LEADER 00806nam a2200241 i 4500 001 991003390369707536 005 20020503191302.0 008 000908s1967 it ||| | ita 035 $ab10498576-39ule_inst 035 $aEXGIL120021$9ExL 040 $aBiblioteca Interfacoltà$bita 082 0 $a851.91 100 1 $aMajorino, Giancarlo$0449487 245 10$aLotte secondarie /$cGiancarlo Majorino 260 $aMilano :$bMondadori,$c1967 300 $a178 p. ;$c19 cm. 440 3$aLo specchio 907 $a.b10498576$b02-04-14$c27-06-02 912 $a991003390369707536 945 $aLE002 It. V H 20$g1$i2002000644190$lle002$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10575212$z27-06-02 996 $aLotte secondarie$9212435 997 $aUNISALENTO 998 $ale002$b01-01-00$cm$da $e-$fita$git $h0$i1 LEADER 05462nam 2200901Ia 450 001 9910963846103321 005 20200520144314.0 010 $a9786612320101 010 $a9781134771783 010 $a1134771789 010 $a9783906757810 010 $a3906757811 010 $a9781283641821 010 $a1283641828 010 $a9781282320109 010 $a1282320106 010 $a9781134771790 010 $a1134771797 010 $a9780203196953 010 $a0203196953 024 7 $a10.4324/9780203196953 035 $a(CKB)1000000000251242 035 $a(EBL)179698 035 $a(OCoLC)62722412 035 $a(SSID)ssj0000071090 035 $a(PQKBManifestationID)11109722 035 $a(PQKBTitleCode)TC0000071090 035 $a(PQKBWorkID)10071556 035 $a(PQKB)10953785 035 $a(Au-PeEL)EBL179698 035 $a(CaPaEBR)ebr10057601 035 $a(CaONFJC)MIL232010 035 $a(OCoLC)900283722 035 $a(OCoLC)697715646 035 $a(OCoLC-P)697715646 035 $a(FlBoTFG)9780203196953 035 $a(PPN)18729187X 035 $a(FR-PaCSA)41000877 035 $a(MiAaPQ)EBC179698 035 $a(FRCYB41000877)41000877 035 $a(EXLCZ)991000000000251242 100 $a20080604d1970 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aJonathan Swift /$fedited by Kathleen Williams 205 $a1st ed. 210 $aLondon $cRoutledge & K. Paul$d1970 215 $a1 online resource (359 p.) 225 1 $aThe critical heritage series 300 $aDescription based upon print version of record. 311 08$a9780415568869 311 08$a0415568862 311 08$a9780415139083 311 08$a0415139082 320 $aIncludes bibliography and index. 327 $aCover; Jonathan Swift: The Critical Heritage; Copyright; General Editor's Preface; Contents; Introduction; Note on the Text; 1. Dr. William King on a Tale of a Tub 1704; 2. Francis Atterbury on a Tale of a Tub 1704; 3. William Wotton on a Tale of a Tub 1705; 4. Richard Steele on a Project for the Advancement of Religion 1709; 5. John Dennis on the Examiner 1712; 6. The Aim of a Tale of a Tub 1714; 7. Sir Richard Blackmore on a Tale of a Tub 1716; 8. A Translator's Opinions of a Tale of a Tub 1721; 9. A Swiss View of a Tale of a Tub and the Battle of the Books 1721 327 $a10. The Reception of Gulliver's Travels 172611. Lady Mary Wortlby Montagu on Gulliver's Travels 1726; 12. An Anonymous Opinion of Gulliver's Travels 1726; 13. William Warburton on Swift and Human Nature 1727; 14. Voltaire on Swift 1727, 1734, 1756, 1767, 1777; 15. Abbe Desfontainesand Gulliver's Travels 1727,1730,1787; 16. Jonathan Smedley on Gulliver's Travels 1728; 17. Swift as Political Dictator 1728; 18. Anonymous Criticisms of Houyhnhnmland 1735; 19. George Faulkner on Swift's Poetry 1735; 20. The Duchess of Marlborough on Swift 1736 327 $a21 Frangois Cartaud De La Villate on a Tale of a Tub 173622. Samuel Richardson on Swift 1740, 1748, 1752, 1754; 23. Paradis De Moncrif on Gulliver's Travels 1743; 24. Henry Fielding on Swift 1745, 1751, 1752; 25. David Hume on Swift 1751, 1752, 1768; 26. Lord Orrery on Swift 1752; 27. Patrick Delany on Swift 1754; 28. Deaneswifton Gulliver's Travels and on Swift as a Poet 1755; 29. John Hawkes Worth on