LEADER 02418nam 2200577Ia 450 001 9910454866903321 005 20200520144314.0 010 $a1-56976-261-9 035 $a(CKB)1000000000773354 035 $a(EBL)445254 035 $a(OCoLC)429642804 035 $a(SSID)ssj0000183443 035 $a(PQKBManifestationID)11170894 035 $a(PQKBTitleCode)TC0000183443 035 $a(PQKBWorkID)10195972 035 $a(PQKB)11288742 035 $a(MiAaPQ)EBC445254 035 $a(Au-PeEL)EBL445254 035 $a(CaPaEBR)ebr10308985 035 $a(CaONFJC)MIL536456 035 $a(EXLCZ)991000000000773354 100 $a20081015d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aInvisible China$b[electronic resource] $ea journey through ethnic borderlands /$fColin Legerton and Jacob Rawson 205 $a1st ed. 210 $aChicago $cChicago Review Press$dc2009 215 $a1 online resource (256 p.) 300 $aDescription based upon print version of record. 311 $a1-306-05205-X 311 $a1-55652-814-0 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Copyright; Contents; Acknowledgments; Authors' Note; Introduction; I The Northeast; II The Southwest; III The Northwest; IV The East; Afterword; Selected Suggested Reading; Index 330 $aTraveling more than 14,000 miles by bus and train to the farthest reaches of China, the authors of this narrative explore the minority peoples who dwell there, talking to farmers in their fields, monks in their monasteries, fishermen on their skiffs, and herders on the steppe. Closely observing daily life in these remote regions, they document the many lifestyles and adventures of the Chinese natives?they visit an old Catholic fisherman at a church that has been without a priest for 40 years; they hike around high-altitude Lugu Lake to farm with the matriarchal Mos 606 $aMinorities$zChina 607 $aChina$xDescription and travel 608 $aElectronic books. 615 0$aMinorities 676 $a305.800951 700 $aLegerton$b Colin$0948079 701 $aRawson$b Jacob$0948080 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910454866903321 996 $aInvisible China$92142951 997 $aUNINA LEADER 00997nam 2200289Ia 450 001 996387939303316 005 20221108074940.0 035 $a(CKB)1000000000625532 035 $a(EEBO)2240936702 035 $a(OCoLC)11988001 035 $a(EXLCZ)991000000000625532 100 $a19850501d1660 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 14$aThe late news, or, Message from Bruxels unmasked$b[electronic resource] $eand His Majesty vindicated from the base calumny and scandal therein fixed on him 210 $aLondon $cPrinted for Richard Lowndes ...$d1660 215 $a[2], 6 p 300 $aAttributed to Evelyn by Wing and NUC pre-1956 imprints. 300 $aReproduction of original in the Huntington Library. 330 $aeebo-0113 700 $aEvelyn$b John$f1620-1706.$0168010 801 1$bEAE 801 2$bWaOLN 906 $aBOOK 912 $a996387939303316 996 $aThe late news, or, Message from Bruxels unmasked$92421029 997 $aUNISA LEADER 03389nam 2200637z- 450 001 9910557351203321 005 20220111 035 $a(CKB)5400000000042378 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76497 035 $a(oapen)doab76497 035 $a(EXLCZ)995400000000042378 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNumerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (110 p.) 311 08$a3-0365-0956-9 311 08$a3-0365-0957-7 330 $aThe book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured meshes are introduced to capture the important dynamics that operate over a range of length scales. Deep learning techniques enable rapid and accurate modelling simulations and pave the way towards both real-time forecasting and overall optimisation control over time, thus improving profitability and managing risk. The use of data assimilation techniques incorporates information from experiments and observations to reduce uncertainties in predictions and improve predictive accuracy. Targeted observation approaches can be used for identifying when, where, and what types of observations would provide the greatest improvement to specific model forecasts at a future time. Such targeted observations are important as they will allow the most effective use of available monitoring resources. The combination of deep learning and data assimilation enables a rapid and accurate response in emergencies. The technologies discussed here can be also used to determine the sensitivity of outputs to various operational conditions in engineering and management, thus providing reliable information to both the public and policy-makers 606 $aResearch & information: general$2bicssc 610 $a4D-Var 610 $adata assimilation 610 $adeep learning 610 $aensemble spread 610 $aexposure time 610 $afinite volume 610 $ahyper-tidal estuary 610 $ainitial ensemble 610 $aLETKF 610 $amartinez boundary salinity generator 610 $aMEOF 610 $an/a 610 $aNorth Sea 610 $anumerical modelling 610 $aobservation strategies 610 $aocean Double Gyre 610 $aocean forecasting systems 610 $aocean models 610 $aresidence time 610 $aROMS 610 $aSacramento-San Joaquin Delta 610 $asalinity 610 $asingular value decomposition 610 $atransport time scale 610 $aunstructured meshes 615 7$aResearch & information: general 700 $aFang$b Fangxin$4edt$01291787 702 $aFang$b Fangxin$4oth 906 $aBOOK 912 $a9910557351203321 996 $aNumerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering$93021921 997 $aUNINA