LEADER 02086nam 2200577 450 001 9910818809003321 005 20170918215726.0 010 $a0-8218-9953-8 035 $a(CKB)3360000000464066 035 $a(EBL)3113526 035 $a(SSID)ssj0000973435 035 $a(PQKBManifestationID)11616162 035 $a(PQKBTitleCode)TC0000973435 035 $a(PQKBWorkID)10960120 035 $a(PQKB)10545318 035 $a(MiAaPQ)EBC3113526 035 $a(RPAM)2678565 035 $a(PPN)195410351 035 $a(EXLCZ)993360000000464066 100 $a20740823d1974 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOn the general Rogers-Ramanujan theorem /$f[by] George E. Andrews 210 1$aProvidence :$cAmerican Mathematical Society,$d1974. 215 $a1 online resource (89 p.) 225 1 $aMemoirs of the American Mathematical Society ;$vnumber 152 300 $aDescription based upon print version of record. 311 $a0-8218-1852-X 320 $aBibliography: pages 86. 327 $a""Abstract""; ""1. Introduction""; ""2. General comments""; ""3. Outline of proof of Theorem 6.3""; ""4. The q-difference equations""; ""5. The auxiliary partition functions""; ""6. The general theorem for a a?? I?»""; ""7. Further auxiliary partition functions""; ""8. The general theorem""; ""9. Conclusion""; ""References"" 410 0$aMemoirs of the American Mathematical Society ;$vno. 152. 606 $aNumber theory 606 $aPartitions (Mathematics) 606 $aHypergeometric functions 606 $aRogers-Ramanujan theorem 615 0$aNumber theory. 615 0$aPartitions (Mathematics) 615 0$aHypergeometric functions. 615 0$aRogers-Ramanujan theorem. 676 $a512/.73 700 $aAndrews$b George E.$f1938-$040860 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910818809003321 996 $aOn the general Rogers-Ramanujan theorem$93941914 997 $aUNINA LEADER 03809nam 22007215 450 001 9910574072603321 005 20260130165453.0 010 $a3-030-95281-9 024 7 $a10.1007/978-3-030-95281-5 035 $a(CKB)5600000000460481 035 $a(MiAaPQ)EBC6996388 035 $a(Au-PeEL)EBL6996388 035 $a(BIP)84309340 035 $a(BIP)82668304 035 $a(DE-He213)978-3-030-95281-5 035 $a(EXLCZ)995600000000460481 100 $a20220520d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEpidemic Analytics for Decision Supports in COVID19 Crisis /$fedited by Joao Alexandre Lobo Marques, Simon James Fong 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (161 pages) 311 08$a3-030-95280-0 320 $aIncludes bibliographical references. 327 $aChapter 1. Research and Technology Development Achievements During the COVID-19 Pandemic ? An Overview -- Chapter 2. Analysis of the COVID-19 Pandemic Behavior based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models -- Chapter 3. The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak -- Chapter 4. Probabilistic Forecasting Model for the COVID-19 Pandemic based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System -- Chapter 5. The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID-19 Pandemic -- Chapter 6. A Quantum Field formulation for a pandemic propagation. 330 $aCovid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future. 606 $aIndustrial Management 606 $aEpidemiology 606 $aOperations research 606 $aData mining 606 $aMedicine, Preventive 606 $aHealth promotion 606 $aIndustrial Management 606 $aEpidemiology 606 $aOperations Research and Decision Theory 606 $aData Mining and Knowledge Discovery 606 $aHealth Promotion and Disease Prevention 615 0$aIndustrial Management. 615 0$aEpidemiology. 615 0$aOperations research. 615 0$aData mining. 615 0$aMedicine, Preventive. 615 0$aHealth promotion. 615 14$aIndustrial Management. 615 24$aEpidemiology. 615 24$aOperations Research and Decision Theory. 615 24$aData Mining and Knowledge Discovery. 615 24$aHealth Promotion and Disease Prevention. 676 $a614.40285 676 $a614.40285 702 $aMarques$b Joao Alexandre Lobo 702 $aFong$b Simon James 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910574072603321 996 $aEpidemic analytics for decision supports in COVID19 crisis$92987375 997 $aUNINA