00757nam0-22002651i-450-99000360310040332120060320140620.0000360310FED01000360310(Aleph)000360310FED0100036031020030910d1939----km-y0itay50------baitay-------001yyConsiderazioni sul commercio di Milano.Pietro VerriMilanos.e.1939Verri,Pietro<1728-1797>292794ITUNINARICAUNIMARCBK990003603100403321SE 09.01.020-15682DECSESE 09.01.021-14929DECSEDECSEConsiderazioni sul commercio di Milano495075UNINA01023cam0 2200277 450 E60020002603020210412080459.020070322d1971 |||||ita|0103 baitaIT<<La >>Rivoluzione industrialesaggio sulle origini della grande industria moderna in InghilterraPaul Mantouxprefazione di Giorgio MoriRomaEditori riuniti1971570 p. 22 cmBiblioteca di storia34001LAEC000152002001 *Biblioteca di storia34Mantoux, PaulA60020004124807049033Mori, GiorgioA600200039019070ITUNISOB20210412RICAUNISOBUNISOB900|Coll|2|K16839E600200026030M 102 Monografia moderna SBNM900|Coll|2|K000017Si16839acquistopregresso1UNISOBUNISOB20070322095534.020210412080459.0rovitoRivoluzione industriale206091UNISOB01070nam a22002411i 4500991002686119707536150225m19340000it / /00 ita b14216619-39ule_instDip.to Studi UmanisticiitaMartini, Giuseppe191765Catalogo della libreria di Giuseppe Martini :compilato dal possessore da servire come saggio per una nuova bibliografia di storia e letteratura italiana ... /prefazione del prof. Achille PellizzariMilano :U. Hoepli,1934-v. I :ill. (facs.) ;30 cm.Bibliografia: v. I, p. XIX-XXVII.pt. 1. Incunabuli.IncunabuliBibliografiaItaliaPellizzari, Achille .b1421661925-02-1525-02-15991002686119707536LE008 FL.M. III F 7 12008000087579le008-E0.00-no 00000.i1565930625-02-15Catalogo della libreria di Giuseppe Martini257361UNISALENTOle00825-02-15ma itait 0001522nam a2200349 i 4500991003956209707536081022s1993 nyua b 101 0 eng d0874700531b13777269-39ule_instDi.S.Te.B.A.englccopycatSociety of General Physiologists.Symposium<46th ;1992 ;Woods Hole, Mass.>324327Molecular biology and function of carrier proteins /edited by Luis Reuss, John M. Russell, Michael L. JenningsNew York :Rockefeller University Press,1993vii, 324 p. :ill. ;26 cmSociety of General Physiologists series ;48"Marine Biological Laboratory, Woods Hole Massachusetts, 10-13 September 1992"Includes bibliographical references and indexCarrier proteinsCongressesMolecular biologyCongressesBiological TransportCongressesCarrier ProteinsphysiologyCongressesMembrane ProteinsphysiologyCongressesReuss, LuisRussell, John M.Jennings, Michael L..b1377726928-01-1422-10-08991003956209707536LE003 571 SGP01.01 48 (1993)12003000083323le003gE7.00-l- 00000.i1486225622-10-08Molecular biology and function of carrier proteins1228107UNISALENTOle00322-10-08ma -engnyu0011189nam 2200541 450 99646673280331620231110224603.03-030-80542-5(MiAaPQ)EBC6875874(Au-PeEL)EBL6875874(CKB)21022420300041(PPN)269154663(EXLCZ)992102242030004120220917d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdvances in uncertainty quantification and optimization under uncertainty with aerospace applications proceedings of the 2020 UQOP international conference /edited by Massimiliano Vasile and Domenico QuagliarellaCham, Switzerland :Springer,[2022]©20221 online resource (448 pages)Space Technology Proceedings ;v.8Print version: Vasile, Massimiliano Advances in Uncertainty Quantification and Optimization under Uncertainty with Aerospace Applications Cham : Springer International Publishing AG,c2022 9783030805418 Includes bibliographical references and index.Intro -- Preface -- Contents -- Part I Applications of Uncertainty in Aerospace &amp -- Engineering (ENG) -- From Uncertainty Quantification to Shape Optimization: Cross-Fertilization of Methods for Dimensionality Reduction -- 1 Introduction -- 2 Design-Space Dimensionality Reduction in Shape Optimization -- 2.1 Geometry-Based Formulation -- 2.2 Physics-Informed Formulation -- 3 Example Application -- 4 Concluding Remarks -- References -- Cloud Uncertainty Quantification for Runback Ice Formations in Anti-Ice Electro-Thermal Ice Protection Systems -- Nomenclature -- 1 Introduction -- 2 Modelling of an AI-ETIPS -- 2.1 Computational Model -- 2.2 Case of Study -- 3 Cloud Uncertainty Characterization -- 4 Uncertainty Propagation Methodologies -- 4.1 Monte Carlo Sampling Methods -- 4.2 Generalized Polynomial Chaos Expansion -- 5 Numerical Results -- 6 Concluding Remarks -- References -- Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty Using Far-Field Drag Approximation -- 1 Introduction -- 2 Multi-fidelity Gaussian Process Regression -- 3 Aerodynamic Computational Chain -- 4 Far-Field Drag Coefficient Calculation -- 5 Deterministic Design Optimisation Problem -- 6 Probabilistic Design Optimisation Problem -- 7 Optimisation Pipeline -- 8 Results -- 8.1 Deterministic Optimisation -- 8.2 Probabilistic Optimisation -- 9 Conclusion -- References -- Scalable Dynamic Asynchronous Monte Carlo Framework Applied to Wind Engineering Problems -- 1 Introduction -- 2 Monte Carlo Methods -- 2.1 Monte Carlo -- 2.2 Asynchronous Monte Carlo -- 2.3 Scheduling -- 3 Wind Engineering Benchmark -- 3.1 Problem Description -- 3.2 Source of Uncertainty -- 3.3 Results -- 4 Conclusion -- References -- Multi-Objective Optimal Design and Maintenance for Systems Based on Calendar Times Using MOEA/D-DE -- 1 Introduction.2 Methodology and Description of the Proposed Model -- 2.1 Extracting Availability and Economic Cost from Functionability Profiles -- 2.2 Multi-Objective Optimization Approach -- 2.3 Building Functionability Profiles -- 3 The Application Case -- 4 Results and Discussion -- 5 Conclusions -- References -- Multi-objective Robustness Analysis of the Polymer Extrusion Process -- 1 Introduction -- 2 Robustness in Polymer Extrusion -- 2.1 Extrusion Process -- 2.2 Robustness Methodology -- 2.3 Multi-objective Optimization with Robustness -- 3 Results and Discussion -- 4 Conclusion -- References -- Quantification of Operational and Geometrical Uncertainties of a 1.5-Stage Axial Compressor with Cavity Leakage Flows -- 1 Motivation and Test Case Description -- 1.1 Geometry and Operating Regime -- 1.2 Uncertainty Definition -- Correlated Fields at the Main Inlet -- Secondary Inlets -- Rotor Blade Tip Gap -- 2 Uncertainty Quantification Method -- 2.1 Scaled Sensitivity Derivatives -- 3 Simulation Setup and Computational Cost -- 4 Results and Discussion -- 4.1 Non-deterministic Performance Curve -- 4.2 Scaled Sensitivity Derivatives -- 5 Conclusions -- References -- Can Uncertainty Propagation Solve the Mysterious Case of Snoopy? -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Dynamics Modelling -- 3.2 Using the TDA Structure to Solve ODE -- 3.3 Performing Numerical Analysis -- 3.4 Propagator Implementation and Validation -- 3.5 Monte-Carlo Estimation -- 4 Results and Discussion -- 4.1 Performing Numerical Analysis on the Trajectory of Snoopy -- 4.2 Computing Snoopy's Trajectory -- 4.3 Estimating the Probability of Snoopy's Presence -- 5 Conclusions and Future Work -- References -- Part II Imprecise Probability, Theory and Applications (IP) -- Robust Particle Filter for Space Navigation Under EpistemicUncertainty -- 1 Introduction.2 Filtering Under Epistemic Uncertainty -- 2.1 Imprecise Formulation -- 2.2 Expectation Estimator -- 2.3 Bound Estimator -- 3 Test Case -- 3.1 Initial State Uncertainty -- 3.2 Observation Model and Errors -- 3.3 Results -- 4 Conclusions -- References -- Computing Bounds for Imprecise Continuous-Time Markov Chains Using Normal Cones -- 1 Introduction -- 2 Imprecise Markov Chains in Continuous Time -- 2.1 Imprecise Distributions over States -- 2.2 Imprecise Transition Rate Matrices -- 2.3 Distributions at Time t -- 3 Numerical Methods for Finding Lower Expectations -- 3.1 Lower Expectation and Transition Operators as Linear Programming Problems -- 3.2 Computational Approaches to Estimating Lower Expectation Functionals -- 4 Normal Cones of Imprecise Q-Operators -- 5 Norms of Q-Matrices -- 6 Numerical Methods for CTIMC Bounds Calculation -- 6.1 Matrix Exponential Method -- 6.2 Checking Applicability of the Matrix Exponential Method -- 6.3 Checking the Normal Cone Inclusion -- 6.4 Approximate Matrix Exponential Method -- 7 Error Estimation -- 7.1 General Error Bounds -- 7.2 Error Estimation for a Single Step -- 7.3 Error Estimation for the Uniform Grid -- 8 Algorithm and Examples -- 8.1 Parts of the Algorithm -- 8.2 Examples -- 9 Concluding Remarks -- References -- Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference -- 1 Introduction -- 2 Markov Chain Monte Carlo -- 3 Simultaneous Sampling -- 4 Markov Chain Monte Carlo for Imprecise Models -- 5 Practical Implementation -- 6 Linear Representation for Exponential Families -- 7 Computer Representation