Swift 1755; 30. W. H. Dilworth on Swift 1758; 31. Edward Young on Gulliver's Travels 1759; 32. George Lord Lyttelton on Swift 1760; 33. A French Reissue of Gulliver's Travels 1762 327 $a34. Oliver Goldsmith on Swift 176435. Ralph Griffiths on Swift's 'cause' 1765; 36. Horacb Walpole and His Circle on Swift 1771, 1780; 37. Lord Monboddo on Gulliver's Travels 1776; 38. James Beattie on Gulliver's Travels, a Tale of a Tub, and the Day of Judgment 1776, 1783; 39. A French Comment on a Modest Proposal 1777; 40. Dr. Johnson on Swift 1779, 1785, 1791; 41. Samuel Bad Cock on Swift's 'true Wit' 1779; 42 James Harris on Gulliver's Travels 1781; 43 Joseph Warton on Swift's Descriptions 1782; 44. Swift's Characteristics as a Writer 1782; 45. Hugh Blair on Swift's Style 1783 327 $a46. Thomas Sheridan on Swift 178447. Incidental Comments on Gulliver's Travels 1789; 48. George-monck Berkeley on Swift 1789; 49. Thomas Ogle on Swift and Misanthropy 1790; 50. Swift as Satirist and Poet 1790; 51. William Godwin on Swift's Style 1797; 52. John Nichols on Swift 1801, 1828; 53. Alexander Chalmers on Swift's Style and Character 1803; 54. Swiftiana 1804; 55. John Aikin on Swift's Poetry 1804,1820; 56. Richard Payne Knight on the Plausibility of Gulliver's Travels 1805; 57. Nathan Drake on Swift 1805; 58. John Dunlop on the Background of Gulliver's Travels 1814 327 $a59. Sir Walter Scott on Swift 1814 330 8 $aThe Critical Heritage gathers together a large body of critical sources on major figures in literature. Each volume presents contemporary responses to a writer's work, enabling student and researcher to read the material themselves. 410 0$aCritical heritage series. 606 $aEnglish literature 615 0$aEnglish literature. 676 $a823.5 676 $a827/.5 701 $aWilliams$b Kathleen$0193676 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910963846103321 996 $aJonathan Swift$9165724 997 $aUNINA LEADER 05704nam 22006375 450 001 9910842292703321 005 20250731130355.0 010 $a3-031-52645-7 024 7 $a10.1007/978-3-031-52645-9 035 $a(CKB)30597583400041 035 $a(MiAaPQ)EBC31200863 035 $a(Au-PeEL)EBL31200863 035 $a(DE-He213)978-3-031-52645-9 035 $a(EXLCZ)9930597583400041 100 $a20240228d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSmart Big Data in Digital Agriculture Applications $eAcquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence /$fby Haoyu Niu, YangQuan Chen 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (243 pages) 225 1 $aAgriculture Automation and Control,$x2731-3506 311 08$a3-031-52644-9 320 $aIncludes bibliographical references and index. 327 $aPart I Why Big Data Is Not Smart Yet? -- 1. Introduction -- 2. Why Do Big Data and Machine Learning Entail the Fractional Dynamics? -- Part II Smart Big Data Acquisition Platforms -- 3. Small Unmanned Aerial Vehicles (UAVs) and Remote Sensing Payloads -- 4. The Edge-AI Sensors and Internet of Living Things (IoLT) -- 5. The Unmanned Ground Vehicles (UGVs) for Digital Agriculture -- Part III Advanced Big Data Analytics, Plant Physiology-informed Machine Learning, and Fractional-order Thinking -- 6. Fundamentals of Big Data, Machine Learning, and Computer VisionWorkflow -- 7. A Low-cost Proximate Sensing Method for Early Detection of Nematodes inWalnut Using Machine Learning Algorithms -- 8. Tree-level Evapotranspiration Estimation of Pomegranate Trees Using Lysimeter and UAV Multispectral Imagery -- 9. Individual Tree-level Water Status Inference Using High-resolution UAV Thermal