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Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors
| Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (230 pages) |
| Disciplina | 519.3 |
| Collana | Modeling and Simulation in Science, Engineering and Technology |
| Soggetto topico |
Mathematical optimization
Mathematical optimization - Computer programs Models matemàtics Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-93302-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Variability and Heterogeneity in Natural Swarms: Experiments and Modeling -- 1 Introduction -- 2 Sources of Variability in Nature -- 2.1 Development as a Source of Variation -- 2.2 Transient Changes in the Behavior of Individuals -- 2.3 Environmentally Induced Variations -- 2.4 Social Structure -- 2.5 Inherent/Intrinsic Properties and Animal Personality -- 2.6 Variability in Microorganisms -- 3 Experiments with Heterogeneous Swarms -- 3.1 Fish -- 3.2 Mammals -- 3.3 Insects -- 3.4 Microorganisms -- 4 Modeling Heterogeneous Collective Motion -- 4.1 Continuous Models -- 4.2 Agent-Based Models -- 4.3 Specific Examples: Locust -- 4.4 Specific Examples: Microorganisms and Cells -- 5 Summary and Concluding Remarks -- References -- Active Crowds -- 1 Introduction -- 2 Models for Active Particles -- 2.1 Continuous Random Walks -- 2.1.1 Excluded-Volume Interactions -- 2.2 Discrete Random Walks -- 2.3 Hybrid Random Walks -- 3 Models for Externally Activated Particles -- 3.1 Continuous Models -- 3.2 Discrete Models -- 4 General Model Structure -- 4.1 Wasserstein Gradient Flows -- 4.2 Entropy Dissipation -- 5 Boundary Effects -- 5.1 Mass Conserving Boundary Conditions -- 5.2 Flux Boundary Conditions -- 5.3 Other Boundary Conditions -- 6 Active Crowds in the Life and Social Science -- 6.1 Pedestrian Dynamics -- 6.2 Transport in Biological Systems -- 7 Numerical Simulations -- 7.1 One Spatial Dimension -- 7.2 Two Spatial Dimensions -- References -- Mathematical Modeling of Cell Collective Motion Triggered by Self-Generated Gradients -- 1 Introduction -- 2 The Keller-Segel Model and Variations -- 2.1 The Construction of Waves by Keller and Segel -- 2.2 Positivity and Stability Issues -- 2.3 Variations on the Keller-Segel Model -- 2.4 Beyond the Keller-Segel Model: Two Scenarios for SGG.
3 Scenario 1: Strongest Advection at the Back -- 4 Scenario 2: Cell Leakage Compensated by Growth -- 5 Conclusion and Perspectives -- References -- Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker-Planck Interpolation -- 1 Introduction -- 1.1 Mean Shift-Based Methods -- 1.1.1 Lifting the Dynamics to the Wasserstein Space -- 1.2 Spectral Methods -- 1.2.1 Normalized Versions of the Graph Laplacian -- 1.2.2 More General Spectral Embeddings -- 1.3 Outline -- 2 Mean Shift and Fokker-Planck Dynamics on Graphs -- 2.1 Dynamic Interpretation of Spectral Embeddings -- 2.2 The Mean Shift Algorithm on Graphs -- 2.2.1 Mean Shift on Graphs as Inspired by Wasserstein Gradient Flows -- 2.2.2 Quickshift and KNF -- 3 Fokker-Planck Equations on Graphs -- 3.1 Fokker-Planck Equations on Graphs via Interpolation -- 3.2 Fokker-Planck Equation on Graphs via Reweighing and Connections to Graph Mean Shift -- 4 Continuum Limits of Fokker-Planck Equations on Graphs and Implications -- 4.1 Continuum Limit of Mean Shift Dynamics on Graphs -- 4.2 Continuum Limits of Fokker-Planck Equations on Graphs -- 4.3 The Witten Laplacian and Some Implications for Data Clustering -- 5 Numerical Examples -- 5.1 Numerical Method -- 5.2 Simulations -- 5.2.1 Graph Dynamics as Density Dynamics -- 5.2.2 Comparison of Graph Dynamics and PDE Dynamics -- 5.2.3 Clustering Dynamics -- 5.2.4 Effect of the Kernel Density Estimate on Clustering -- 5.2.5 Effect of Data Distribution on Clustering -- 5.2.6 Blue Sky Problem -- 5.2.7 Density vs. Geometry -- References -- Random Batch Methods for Classical and Quantum Interacting Particle Systems and Statistical Samplings -- 1 Introduction -- 2 The Random Batch Methods -- 2.1 The RBM Algorithms -- 2.2 Convergence Analysis -- 2.3 An Illustrating Example: Wealth Evolution -- 3 The Mean-Field Limit -- 4 Molecular Dynamics. 