00934nam a2200277 i 450099100168421970753620020503124508.0000419s1976 it ||| | ita b10257251-39ule_instLE01282800ExLDip.to LingueitaDreiser, Theodore131608Il titano /Theodore Dreiser ; traduzione di Bruno Fonzi ; nota introduttiva di Guido Carboni1 ed.Torino :Einaudi,c1976xxii,561 p. ;20 cm.Gli struzzi ;98Fonzi, Bruno Carboni, GuidoThe Titan.b1025725121-09-0627-06-02991001684219707536LE012 818.52 DRE12012000017832le012-E0.00-l- 01010.i1030778327-06-02Titano205781UNISALENTOle01201-01-00ma -itait 3103858nam 22005655 450 991085537400332120250807143229.0981-9708-30-310.1007/978-981-97-0830-7(MiAaPQ)EBC31318904(Au-PeEL)EBL31318904(CKB)31889897200041(DE-He213)978-981-97-0830-7(EXLCZ)993188989720004120240502d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierGenetic Studies in Model Organisms From Classical to Modern Genetics /by Kwang-Wook Choi1st ed. 2024.Singapore :Springer Nature Singapore :Imprint: Springer,2024.1 online resource (475 pages)KAIST Research Series,2214-255X981-9708-29-X Part I. Mendel, Genetic Models, and Mutations -- Chapter 1. Mendel's Principles of Inheritance and Chromosome Theory -- Chapter 2. Genetic Model Organisms -- Chapter 3. Recombination and Chromosomal Rearrangements -- Chapter 4. Loss-of-Function Mutagenesis in Forward Genetics -- Part II. Transposons, Transgenesis, Gene Editing, and Genetic Mosaics -- Chapter 5. Non-Mendelian Genetics: Transposable elements (I) -- Chapter 6. Transposable Elements II: Insertional Mutagenesis -- Chapter 7. Gain-of-Function and Gene Silencing -- Chapter 8. Genetic Interaction, Epistasis, Modifiers -- Chapter 9. Targeted Mutagenesis -- Chapter 10. Transgenesis in Clonal Analysis -- Part III. Epigenetics, Genome Organization, and piRNA -- Chapter 11. Epigenetic Control of Gene Expression -- Chapter 12. Chromosomal Interaction in Chromatin Organization -- Chapter 13. Piwi and piRNA in Germline and Epigenetic Regulation -- Part IV. Applications of Genetic Analysis -- Chapter 14. Genetic Control of Dosage Compensation -- Chapter 15. Genetics of Programmed Cell Death -- Chapter 16. Genetics of Growth Control -- Chapter 17. Genetic Studies on Behavior.This book reviews key advances and new fundamentals in genetics. The increasing importance of genetic approaches in diverse areas of biology and medical sciences constantly requires in-depth information on genetic discoveries and research strategies for advanced graduate-level students as well as current researchers. This book focuses on genetic studies of various animal model systems and their major contributions to establishing modern genetics. Information covered in this book is mostly based on original research papers that extend from classical to modern genetics and applications. The contents are organized into four parts. Part I introduces fundamental concepts and experimental strategies in classical genetics. Part II discusses molecular genetics with transposons, transgenesis, clonal analysis, and gene editing technologies. Part III emphasizes epigenetic regulation of genome organization and gene expression. Part IV integrates earlier parts with landmark genetic studies on non-coding RNAs in dosage compensation, programmed cell death, growth control related to cancer, and behavioral neurobiology. .KAIST Research Series,2214-255XBiologyGeneticsBiological SciencesGeneticsGenetics and GenomicsGenotypeBiology.Genetics.Biological Sciences.Genetics.Genetics and Genomics.Genotype.575.1Choi Kwang-Wook1737847MiAaPQMiAaPQMiAaPQBOOK9910855374003321Genetic Studies in Model Organisms4159749UNINA05190nam 22008175 450 991099967910332120251202150009.09783031843044303184304510.1007/978-3-031-84304-4(CKB)38485083600041(DE-He213)978-3-031-84304-4(MiAaPQ)EBC32011951(Au-PeEL)EBL32011951(EXLCZ)993848508360004120250417d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAdvanced Portfolio Optimization A Cutting-edge Quantitative Approach /by Dany Cajas1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (XV, 503 p. 216 illus., 186 illus. in color.)9783031843037 3031843037 Chapter 1 Introduction -- Chapter 2 Why use Python? -- Part I Parameter Estimation -- Chapter 3 Sample Based Methods -- Chapter 4 Risk Factors Models -- Chapter 5 Black Litterman Models -- Chapter 7 Convex Risk Measures -- Chapter 8 Return-Risk Trade-Off Optimization -- Chapter 9 Real Features Constraints -- Chapter 10 Risk Parity Optimization -- Chapter 11 Robust Optimization -- Part III Machine Learning Portfolio Optimization -- Chapter 12 Hierarchical Clustering Portfolios -- Chapter 13 Graph Theory Based Portfolios -- Part IV Backtesting -- Chapter 14 Generation of Synthetic Data -- Chapter 15 Backtesting Process -- Part V Appendix -- Chapter A Linear Algebra -- Chapter B Convex Optimization -- Chapter C Mixed Integer Programming.This book is an innovative and comprehensive guide that provides readers with the knowledge about the latest trends, models and algorithms used to build investment portfolios and the practical skills necessary to apply them in their own investment strategies. It integrates latest advanced quantitative techniques into portfolio optimization, raises questions about which alternatives to modern portfolio theory exists and how they can be applied to improve the performance of multi-asset portfolios. It provides answers and solutions by offering practical tools and code samples that enable readers to implement advanced portfolio optimization techniques and make informed investment decisions. Portfolio Optimization goes beyond traditional portfolio theory (Quadratic Programming), incorporating last advances in convex optimization techniques and cutting-edge machine learning algorithms. It extensively addresses risk management and uncertainty quantification, teaching readers how to measure and minimize various forms of risk in their portfolios. This book goes beyond traditional back testing methodologies based on historical data for investment portfolios, incorporating tools to create synthetic datasets and robust methodologies to identify better investment strategies considering real aspects like transaction costs. The author provides several methodologies for estimating the input parameters of investment portfolio optimization models, from classical statistics to more advanced models, such as graph-based estimators and Bayesian estimators, provide a deep understanding of advanced convex optimization models and machine learning algorithms for building investment portfolios and the necessary tools to design the back testing of investment portfolios using several methodologies based on historical and synthetic datasets that allow readers identify the better investment strategies.StatisticsData miningMachine learningValuationFinancial risk managementStatistics in Business, Management, Economics, Finance, InsuranceData Mining and Knowledge DiscoveryMachine LearningInvestment AppraisalRisk ManagementEstadísticathubMineria de dadesthubAprenentatge automàticthubValoracióthubGestió del riscthubEstadística econòmicathubLlibres electrònicsthubStatistics.Data mining.Machine learning.Valuation.Financial risk management.Statistics in Business, Management, Economics, Finance, Insurance.Data Mining and Knowledge Discovery.Machine Learning.Investment Appraisal.Risk Management.EstadísticaMineria de dadesAprenentatge automàticValoracióGestió del riscEstadística econòmica300.727Cajas Danyauthttp://id.loc.gov/vocabulary/relators/aut1817231MiAaPQMiAaPQMiAaPQBOOK9910999679103321Advanced Portfolio Optimization4374791UNINA