of the Credal Sets -- 8 Credal Set Merging -- 9 Discussion -- Reference -- Computing Expected Hitting Times for Imprecise Markov Chains -- 1 Introduction -- 2 Existence of Solutions -- 3 A Computational Method -- 4 Complexity Analysis -- References.Part III Robust and Reliability-Based Design Optimisation in Aerospace Engineering (RBDO) -- Multi-Objective Robust Trajectory Optimization of Multi-Asteroid Fly-By Under Epistemic Uncertainty -- 1 Introduction -- 2 Problem Formulation -- 3 Lower Expectation -- 3.1 Minimizing the Expectation -- 3.2 Estimating the Expectation -- 4 Multi-Objective Optimization -- 4.1 Control Mapping for Dimensionality Reduction -- Deterministic Control Map -- Max-Min Control Map -- Min-Max Control Map -- 4.2 Threshold Mapping -- 5 Asteroid Tour Test Case -- 6 Results -- 6.1 Control Map and Threshold Map -- 6.2 Lower Expectation -- 6.3 Expectation and Sampling Methods -- 6.4 Execution Times -- 7 Conclusions -- References -- Reliability-Based Robust Design Optimization of a Jet Engine Nacelle -- 1 Introduction -- 2 Definition of Aeronautical Optimization Under Uncertainties -- 2.1 Nacelle Acoustic Liner and Manufacturing Tolerances -- 2.2 Nacelle Acoustic Liner FEM Model -- 3 Adaptive Sparse Polynomial Chaos for Reliability Problems -- 3.1 Basic Formulation of Adaptive PCE -- 3.2 Adaptive Sparse Polynomial Chaos Expansion -- 3.3 Application of Adaptive PCE to Reliability-Based Optimization -- 4 Reliability-Based Optimization of the Engine Nacelle -- 4.1 Optimization Platform -- 4.2 Optimization Results -- 5 Conclusion -- References -- Bayesian Optimization for Robust Solutions Under Uncertain Input -- 1 Introduction -- 2 Literature Review -- 3 Problem Definition -- 4 Methodology -- 4.1 Gaussian Process -- 4.2 Robust Bayesian Optimization -- Direct Robustness Approximation -- Robust Knowledge Gradient -- 4.3 Stochastic Kriging -- 5 Experiments -- 5.1 Benchmark Problems -- Test Functions -- Experimental Setup -- 5.2 Results -- Latin Hypercube Sampling -- Stochastic Kriging -- Uncontrollable Input -- 6 Conclusions -- References.Optimization Under Uncertainty of Shock Control Bumps for Transonic Wings -- 1 Introduction -- 2 Gradient-Based Robust Design Framework -- 2.1 Motivation -- 2.2 Surrogate-Based Uncertainty Quantification -- 2.3 Obtaining the Gradients of the Statistics -- 2.4 Optimization Architecture -- 2.5 Application to Analytical Test Function -- 3 Application to the Robust Design of Shock Control Bumps: Problem Definition -- 3.1 Test Case -- 3.2 Numerical Model -- 3.3 Parametrization of Shock Control Bumps -- 3.4 Optimization Formulations -- 4 Results -- 4.1 Single-Point (Deterministic) Results -- 4.2 Uncertainty Quantification -- 4.3 Robust Results -- 5 Conclusions -- References -- Multi-Objective Design Optimisation of an Airfoil with Geometrical Uncertainties Leveraging Multi-Fidelity Gaussian Process Regression -- 1 Introduction -- 2 Design Optimisation Problem of Airfoil -- 3 Solvers -- 4 Multi-Fidelity Gaussian Process Regression -- 5 Uncertainty Treatment -- 6 Multi-Objective Optimisation Framework for Airfoil Optimisation Under Uncertainty -- 7 Results -- 8 Conclusion -- References -- High-Lift Devices Topology Robust Optimisation Using Machine Learning Assisted Optimisation -- 1 Introduction -- 2 Machine Learning Assisted Optimisation -- 2.1 Surrogate Model -- 2.2 Classifier -- 3 Quadrature Approach for Uncertainty Quantification -- 4 Problem Formulation -- 4.1 Optimisation Design Variables -- 4.2 High-Lift Devices Robust Optimisation Problem -- Original Objective Function -- Artificial Objective Function -- 5 Optimisation Setup -- 6 Results -- 7 Conclusions and Future Work -- References -- Network Resilience Optimisation of Complex Systems -- 1 Introduction -- 2 Evidence Theory as Uncertainty Framework -- 3 System Network Model -- 4 Complexity Reduction of Uncertainty Quantification -- 4.1 Network Decomposition -- 4.2 Tree-Based Exploration.4.3 Combined Method.Space Technology Proceedings Measurement uncertainty (Statistics)Mathematical optimizationOuter spaceMeasurement uncertainty (Statistics)Mathematical optimization.629.101519544Vasile MassimilianoQuagliarella D.MiAaPQMiAaPQMiAaPQBOOK996466732803316Advances in uncertainty quantification and optimization under uncertainty with aerospace applications2914970UNISA