4.1 RBM with Kernel Splitting -- 4.2 Random Batch Ewald: An Importance Sampling in the Fourier Space -- 5 Statistical Sampling -- 5.1 Random Batch Monte Carlo for Many-Body Systems -- 5.2 RBM-SVGD: A Stochastic Version of Stein Variational Gradient Descent -- 6 Agent-Based Models for Collective Dynamics -- 6.1 The Cucker-Smale Model -- 6.2 Consensus Models -- 7 Quantum Dynamics -- 7.1 A Theoretical Result on the N-Body Schrödinger Equation -- 7.1.1 Mathematical Setting and Main Result -- 7.2 Quantum Monte Carlo Methods -- 7.2.1 The Random Batch Method for VMC -- 7.2.2 The Random Batch Method for DMC -- References -- Trends in Consensus-Based Optimization -- 1 Introduction -- 1.1 Notation and Assumptions -- 1.1.1 The Weighted Average -- 2 Consensus-Based Global Optimization Methods -- 2.1 Original Statement of the Method -- 2.1.1 Particle Scheme -- 2.1.2 Mean-Field Limit -- 2.1.3 Analytical Results for the Original Scheme Without Heaviside Function -- 2.1.4 Numerical Methods -- 2.2 Variant 1: Component-Wise Diffusion and Random Batches -- 2.2.1 Component-Wise Geometric Brownian Motion -- 2.2.2 Random Batch Method -- 2.2.3 Implementation and Numerical Results -- 2.3 Variant 2: Component-Wise Common Diffusion -- 2.3.1 Analytical Results -- 2.3.2 Numerical Results -- 3 Relationship of CBO and Particle Swarm Optimization -- 3.1 Variant 4: Personal Best Information -- 3.1.1 Performance -- 4 CBO with State Constraints -- 4.1 Variant 5: Dynamics Constrained to Hyper-Surfaces -- 4.1.1 Analytical Results -- 5 Overview of Applications -- 5.1 Global Optimization Problems: Comparison to Heuristic Methods -- 5.2 Machine Learning -- 5.3 Global Optimization with Constrained State Space -- 5.4 PDE Versus SDE Simulations -- 6 Conclusion, Outlook and Open problems -- References. |
| Record Nr. | UNISA-996466417303316 |
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advances in computational methods and technologies in aeronautics and industry / / edited by Dietrich Knoerzer, Jacques Periaux, and Tero Tuovinen
| Advances in computational methods and technologies in aeronautics and industry / / edited by Dietrich Knoerzer, Jacques Periaux, and Tero Tuovinen |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (290 pages) |
| Disciplina | 551.48 |
| Collana | Computational Methods in Applied Sciences |
| Soggetto topico |
Aeronautics
Aeronàutica Models matemàtics Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-12019-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996503550903316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advances in fuzzy group decision making / / Tin-Chih Toly Chen
| Advances in fuzzy group decision making / / Tin-Chih Toly Chen |
| Autore | Chen Toly <1969-> |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (94 pages) |
| Disciplina | 003.56 |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Fuzzy decision making
Conjunts borrosos Decisió de grup Models matemàtics Group decision making - Mathematical models |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-86208-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466387303316 |
Chen Toly <1969->
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors
| Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (426 pages) |
| Disciplina | 511.8 |
| Soggetto topico |
Mathematical models
Simulation methods Models matemàtics Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-10193-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Biography -- Contents -- Monte Carlo Methods for Pricing American Options -- 1 Introduction -- 2 American Option Pricing -- 3 Binomial Tree Method -- 4 Dynamic Programming Approach -- 4.1 Regression Methods -- 4.2 Malliavin Calculus -- 5 Control Variates -- 6 Numerical Experiments -- 7 Conclusion -- References -- Remarks on Lévy Process Simulation -- 1 Introduction -- 2 Lévy Processes -- 3 Main Examples -- 4 The ε-Algorithm -- 5 Using Complete Monotonicity Structure -- 6 Numerical Examples -- 7 Exact Simulation of X(h) and other Methods -- 8 Maxima, Minima and Other Path Functionals -- References -- Exact Sampling for the Maximum of Infinite Memory Gaussian Processes -- 1 Introduction -- 2 Basic Strategy -- 2.1 Milestone Events -- 2.2 Main Algorithm -- 3 Intermediate Steps in Algorithm 2 -- 4 Analysis of Algorithm 2 -- 4.1 Output Analysis -- 4.2 Complexity Analysis -- 5 Numerical Experiments -- 6 Conclusion -- References -- Truncated Multivariate Student Computations via Exponential Tilting -- 1 Introduction -- 2 Review of the Sequentially Tilted Proposal Density -- 3 Asymptotic Efficiency of the IS Estimator -- 4 Application to Constrained Linear Regression -- 5 Tobit Model Application -- 6 Application to ``Bayesian'' Splines for Non-negative Functions -- 7 The Reject-Regenerate Sampler -- 7.1 Nummelin Splitting of Transition Kernel -- 7.2 Rare-Event Robustness -- 8 Concluding Remarks -- References -- Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints -- 1 Introduction -- 2 Classical Portfolio Selection in a Nutshell -- 3 Portfolio Optimization with Many Constraints -- 4 Approximation of the Opportunity Set by Naïve Monte Carlo, and by Exponential Monte Carlo -- 5 Approximation of the Opportunity Set with Exponential QMC.
