64 research outputs found
On the Size Complexity of Non-Returning Context-Free PC Grammar Systems
Get PDFImproving the previously known best bound, we show that any recursively
enumerable language can be generated with a non-returning parallel
communicating (PC) grammar system having six context-free components. We also
present a non-returning universal PC grammar system generating unary languages,
that is, a system where not only the number of components, but also the number
of productions and the number of nonterminals are limited by certain constants,
and these size parameters do not depend on the generated language
Treatment of mild and moderate type-2 diabetes: open prospective trial with Hintonia latiflora extract
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Modeling of Sulfur Incorporation during Low Pressure CVD of GaAs (100) in the Ga ‐ HCl ‐ AsH3 ‐ H 2 ‐ H 2 S System
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Amino acid sequence homology between protein products of oncogenes and hormones (v- — gastrin and oxytocin, v- — secretin)
No full textЗа кадры. 1981. № 27 (2324)
No full textВсе - на ленинский субботник!Боевая программа ученых. 19 апреля - День советской науки / В. ЕпонешниковИзлучение идеи / С. СаксПолитехники - нефтехиму / С. ЕмельяноваУчимся исследованиям / Т. Лисицына, Н. СупонинаРождается СКБ / Д. Арласов"Система" действует / В. РотарьПосвященная М. А. Усову / А. МаксимовНа английском языке / Л. РыбаченкоКачество учебы и производственный эффект / А. И. ЧучалинПо-хозяйски к дому своему / М. КровяковаНикакие годы не сотрутК конкурсу стенных газетВыставка прикладного искусстваЛедоход - не только зрелище / Т. Сидоренк
Prediction of traffic accidents by using neural network
No full textThe prediction of traffic accidents in urban networks is one of the key future theme in the areas of traffic control and navigation. Early identification of the risk of a traffic accident can lead to an increase in the safety and smoothness of road transport. Neural networks belong to the expert methods for modeling of complex systems. The issue of their use in the transport sector is scientifically quite progressive. The article describes the design of prediction model based on available traffic data from town Uherské Hradiště. Traffic data was collected from many sources, e.g. junction detectors, meteorological stations or traffic accident portal. Appropriate parameters for the model were selected from the traffic data. The model was then tested on a 2-month data sample. The aim of the article is to confirm the suitability of using neural networks to predict traffic accidents
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