Imagery and Complexity-informed Machine Learning -- 10. Scale-aware Pomegranate Yield Prediction Using UAV Imagery and Machine Learning -- Part IV Towards Smart Big Data in Digital Agriculture -- 11. Intelligent Bugs Mapping and Wiping (iBMW): An Affordable Robot-Driven Robot for Farmers -- 12. A Non-invasive Stem Water Potential Monitoring Method Using Proximate Sensor and Machine Learning Classification Algorithms -- 13. A Low-cost Soil Moisture Monitoring Method by Using Walabot and Machine Learning Algorithms -- 14. Conclusions and Future Research. 330 $aIn the dynamic realm of digital agriculture, the integration of big data acquisition platforms has sparked both curiosity and enthusiasm among researchers and agricultural practitioners. This book embarks on a journey to explore the intersection of artificial intelligence and agriculture, focusing on small-unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), edge-AI sensors and the profound impact they have on digital agriculture, particularly in the context of heterogeneous crops, such as walnuts, pomegranates, cotton, etc. For example, lightweight sensors mounted on UAVs, including multispectral and thermal infrared cameras, serve as invaluable tools for capturing high-resolution images. Their enhanced temporal and spatial resolutions, coupled with cost effectiveness and near-real-time data acquisition, position UAVs as an optimal platform for mapping and monitoring crop variability in vast expanses. This combination of data acquisition platforms and advanced analytics generates substantial datasets, necessitating a deep understanding of fractional-order thinking, which is imperative due to the inherent ?complexity? and consequent variability within the agricultural process. Much optimism is vested in the field of artificial intelligence, such as machine learning (ML) and computer vision (CV), where the efficient utilization of big data to make it ?smart? is of paramount importance in agricultural research. Central to this learning process lies the intricate relationship between plant physiology and optimization methods. The key to the learning process is the plant physiology and optimization method. Crafting an efficient optimization method raises three pivotal questions: 1.) What represents the best approach to optimization? 2.) How can we achieve a more optimal optimization? 3.) Is it possible to demand ?more optimal machine learning,? exemplified by deep learning, while minimizing the need for extensive labeled data for digital agriculture? This book details the foundations of the plant physiology-informed machine learning (PPIML) and the principle of tail matching (POTM) framework. It is the 9th title of the "Agriculture Automation and Control" book series published by Springer. 410 0$aAgriculture Automation and Control,$x2731-3506 606 $aAgriculture 606 $aPlant physiology 606 $aQuantitative research 606 $aEngineering design 606 $aAgriculture 606 $aPlant Physiology 606 $aData Analysis and Big Data 606 $aEngineering Design 615 0$aAgriculture. 615 0$aPlant physiology. 615 0$aQuantitative research. 615 0$aEngineering design. 615 14$aAgriculture. 615 24$aPlant Physiology. 615 24$aData Analysis and Big Data. 615 24$aEngineering Design. 676 $a338.10285 700 $aNiu$b Haoyu$01263980 702 $aChen$b YangQuan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910842292703321 996 $aSmart Big Data in Digital Agriculture Applications$94237393 997 $aUNINA