6 Approximating the Market Portfolio with MC, Exponential MC, and Exponential QMC -- 7 Approximating the Whole OS with MC, Exponential MC, and Exponential QMC -- 8 How to Calculate the Dispersion of a Sample Set in an OS? -- 9 Some Simulation Results -- 10 Conclusions, Outlook, and Further Practical Problem -- References -- Geometric-Moment Contraction of G/G/1 Waiting Times -- 1 Introduction -- 2 Main Results -- 3 Monte Carlo Results -- 3.1 M/M/1 Queue -- 3.2 M/G/1 Queues -- 4 Conclusions -- References -- Tractability of Approximation in the Weighted Korobov Space in the Worst-Case Setting -- 1 Introduction -- 2 Basic Definitions -- 2.1 Function Space Setting -- 2.2 Approximation in script upper H Subscript d comma alpha comma bold italic gammamathcalHd,α,γ -- 2.3 The Worst-Case Setting -- 2.4 Useful Relations -- 2.5 Relations to the Average-Case Setting -- 2.6 Notions of Tractability -- 3 The Results for normal upper A normal upper P normal upper P Subscript 2APP2 -- 4 The Results for normal upper A normal upper P normal upper P Subscript normal infinityAPPinfty -- 5 Overview and Formulation of Open Problems -- 5.1 Open Problems -- References -- Rare-Event Simulation via Neural Networks -- 1 Introduction -- 1.1 Background -- 2 Rare-Event Deep Learning -- 2.1 Networks and Loss Functions -- 2.2 Kernel Density Estimation -- 2.3 Training Procedure -- 2.4 Rare-Event Distribution -- 3 Experimental Results -- 3.1 Learning Normal Distributions -- 3.2 Normal Distribution Rare-Events -- 3.3 Learning Sum of Exponential Distributions -- 4 Conclusions and Further Research -- References -- Preintegration is Not Smoothing When Monotonicity Fails -- 1 Introduction -- 1.1 Related Work -- 1.2 The Problem -- 1.3 Informative Examples -- 1.4 Outline of This Paper -- 2 Smoothness Theorems in dd Dimensions -- 3 A High-Dimensional Example -- 4 Conclusion -- References. Combined Derivative Estimators -- 1 Introduction -- 2 Derivative Estimation -- 2.1 Background -- 2.2 Combined Estimators -- 2.3 Second Derivatives -- 2.4 Finite Difference Estimators and IPA -- 2.5 IPA and Randomized Score Functions -- 2.6 LRM Singularities -- 2.7 Generalized Likelihood Ratio Method -- 3 A Barrier Option Example -- 3.1 The Option Pricing Setting -- 3.2 The Barrier Option -- 3.3 A Combined IPA-LRM Estimator of Wang et al. ch10wang -- 3.4 GLR as a Combined IPA-LRM Estimator -- 4 Approaching Continuous Time: Averaging Low-Rank GLR Estimators -- 4.1 Approximating Continuous-Time Sensitivities -- 4.2 Averaging GLR Estimators -- 5 Concluding Remarks -- References -- A Central Limit Theorem For Empirical Quantiles in the Markov Chain Setting -- 1 Introduction -- 2 A Quantile Central Limit Theorem -- 3 A Uniform CLT for 1-Dependent Sequences -- 4 A Quantile Central Limit Theorem for Harris Processes -- 5 The Validity of Non-overlapping Batch-Means Estimation -- 6 Sufficient Conditions -- References -- Simulation of Markov Chains with Continuous State Space by Using Simple Stratified and Sudoku Latin Square Sampling -- 1 Introduction -- 2 Markov Chain Simulation with Stratified Sampling -- 2.1 Classical Monte Carlo -- 2.2 Simple Stratified Sampling -- 2.3 Sudoku Latin Square Sampling -- 3 Variance Bounds -- 3.1 Classical Monte Carlo -- 3.2 Simple Stratified Sampling -- 3.3 Sudoku Latin Square Sampling -- 4 Numerical Experiments -- 4.1 An Autoregressive Process -- 4.2 A European Put Option -- 4.3 Diffusion -- 5 Conclusions -- References -- Quasi-Random Sampling with Black Box or Acceptance-Rejection Inputs -- 1 Introduction -- 2 Methods for the Black Box Setting -- 2.1 Methods Based on the Empirical Quantile Function -- 2.2 Methods Based on a Generalized Pareto Approximation in the Tail -- 3 Combining AR with RQMC. 4 Application: Basket Option Pricing -- 5 Conclusion -- References -- A Generalized Transformed Density Rejection Algorithm -- 1 Introduction -- 2 Transformed Density Rejection with Inflection Points -- 3 Determine Signs of Second Derivatives -- 3.1 Initial Intervals -- 3.2 Splitting Intervals -- 4 The Algorithm -- 5 Applications -- 5.1 Generalized Hyperbolic Distribution -- 5.2 Truncated