LEADER 01845oam 2200421 450 001 9910703863403321 005 20150820155458.0 035 $a(CKB)5470000002435096 035 $a(OCoLC)915566072 035 $a(EXLCZ)995470000002435096 100 $a20150804j201505 ua 0 101 0 $aeng 135 $aurmn||||a|||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aEvaluation of LTPP climatic data for use in Mechanistic-Empirical Pavement Design Guide (MEPDG) calibration and other pavement analysis /$fCharles W. Schwartz [and six others] 210 1$aMcLean, VA :$cU.S. Department of Transportation, Federal Highway Administration, Research, Development, and Technology, Turner-Fairbank Highway Research Center,$dMay 2015. 215 $a1 online resource (142 unnumbered pages) $ccolor illustrations, maps 300 $aTitle from title screen (viewed on July 29, 2015). 300 $a"May 2015." 300 $a"FHWA-HRT-15-019." 320 $aIncludes bibliographical references (pages 121-126). 517 $aEvaluation of LTPP climatic data for use in Mechanistic-Empirical Pavement Design Guide 606 $aPavements$zUnited States$xAnalysis 606 $aPavements$xClimatic factors 615 0$aPavements$xAnalysis. 615 0$aPavements$xClimatic factors. 700 $aSchwartz$b Charles Warren$01403253 712 02$aTurner-Fairbank Highway Research Center, 712 02$aUnited States.$bFederal Highway Administration.$bOffice of Infrastructure Research and Development, 801 0$bGPO 801 1$bGPO 801 2$bGPO 906 $aBOOK 912 $a9910703863403321 996 $aEvaluation of LTPP climatic data for use in Mechanistic-Empirical Pavement Design Guide (MEPDG) calibration and other pavement analysis$93477613 997 $aUNINA LEADER 05143nam 22007215 450 001 9910792491103321 005 20200702215818.0 010 $a1-4757-9293-X 024 7 $a10.1007/978-1-4757-9293-5 035 $a(CKB)2660000000024840 035 $a(SSID)ssj0000930699 035 $a(PQKBManifestationID)11506927 035 $a(PQKBTitleCode)TC0000930699 035 $a(PQKBWorkID)10853480 035 $a(PQKB)11358184 035 $a(DE-He213)978-1-4757-9293-5 035 $a(MiAaPQ)EBC3086279 035 $a(PPN)238084612 035 $a(EXLCZ)992660000000024840 100 $a20130508d1995 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAsphaltenes$b[electronic resource] $eFundamentals and Applications /$fedited by Maite Subirana, Eric Y. Sheu 205 $a1st ed. 1995. 210 1$aNew York, NY :$cSpringer US :$cImprint: Springer,$d1995. 215 $a1 online resource (XII, 246 p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-306-45191-3 311 $a1-4757-9295-6 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aI. Colloidal Properties of Asphaltenes in Organic Solvents -- II. Sulfur and Nitrogen Molecular Structures in Asphaltenes and Related Materials Quantified by XANES Spectroscopy -- III. Solubility and Phase Behavior of Asphaltenes in Hydrocarbon Media -- IV. A Unified View of the Colloidal Nature of Asphaltenes -- V. The Effects of Asphaltenes on the Chemical and Physical Characteristics of Asphalt -- VI. Asphalt Emulsion for Environmental Coating and Encapsulation -- VII. Potential of Ultrasonic Generators for Use in Oil Wells and Heavy Crude Oil/Bitumen Transportation Facilities -- VIII. New Methods of Petroleum Sludge Disposal and Utilization. 330 $aAsphaltenes have traditionally been viewed as being extremely complex, thus very hard to characterize. In addition, certain fundamental properties of asphaltenes have pre­ viously been inaccessible to study by traditional macroscopic methods, further limiting understanding of asphaltenes. These limitations inhibited development of descriptions regarding the microscopic structure and solution dynamics of asphaltenes. However, a variety ofmore recent studies have implied that asphaltenes share many chemical properties with the smaller, more tractable components of crude oils. Recent measurements have indicated that asphaltene molecular weights are not as !arge as previously thought, perhaps in the range of 600 to I 000 amu. In