org/10.2118/200764-ms.

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Abstract Oil and gas operators have a multitude of tasks to perform for smooth business operations. In addition to various engineering, technical, and operational decisions, there are back-office operations that should be efficient to keep business running smoothly, including production royalty management (PRM). The current system for PRM is extremely complex and costly, and involvesmultiple parties/contracts and often causes disputes. A blockchain-based solution allows the organizations to better navigate complex laws, courtrulings, and legal jargon that determine how billions of dollars are distributed every year. There are two aspects to PRM —royalties owed and royalties received—and the proposed solution caters to both operators and independent mineral owners. The operator should compile production and revenue data monthly and distribute it to all owners as per their individual lease. The expenses and marketing costs should be entered into the accounting system before revenue is distributed. Owners may be skeptical about data shared; therefore, the entire process can take up to two or more months. Furthermore, privacy is of utmost importance because theoperator can neither share data between owners nor disclose proprietary information. This paper proposes the entire royalty management system be hosted on a permissioned blockchain. Given their intrinsic nature of trustworthiness and transparent execution of transactions involving multiple parties, blockchain can streamline the royalty distribution process. The design and implementation of a smart contract for consent-driven and double-blind data sharingon a blockchain platform is presented. The system is designed for both inter- and intra-organizational transaction—a local peer interacts with an associated anchor peer within the globalnetwork. In addition to transparency and efficiency, this method provides important features ofprivacy and auditability while limiting ledger size. Further, peer identities are hiddenfrom each other while they collectively agree on transaction outcomes, and data will never be shared among the peers of the blockchain without explicit consent from the owners to ensure that the conflicting objectives of privacy and transparency are met. Blockchain is peer-to-peer decentralized digital ledger technology with promising potential. Even after all the recent innovations, the owner-relations department still operates using old methods. An innovative blockchain-based solution to reduce time and money spent on disbursing revenue for the operator and to increase the time value of information and trust in the system for the owner is presented.
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Tormen, Damiano, Pietro Giannattasio, Alessandro Zanon, Helmut Kühnelt, and Michele De Gennaro. "Application of a RANS-Informed Analytical Model for Fast Noise Prediction of Contra Rotating Open Rotors." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64162.

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The present work focuses on the fast prediction of the interaction noise (IN) components of a Contra Rotating Open Rotor (CROR) engine at take-off. The flow field past the CROR is computed using a steady RANS approach coupled with the concept of mixing plane between the rotors to remove the flow unsteadiness due to the propeller interaction. The effects of such interaction are then recovered applying the analytical model of Jaron et al. (2014), balanced with data extracted from the RANS solution, to extrapolate the information about the wake of the front rotor and the potential flow fields through the mixing plane. This RANS-informed approximation allows recovering the unsteadiness of the flow-blades interaction in terms of unsteady blade response. The tonal noise at the blade passing frequency and the interaction noise are then estimated using the analytical frequency domain model proposed by Hanson (1985). The present method for the fast prediction of CROR noise has been validated by comparison with the results of URANS simulations and noise measurements. CROR geometry UDF F7/A7 with both 8 × 8 and 11 × 9 blade counts has been considered. The flow velocity profiles extrapolated through the mixing plane agree well with the URANS results, except in the vicinity of the blade tip, where the analytical extrapolation method is not able to deal properly with the strongly 3D tip vortex flow. The comparison of the predicted interaction noise with acoustic measurements shows that the present fast RANS-informed approach is capable of estimating the directivity of the CROR noise with reasonable accuracy.
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Bertolini, Ettore, Paul Pieringer, and Wolfgang Sanz. "Large Eddy Simulation of a Transonic Linear Cascade With Synthetic Inlet Turbulence." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14461.

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Abstract The aim of this work is to predict the boundary layer transition and the heat transfer on a highly loaded transonic turbine cascade using Large Eddy Simulations (LESs) with prescribed inlet synthetic turbulence. The numerical simulations were performed for the flow in a linear turbine cascade tested at the von Karman Institute for Fluid Dynamic (MUR test case). For the numerical case, two operating conditions with two different levels of free-stream turbulence intensity are evaluated. For the lower turbulence level case (Tu = 0.8%, MUR132) a laminar inflow is used for the LES simulations whereas for the higher one (Tu = 6%, MUR237) the inlet turbulence is prescribed by using the Synthetic Eddy Method (SEM) of Jarrin. The first part of this work deals with the LES setup. The standard Smagorinsky model was used as closure model. A value of the Smagorinsky constant CS = 0.05 was chosen whereas the turbulent viscosity was reduced in the region closest to the wall by changing the definition of the Smagorinsky length scale. To handle the strong fluctuations in the flow field the cell fluxes are computed using the WENO-P scheme. In the second part, precursor RANS and LES simulations are used to set the optimal values of the SEM parameters and to guarantee the correct level of turbulence at the blade leading edge. The turbulence decay of the synthetic turbulence is compared with the one of the RANS κ–ωSST model. Finally, a comparison between experimental and numerical results is done and the ability of LES to predict the boundary layer transition and the heat transfer on the blade surface is evaluated for the two different inflow conditions.
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