Distributions -- 5.3 Watson Distributions -- 6 Conclusions -- References -- Fast Automatic Bayesian Cubature Using Sobol' Sampling -- 1 Introduction -- 2 Bayesian Cubature -- 3 Digital Nets and Walsh Kernels -- 3.1 Digital Sequences -- 3.2 Covariance Kernels Constructed Via Walsh Functions -- 3.3 Eigenvector-Eigenvalue Decomposition of the Gram Matrix -- 4 Numerical Experiments -- 4.1 Multivariate Gaussian Probability -- 4.2 Keister's Example -- 4.3 Asian Option Pricing -- 4.4 Discussion -- 5 Conclusion and Future Work -- References -- Rendering Along the Hilbert Curve -- 1 Introduction -- 2 Visual Error in Image Synthesis -- 3 Enumerating Pixels Along the Hilbert Curve -- 3.1 Correlation in Space-Filling Curves -- 3.2 Blue-Noise Dithered Sampling -- 4 Progressive Image Synthesis -- 4.1 Deterministic Cranley-Patterson Rotation -- 4.2 Randomization -- 4.3 Contiguous Segments of one Low Discrepancy Sequence -- 4.4 Partitioning one Low Discrepancy Sequence -- 5 Results and Discussion -- 6 Conclusion -- References -- Array-RQMC to Speed up the Simulation for Estimating the Hitting-Time Distribution to a Rare Set of a Regenerative System -- 1 Introduction -- 2 Regenerative-Simulation-Based Estimators of the Distribution of the Hitting Time to a Rarely Visited Set -- 2.1 Assumptions and Notations -- 2.2 Exponential Limit -- 2.3 Exponential Estimators with Monte Carlo (MC) -- 2.4 Convolution Estimators with Monte Carlo. 3 Array-RQMC Implementation of Regenerative-Simulation-Based Estimators of Quantiles -- 3.1 RQMC and Array-RQMC -- 3.2 Array-RQMC Exponential and Convolution Estimators -- 4 Numerical Illustration of the Gain on the Simulation of an M/M/1 Queue -- 5 Conclusions -- References -- Foundations of Ranking & -- Selection for Simulation Optimization -- 1 Introduction -- 2 Set Up -- 3 The Normal Means Case -- 3.1 The Indifference-Zone (IZ) Formulation -- 3.2 R& -- S Based on ``Statistical Learning'' -- 3.3 A Convergence-Rate Perspective -- 3.4 Doing Better Than ``Rate Optimal'' -- 3.5 Common Random Numbers -- 3.6 ``Good Selection'' -- 3.7 Unknown Variances -- 3.8 A Note on Asymptotic Analysis -- 4 Parallel R& -- S -- 4.1 New Measures of Efficiency -- 4.2 New Objectives -- 4.3 Parting Thoughts -- 5 Other Formulations -- 6 Multi-armed Bandits -- References -- Where are the Logs? -- 1 Introduction -- 2 Background -- 3 Proof of the Lower Bound -- 4 Discrepancy and the Case of d equals 1d=1 -- 5 Empirical Investigations for d equals 2d=2 -- 6 Very Large mm for Sobol' Nets -- 7 Discussion -- References -- Network Reliability, Performability Metrics, Rare Events and Standard Monte Carlo -- 1 Introduction -- 2 Performability Metrics and Resilience -- 2.1 The Resilience Metric -- 2.2 Some Properties of Resilience -- 3 Using Standard Monte Carlo for Resilience-Based Analysis -- 3.1 The Standard Estimator -- 3.2 The Standard Estimator Efficiently Implemented in the Rare Event Case -- 3.3 Estimating the Resilience -- 3.4 Improving Algorithm B -- 3.5 Sensitivity Analysis -- 4 Examples and Discussions -- 5 Conclusions -- References. |
| Record Nr. | UNISA-996499866203316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Algorithms for a New World [[electronic resource] ] : When Big Data and Mathematical Models Meet / / by Alfio Quarteroni
| Algorithms for a New World [[electronic resource] ] : When Big Data and Mathematical Models Meet / / by Alfio Quarteroni |
| Autore | Quarteroni Alfio |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (68 pages) : illustrations |
| Disciplina | 006.3 |
| Soggetto topico |
Mathematics
Machine learning Artificial intelligence Quantitative research Algorithms Applications of Mathematics Machine Learning Artificial Intelligence Data Analysis and Big Data Models matemàtics Intel·ligència artificial Dades massives |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030961664
9783030961657 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Epidemic -- 2 Retrospective -- 3 Interlude: the revolution that did not happen and the revolution that was unforeseen -- 4 Artificial intelligence, learning computers, artificial neural networks -- 5 A bit of maths (behind artificial intelligence and machine learning) -- 6 BIG DATA - BIG BROTHER (or, on the ethical and moral aspects of artificial intelligence). |