addition, new experimental methods applied to asphaltene chemical structures have been quite revealing, yielding a broad understanding. Conse­ quently, the ability to relate chemical structure with physical and chemical properties can be developed and extended to the understanding of important commercial properties of asphal­ tenes. This book treats significant new developments in the fundamentals and applications of asphaltenes. In the first section ofthe book, new experimental methods are described that characterize asphaltene structures from the molecular to colloidallength scale. The colloidal properties are understandable in terms of asphaltene chemical structures, especially with regard to the heteroatom impact on bonding. However, quantitative measurements of the of asphaltene self-association still need to be determined. In the second section of enthalpy this book, the fundamental understanding of asphaltenes is related riirectly to asphaltene utilization. 606 $aChemical engineering 606 $aInorganic chemistry 606 $aOrganic chemistry 606 $aPolymers   606 $aMaterials science 606 $aIndustrial Chemistry/Chemical Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/C27000 606 $aInorganic Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/C16008 606 $aOrganic Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/C19007 606 $aPolymer Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/C22008 606 $aCharacterization and Evaluation of Materials$3https://scigraph.springernature.com/ontologies/product-market-codes/Z17000 615 0$aChemical engineering. 615 0$aInorganic chemistry. 615 0$aOrganic chemistry. 615 0$aPolymers  . 615 0$aMaterials science. 615 14$aIndustrial Chemistry/Chemical Engineering. 615 24$aInorganic Chemistry. 615 24$aOrganic Chemistry. 615 24$aPolymer Sciences. 615 24$aCharacterization and Evaluation of Materials. 676 $a660 702 $aSubirana$b Maite$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSheu$b Eric Y$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 02$aFine Particle Society.$bMeeting$d(24th :$f1993 :$eChicago, Ill.) 906 $aBOOK 912 $a9910792491103321 996 $aAsphaltenes$93701830 997 $aUNINA LEADER 05967nam 2200709 450 001 9910826507103321 005 20230807215414.0 010 $a90-272-6871-1 035 $a(CKB)3710000000409637 035 $a(EBL)2040186 035 $a(SSID)ssj0001482422 035 $a(PQKBManifestationID)12613766 035 $a(PQKBTitleCode)TC0001482422 035 $a(PQKBWorkID)11412144 035 $a(PQKB)11076477 035 $a(PQKBManifestationID)16038232 035 $a(PQKB)22353391 035 $a(MiAaPQ)EBC2040186 035 $a(DLC) 2015006473 035 $a(EXLCZ)993710000000409637 100 $a20150520h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aMultiple affordances of language corpora for data-driven learning /$fedited by Agnieszka Lenko-Szymanska, University of Warsaw, Alex Boulton, ATILF-CNRS / University of Lorraine 210 1$aAmsterdam, Netherlands ;$aPhiladelphia, Pennsylvania :$cJohn Benjamins Publishing Company,$d2015. 210 4$dİ2015 215 $a1 online resource (320 p.) 225 1 $aStudies in Corpus Linguistics (SCL),$x1388-0373 ;$vVolume 69 300 $aDescription based upon print version of record. 311 $a90-272-0377-6 320 $aIncludes bibliographical references and indexes. 327 $aMultiple Affordances of Language Corpora for Data-driven Learning; Editorial page; Title page; LCC data; Table of contents; Table of contents; Editors' acknowledgements; Introduction; References; Data-driven learning and language learning theories; 1. Introduction; 2. Language learning theories and learning style; 3. The noticing hypothesis and DDL; 4. Constructivist learning and DDL; 5. Vygotskyan sociocultural theories and DDL; 6. Learning styles and DDL; 7. Conclusion; Acknowledgements; References; Teaching and language corpora; 1. Introduction; 2. Beginnings; 3. What's happened? 