{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":58,"n_cols":5,"rows_kept":58,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[3,2,3,3],[16,2,16,3],[17,2,17,3],[18,2,18,3],[19,2,19,3],[21,2,21,3],[22,2,22,3],[23,2,23,3],[27,2,27,3],[35,2,35,3],[35,4,35,5],[36,2,36,3],[36,4,36,5],[37,2,37,3],[37,4,37,5],[38,2,38,3],[39,2,39,3],[40,2,40,3],[41,2,41,3],[42,2,42,3],[43,2,43,3],[44,2,44,3],[51,2,51,3],[52,2,52,3],[55,2,55,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910963846103321"]
[null,null,"Titolo",null,"Jonathan Swift / / edited by Kathleen Williams"]
[null,null,"Pubbl/distr/stampa",null,"London, : Routledge & K. Paul, 1970"]
[null,null,"ISBN",null,"9786612320101"]
[{"$nulls":4},"9781134771783"]
[{"$nulls":4},"1134771789"]
[{"$nulls":4},"9783906757810"]
[{"$nulls":4},"3906757811"]
[{"$nulls":4},"9781283641821"]
[{"$nulls":4},"1283641828"]
[{"$nulls":4},"9781282320109"]
[{"$nulls":4},"1282320106"]
[{"$nulls":4},"9781134771790"]
[{"$nulls":4},"1134771797"]
[{"$nulls":4},"9780203196953"]
[{"$nulls":4},"0203196953"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (359 p.)"]
[null,null,"Collana",null,"The critical heritage series"]
[null,null,"Disciplina",null,"823.5"]
[{"$nulls":4},"827/.5"]
[null,null,"Soggetti",null,"English literature"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliography and index."]
[null,null,"Nota di contenuto",null,"Cover; Jonathan Swift: The Critical Heritage; Copyright; General Editor's Preface; Contents; Introduction; Note on the Text; 1. Dr. William King on a Tale of a Tub 1704; 2. Francis Atterbury on a Tale of a Tub 1704; 3. William Wotton on a Tale of a Tub 1705; 4. Richard Steele on a Project for the Advancement of Religion 1709; 5. John Dennis on the Examiner 1712; 6. The Aim of a Tale of a Tub 1714; 7. Sir Richard Blackmore on a Tale of a Tub 1716; 8. A Translator's Opinions of a Tale of a Tub 1721; 9. A Swiss View of a Tale of a Tub and the Battle of the Books 1721"]
[{"$nulls":4},"10. 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 "]
[{"$nulls":4},"Nature 1727; 14. Voltaire on Swift 1727, 1734, 1756, 1767, 1777; 15. Abbe Desfontainesand Gulliver's Travels 1727,1730,1787; 16. Jonathan Smedley on Gulliver's Travels 1728; 17. Swift as Political Dictator 1728; 18. Anonymous Criticisms of Houyhnhnmland 1735; 19. George Faulkner on Swift's Poetry 1735; 20. The Duchess of Marlborough on Swift 1736"]
[{"$nulls":4},"21 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"]
[{"$nulls":4},"34. Oliver Goldsmith on Swift 176435. Ralph Griffiths on Swift's 'cause' 1765; 36. Horacb Walpole and His Circle on Swift 1771, 1780; 37. Lord Monboddo on Gulliver's Travels 1776; 38. James Beattie on Gulliver's Travels, a Tale of a Tub, and the Day of Judgment 1776, 1783; 39. A French Comment on a Modest Proposal 1777; 40. Dr. Johnson on Swift 1779, 1785, 1791; 41. Samuel Bad Cock on Swift's 'true Wit' 1779; 42 James Harris on Gulliver's Travels 1781; 43 Joseph Warton on Swift's Descriptions 1782; 44. Swift's Characteristics as a Writer 1782; 45. Hugh Blair on Swift's Style 1783"]
[{"$nulls":4},"46. 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"]
[{"$nulls":4},"59. Sir Walter Scott on Swift 1814"]
[null,null,null,"Sommario/riassunto","The Critical Heritage gathers together a large body of critical sources on major figures in literature. Each volume presents contemporary responses to a writer's work, enabling student and researcher to read the material themselves."]
[null,"2.","Record Nr.",null,"UNINA9910842292703321"]
[null,null,"Autore",null,"Niu Haoyu"]
[null,null,"Titolo",null,"Smart Big Data in Digital Agriculture Applications : Acquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence / / by Haoyu Niu, YangQuan Chen"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-52645-7"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (243 pages)"]
[null,null,"Collana",null,"Agriculture Automation and Control, , 2731-3506"]
[null,null,"Disciplina",null,"338.10285"]
[null,null,"Soggetti",null,"Agriculture"]
[{"$nulls":4},"Plant physiology"]
[{"$nulls":4},"Quantitative research"]
[{"$nulls":4},"Engineering design"]
[{"$nulls":4},"Plant Physiology"]
[{"$nulls":4},"Data Analysis and Big Data"]
[{"$nulls":4},"Engineering Design"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Part I Why Big Data Is Not Smart Yet? -- 1. Introduction -- 2. Why Do Big Data and Machine Learning Entail the Fractional Dynamics? -- Part II Smart Big Data Acquisition Platforms -- 3. Small Unmanned Aerial Vehicles (UAVs) and Remote Sensing Payloads -- 4. The Edge-AI Sensors and Internet of Living Things (IoLT) -- 5. The Unmanned Ground Vehicles (UGVs) for Digital Agriculture -- Part III Advanced Big Data Analytics, Plant Physiology-informed Machine Learning, and Fractional-order Thinking -- 6. Fundamentals of Big Data, Machine Learning, and Computer VisionWorkflow -- 7. A Low-cost Proximate Sensing Method for Early Detection of Nematodes inWalnut Using Machine Learning Algorithms -- 8. Tree-level Evapotranspiration Estimation of Pomegranate Trees Using Lysimeter and UAV Multispectral Imagery -- 9. Individual Tree-level Water Status Inference Using High-resolution UAV Thermal Imagery and Complexity-informed Machine Learning -- 10. Scale-aware Pomegranate Yield Prediction Using UAV Imagery and Machine Learning -- Part IV Towards Smart Big Data in Digital Agriculture -- 11. Intelligent Bugs Mapping and Wiping (iBMW): An Affordable Robot-Driven Robot for Farmers -- 12. A Non-invasive Stem Water Potential Monitoring Method Using Proximate "]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","In 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."]