| Record Nr. | UNISA-996483155303316 |
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Quarteroni Alfio
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Analysis and design of nonlinear systems in the frequency domain / / Yunpeng Zhu
| Analysis and design of nonlinear systems in the frequency domain / / Yunpeng Zhu |
| Autore | Zhu Yunpeng |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (xxi, 164 pages) : illustrations |
| Disciplina | 003.75 |
| Collana | Springer Theses, Recognizing Outstanding Ph.D. Research |
| Soggetto topico |
Nonlinear systems - Mathematical models
Volterra equations Sistemes no lineals Models matemàtics Equacions de Volterra |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-70833-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Supervisor's Foreword -- Preface -- Acknowledgements -- Contents -- Contributors -- Abbreviations -- 1 Introduction -- 1.1 Background -- 1.1.1 Modelling of Nonlinear Systems -- 1.1.2 Frequency Domain Analysis and Design of Nonlinear Systems -- 1.1.3 LS Methods in Nonlinear System Analyses -- 1.1.4 Convergence Issues with the Frequency Analysis of Nonlinear Systems -- 1.2 Aim, Objectives and Contributions -- 1.3 Thesis Layout -- References -- 2 Nonlinear Systems and the Frequency Domain Representations -- 2.1 Introduction -- 2.2 Polynomial Models of Nonlinear Systems -- 2.2.1 The NDE Model of Nonlinear Systems -- 2.2.2 The Polynomial NARX Model of Nonlinear Systems -- 2.2.3 The NARX-M-for-D of Nonlinear Systems -- 2.3 The Frequency Domain Representations of Nonlinear Systems -- 2.3.1 The Volterra Series Representation -- 2.3.2 The Generalised Frequency Response Functions (GFRFs) of Nonlinear Systems -- 2.3.3 The Nonlinear Output Frequency Response Functions (NOFRFs) of Nonlinear Systems -- 2.3.4 The Output Frequency Response Function (OFRF) of Nonlinear Systems -- 2.4 Conclusions -- References -- 3 Generalized Associated Linear Equations (GALEs) with Applications to Nonlinear System Analyses -- 3.1 Introduction -- 3.2 The Associated Linear Equations (ALEs) of Nonlinear Systems -- 3.2.1 The ALEs of Duffing Equations -- 3.2.2 The ALEs of the NARX Model -- 3.3 The Generalized Associated Linear Equations (GALEs) -- 3.3.1 The Concept of the GALEs -- 3.3.2 Determination of the GALEs -- 3.4 System Analyses Using the GALEs -- 3.4.1 Evaluation of the Output Response of Nonlinear Systems -- 3.4.2 Evaluation of the NOFRFs of Nonlinear Systems -- 3.4.3 Evaluation of the OFRF of Nonlinear Systems -- 3.5 Application of the GALEs to Nonlinear System Modelling, Fault Diagnosis, and Design.
3.5.1 Application to the Identification of the NDE Model of a Nonlinear System -- 3.5.2 Application to the NOFRFs Based Fault Diagnosis -- 3.5.3 Application to the OFRFs Based Design of Nonlinear Energy Harvester Systems -- 3.6 Conclusions -- References -- 4 The Convergence of the Volterra Series Representation of Nonlinear Systems -- 4.1 Introduction -- 4.2 The NARX Model in the Frequency Domain: Nonlinear Output Characteristic Spectra (NOCS) Model -- 4.3 The Generalized Output Bound Characteristic Function (GOBCF) Based Convergence Analysis -- 4.3.1 A Sufficient Condition of the Convergence -- 4.3.2 The Determination of the GOBCF -- 4.3.3 Convergence Analysis of the Volterra Series Representation of Nonlinear Systems -- 4.3.4 The Procedure for the New Convergence Analysis -- 4.4 Case Studies -- 4.4.1 Case 1-Unplugged Van der Pol Equation -- 4.4.2 Case 2-Duffing Oscillator with Cubic Damping -- 4.5 Conclusions -- References -- 5 The Effects of Both Linear and Nonlinear Characteristic Parameters on the Output Response of Nonlinear Systems -- 5.1 Introduction -- 5.2 The OFRF Based Design of NARX-M-for-D -- 5.2.1 The OFRF of the NARX-M-for-D -- 5.2.2 The Determination of the OFRF of NARX-M-for-D -- 5.2.3 The OFRF Based Design of Nonlinear Systems -- 5.3 The Associated Output Frequency Response Function (AOFRF) -- 5.3.1 Explicit Relationships Between the GFRFs and the Parameters of the NARX Model -- 5.3.2 Two Special Cases -- 5.3.3 The Concept of the Associated Output Frequency Response Function (AOFRF) -- 5.3.4 The AOFRF in Terms of the System Linear and Nonlinear Characteristic Parameters -- 5.3.5 The AOFRF Based Representation of the Output Frequency Response of Nonlinear Systems -- 5.4 Case Studies -- 5.4.1 Case Study 1-The OFRF Based Design of the Vibration Isolation System. 