327 $a3.1 1975-1985: From manual to computer analysis3.2 1986-1990; 3.3 1991-2000; 3.4 2001-2014; 4. Corpus applications in language teaching: The current situation; 5. Who's using language corpora in 2012: Findings from a survey; 5.1 Respondents; 5.2 Who is using corpora in language teaching, and in what contexts?; 5.3 What tools and resources are they using?; 5.4 Favourite resources; 6. What are the benefits?; 7. Conclusion and future directions?; References; Part I. Corpora for language learning; Learning phraseology from speech corpora; 1. Why spoken phraseology matters 327 $a2. Constructing a speech corpus for acquiring spoken phraseology3. Analysing a speech corpus: Some examples; 3.1 Starting from a list; 3.2 Starting from a listening experience; 3.3 One thing leads to another; 4. Implications: The role of the learner; References; Stealing a march on collocation; 1. Introduction and overview; 2. The Sketch Engine; 3. A constrained definition of collocation and its affordances; 4. Collocation Plus (C+); 5. Observing and using Topic Trails in full text; 6. Conclusion; References; Appendix 1: Text examples cited; Appendix 2: Corpora cited 327 $aA corpus and grammatical browsing system for remedial EFL learners1. Appropriate level, needs-driven corpora for the EFL classroom; 2. Developing the Grammatical Pattern Profiling System (GPPS); 2.1 Using LWP-GRC as a model for the GPPS; 2.2 GPPS functionality; 2.3 Selection of grammatical categories; 2.4 Creation of search expressions and patterns; 3. Developing the Sentence Corpus of Remedial English (SCoRE); 3.1 Defining target population proficiency levels; 3.2 Sourcing potential corpus data; 3.3 Defining sentence length; 3.4 Defining the number of sentences 327 $a3.5 Using the source corpus as a model for SCoRE3.6 Translation; 4. Pedagogical applications: Using SCoRE and the GPPS; 5. Limitations of SCoRE and the GPPS; 6. Conclusion; Acknowledgements; References; Part II. Corpora for skills development; Same task, different corpus; 1. Introduction; 2. Background to the course; 2.1 Course programme; 2.2 Course procedure; 3. Data; 3.1 Participants; 3.2 Corpus and worksheet data; 4. Corpus tools in the 'same task, different corpus' approach; 4.1 The Concordance tool; 4.2 The Word List tool; 4.3 The Collocates tool; 4.4 The Concordance Plot tool 327 $a5. Evaluation of the course 330 $aData-driven learning typically involves the use of dedicated concordancers to explore linguistic corpora, which may require significant training if the technology is not to be an obstacle for teacher and learner alike. One possibility is to begin not with corpus or concordancer, but to find parallels with what 'ordinary' users already do. This paper compares the web to a corpus, regular search engines to concordancers, and the techniques used in web searches to data-driven learning. It also examines previous studies which exploit web searches in ways not incompatible with a DDL approach. 410 0$aStudies in corpus linguistics ;$vVolume 69. 606 $aEnglish language$xStudy and teaching$xData processing 606 $aEnglish language$xDiscourse analysis$xData processing 606 $aComputational linguistics 606 $aEnglish language$xStudy and teaching$xForeign speakers$xResearch 606 $aCorpora (Linguistics) 615 0$aEnglish language$xStudy and teaching$xData processing. 615 0$aEnglish language$xDiscourse analysis$xData processing. 615 0$aComputational linguistics. 615 0$aEnglish language$xStudy and teaching$xForeign speakers$xResearch. 615 0$aCorpora (Linguistics) 676 $a420/.285 702 $aLenko-Szymanska$b Agnieszka 702 $aBoulton$b Alex 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826507103321 996 $aMultiple affordances of language corpora for data-driven learning$93927962 997 $aUNINA