5.4.2 Case Study 2-The AOFRF Based Representation of the Output Spectrum of a Duffing Nonlinear System -- 5.5 Conclusions -- References -- 6 Nonlinear Damping Based Semi-active Building Isolation System -- 6.1 Introduction -- 6.2 Semi-active Damping System for the Sosokan Building -- 6.2.1 The Sosokan Building and Its Model Representation -- 6.2.2 Semi-active Damping System for the Sosokan Building -- 6.3 Nonlinear Damping Based Semi-active Building Vibration Isolation -- 6.4 Simulation Studies -- 6.4.1 Objectives of Nonlinear Damping Design -- 6.4.2 Effects of Nonlinear Damping Coefficient -- 6.4.3 Effects of Ground Excitation Magnitude -- 6.4.4 Isolation Performance on Higher Floors -- 6.4.5 Isolation Performance in Terms of the Roof Drift -- 6.4.6 Isolation Performance in Terms of Harmonics and a Comparison with the Performance Under LQG Control -- 6.5 Experimental Validation -- 6.6 Conclusions -- References -- 7 Conclusions -- 7.1 Main Contributions of the Present Research -- 7.2 Future Works -- Appendix A Sampling Frequency Independence -- Appendix B Proof of Lemma 5.1. |
| Record Nr. | UNINA-9910484062803321 |
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Zhu Yunpeng
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Analysis of infectious disease problems (Covid-19) and their global impact / / Praveen Agarwal [and three others], editors
| Analysis of infectious disease problems (Covid-19) and their global impact / / Praveen Agarwal [and three others], editors |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (635 pages) |
| Disciplina | 616.241400285 |
| Collana | Infosys Science Foundation series in mathematical sciences |
| Soggetto topico |
COVID-19
Models matemàtics COVID-19 (Disease) - Mathematical models |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-2450-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- About the Editors -- General Analysis -- Continued and Serious Lockdown Could Have Minimized Many Newly Transmitted Cases of Covid-19 in the U.S.: Wavelets, Deterministic Models, and Data -- 1 Introduction -- 2 Methods, Models and Data -- 3 Data -- 4 Results -- 5 Concluding Remarks -- References -- Dynamical Analysis of a Caputo Fractional Order SIR Epidemic Model with a General Treatment Function -- 1 Introduction -- 2 Mathematical Model and Preliminaries -- 3 Preliminaries -- 4 The Well-Posedness of the Model and Equilibria -- 4.1 Existence of Endemic Equilibrium -- 5 Local Stability Analysis -- 6 Global Stability Analysis -- 6.1 Infection-Free Equilibrium -- 6.2 Endemic Equilibrium -- 7 Numerical Simulations -- 8 Concluding Remarks -- References -- Protective Face Shield Effectiveness: Mathematical Modelling -- 1 Introduction -- 2 Practical Application of Face Shields -- 3 Mathematical Modelling -- 3.1 Euler Form of Equations -- 3.2 Lagrangian Form of Equations -- 3.3 Model Description -- 3.4 Numerical Methods -- 3.5 Computer Simulation -- 4 Full-Scale Experiment -- 5 Conclusion -- References -- On the Evolution Equation for Modelling the Covid-19 Pandemic -- 1 Introduction -- 2 The Evolution Equation -- 2.1 The Classical Kolmogorov-Feller Equation -- 2.2 The Generalised Kolmogorov-Feller Equation -- 2.3 Orthonormal Memory Functions -- 2.4 Time Series Models -- 2.5 Logarithmic Scale Analysis -- 3 Random Walk Fields -- 4 Self-Affine Random Walk Fields -- 4.1 Solution for Eq. (7) -- 4.2 Solution for Eq. (8) -- 4.3 Random Walk Analysis -- 4.4 Example Results -- 5 The Bio-Dynamics Hypothesis -- 5.1 Self-affine Structures of a Virus -- 5.2 A Parametric Self-affine Model -- 5.3 Discussion -- 6 Summary, Conclusions and Future Research -- 6.1 Summary -- 6.2 Conclusions -- 6.3 Future Research.
References -- Modelling the Dynamics of Fake News Spreading Transmission During Covid-19 Through Social Media -- 1 Introduction -- 2 Methodology/Proposal -- 2.1 SIR Model for Fake News Transmission -- 2.2 Fake News Transmission Rate Through Different Social Media Platforms -- 2.3 Fake News Transmission Rate Among Users of Different Age Groups -- 2.4 Fake News Transmission Rate Among Users of Facebook from Different Countries -- 3 Results, Interpretation and Discussion -- 4 Conclusion -- References -- Generalized Logistic Equations in Covid-Related Epidemic Models -- 1 Introduction -- 2 Logistic Coefficients Models -- 2.1 Computable Examples -- 3 Carrying Capacity Periodically Variable -- 3.1 Existence of Periodic Solution -- 3.2 Cosinusoidal Carrying Capacity -- 4 Periodic Harvesting -- 4.1 Global Features of the Solution -- 4.2 Closed-Form Integration and Examples -- 4.3 A Sample Problem -- References -- A Transition of Shared Mobility in Metro Cities-A Challenge Post-Lockdown Covid-19 -- 1 Introduction -- 2 BPR Model -- 3 Data Analysis & -- Implementation -- 3.1 Data Description -- 3.2 Model Application & -- Results -- 3.3 Prediction of Traffic Scenarios Post-Lockdown -- 4 India's Transport Growth Journey and Its Effect on Energy and Environment -- 4.1 Transport and Environment -- 4.2 Health and Social Issues -- 4.3 Personal Vehicles and Their Impact -- 4.4 Measures to Curb the Traffic Upsurge -- References -- Analysis of Covid-19 Virus Spreading Statistics by the Use of a New Modified Weibull Distribution -- 1 Introduction and Preliminaries -- 1.1 The New Model NMWB Distribution -- 1.2 The Reliability Function -- 1.3 Moments of the Distribution -- 1.4 Order Statistics -- 1.5 Parameter Estimation -- 1.6 Relationship with Weibull-Related Results -- 2 Main Results -- 2.1 Statistical Properties -- 2.2 Least Square Estimates (LSES). 2.3 Order Statistics -- 2.4 Parameter Estimation -- 3 Applications -- 4 Conclusion -- References -- Lifting Lockdown Control Measure Assessment: From Finite-to Infinite-Dimensional Epidemic Models for Covid-19 -- 1 Introduction -- 2 Data Collection -- 3 Basic Covid-19 Model -- 3.1 Reproduction Numbers -- 3.2 Parameter and Initial Data Estimation -- 4 Discrete Age-Structured Covid-19 Model -- 4.1 Reproduction Numbers -- 4.2 Parameter and Initial Data Estimation -- 5 Covid-19 Model with Constant Delay -- 5.1 Reproduction Numbers -- 5.2 Parameter and Initial Data Estimation -- 6 Covid-19 Model with Threshold-Type Delay -- 7 Models with Demographic Effects -- 7.1 Covid-19 Model with Constant Delay -- 7.2 Covid-19 Model with Threshold-Type Delay -- 8 Discussion -- References -- Introduction to the Grey Systems Theory and Its Application in Mathematical Modeling and Pandemic Prediction of Covid-19 -- 1 A Brief Introduction to the Grey Systems Theory -- 2 Description of the Traditional Linear and Nonlinear Univariate Grey Models GM(1, 1) and NGBM(1, 1) -- 2.1 Building the Traditional Grey Model GM(1, 1) -- 2.2 The Nonlinear Grey Bernoulli Model NGBM(1, 1) -- 3 Optimization of the Univariate Grey Models -- 3.1 Optimization of Hyper-parameters -- 3.2 Rolling Mechanism -- 3.3 Optimization of the Initial Condition -- 4 Applications of Univariate Grey Models in Predicting Total Covid-19 Infected Cases -- 5 Description of the Existing GM(1, N) and GMC(1, N) Models -- 5.1 The Traditional GM(1, N) Model -- 5.2 The Grey Model with Convolution Integral GMC(1, N) -- 5.3 Variations of the Current GMC(1, N) and GMC(1, N) Models -- 5.4 Representation of the Nonlinear Grey Model with Convolution Integral NGMC(1, N) -- 6 Grey System Models with Fractional Order Accumulation -- 6.1 Definition of the Fractional Order Accumulation -- 6.2 The Fractional GMpq(1, 1) Model. 6.3 The Fractional Multivariate Grey Model with Convolutional Integral GMC pq(1, N) -- 6.4 Optimization of the Fractional Order r -- 7 Introduction to the Grey Relational Analysis -- 7.1 Data Preprocessing -- 7.2 Grey Relational Coefficient and Grey Relational Grade -- 8 Applications of Grey Relational Analysis In medicine -- 8.1 General Applications of Grey Relational Analysis in Medical Data Analysis -- 8.2 Application in Telecare -- 8.3 Grey Data Management in Medicine -- References -- Mathematical Analysis of Diagnosis Rate Effects in Covid-19 Transmission Dynamics with Optimal Control -- 1 Introduction -- 2 Model Formulation -- 3 Mathematical Analysis -- 3.1 The Disease-Free Equilibrium and Control Reproduction Number -- 3.2 Global Stability of DFE -- 3.3 Existence and Local Stability of the Endemic Equilibrium -- 3.4 Sensitivity Analysis -- 3.5 Numerical Simulation -- 4 Optimal Control -- 4.1 Building the Optimal Control Problem -- 4.2 Characterization of the Optimal Control -- 4.3 Numerical Simulation of the Optimal Control Problem -- 5 Conclusion -- References -- Development of Epidemiological Modeling RD-Covid-19 of Coronavirus Infectious Disease and Its Numerical Simulation -- 1 Introduction -- 2 Infectious Disease Epidemiology Components -- 2.1 Timelines of Infection -- 2.2 Estimation of Transmission Probability -- 2.3 The SAR is a Proportion, Not a Rate -- 3 Estimation of Basic Reproduction Number/ Proliferation Number -- 3.1 Estimation of R0 -- 3.2 Virulence of R0 and the Case Fatality Ratio (CFR) -- 4 Incidence Rate as a Function of Prevalence and Contact Rate -- 5 Dynamic Epidemic Process in a Closed Population -- 6 RD-Covid-19 Epidemiological Model -- 7 Numerical Simulation of RD-Covid-19 Model -- 7.1 PART-1: Numerical Outcome of RD-Covid-19 Model Outcome for INDIA. 7.2 PART-2: Numerical Outcome of RD-Covid-19 Model Outcome for CHINA -- 7.3 PART-3: Numerical Outcome of RD-Covid-19 Model Outcome for BRAZIL -- 7.4 PART-4: Numerical Outcome of RD-Covid-19 Model Outcome for RUSSIA -- 8 Conclusions -- References -- Mediterranean Diet-A Healthy Dietary Pattern and Lifestyle for Strong Immunity -- 1 Introduction -- 2 Mediterranean Lifestyle -- 3 Benefits of Mediterranean Diet -- 4 Mediterranean Diet for a Healthy Gut -- 5 Conclusion -- References -- Rate-Induced Tipping Phenomena in Compartment Models of Epidemics -- 1 Introduction -- 1.1 Outline -- 2 Preliminaries -- 2.1 Compartment Models with Time-Dependent Parameters -- 2.2 Autonomous SIR Model -- 2.3 Autonomous SIRS Model -- 3 Linear Compartment Models -- 3.1 Artifacts of Rate-Induced Tipping -- 4 Nonlinear Compartment Models -- 4.1 Local Normal Form for a Bifurcation of Codimension Two -- 4.2 Idealized Models -- 5 Irreducible Rate-Induced Tipping in Non-autonomous Models -- 5.1 Artifacts of Rate-Induced Tipping -- 6 Conclusion -- References -- Analysis of Impact of Covid-19 Pandemic on Financial Markets -- 1 Introduction -- 2 Market Behaviour During Initial and Intermediate Pandemic Phases -- 2.1 Covid-19 Market Crash (2020/02/19-2020/03/19) -- 2.2 Market Recovery After Covid-19 Crash (2020/03/20 - 2020/03/26) -- 2.3 Pandemic Growth After 2020/03/18 -- 3 Framework for Modelling Pandemic Impact -- 3.1 Susceptible, Infected, Recovered and Death (SIRD) Model with Time-Dependent Parameters and Social Distancing -- 3.2 Calibration Algorithm -- 3.3 Phenomenological Pandemic Model (PPM) -- 3.4 The Process N(t) in an Intermediate Phase -- 3.5 Approximation to PPM -- 3.6 Calibration of PPM -- 3.7 Mapping Epidemic Variables to Financial Risk Factors -- 4 Simulation of Stress Scenarios -- 4.1 Simulation of Risk Drivers Under the SIRD Model -- 4.2 PPM Simulation. 5 Conclusion. |
| Record Nr. | UNISA-996466401503316 |
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Analysis of reaction-diffusion models with the Taxis mechanism / / Yuanyuan Ke, Jing Li, Yifu Wang
| Analysis of reaction-diffusion models with the Taxis mechanism / / Yuanyuan Ke, Jing Li, Yifu Wang |
| Autore | Ke Yuanyuan |
| Pubbl/distr/stampa | Singapore, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (ix, 411 pages) : illustrations (some color) |
| Altri autori (Persone) |
LiJing WangYifu |
| Collana | Financial Mathematics and Fintech |
| Soggetto topico |
Boundary value problems
Chemotaxis - Mathematical models Navier-Stokes equations Problemes de contorn Quimiotaxi Models matemàtics Equacions de Navier-Stokes |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
Reaction-Diffusion
Chemotaxis Haptotaxis Navier-Stokes Cancer invasion Coral fertilization Sensity-suppressed motility Oncolytic virotherapy Foraging scrounging interplay |
| ISBN | 981-19-3763-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Large time behavior of solutions to the chemotaxis-fluid Chapter 2. Global existence in Keller Segel Navier Stokes system involving tensor-valued sensitivity Chapter 3. Large time behavior of solutions to chemotaxis haptotaxis models Chapter 4. Large time behavior of Keller Segel (Navier) Stokes system modeling coral fertilization Chapter 5. Qualitative properties to density-suppressed motility models Chapter 6. Large time behavior of multi-taxis cross-diffusion system |
| Record Nr. | UNISA-996485660903316 |
|
Ke Yuanyuan
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| Singapore, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Analysis of the gravity field : direct and inverse problems / / Fernando Sansò, Daniele Sampietro
| Analysis of the gravity field : direct and inverse problems / / Fernando Sansò, Daniele Sampietro |
| Autore | Sansò F (Fernando), <1945-> |
| Pubbl/distr/stampa | Cham, Switzerland : , : Birkhäuser, , [2022] |
| Descrizione fisica | 1 online resource (542 pages) |
| Disciplina | 531.14 |
| Collana | Lecture notes in geosystems mathematics and computing |
| Soggetto topico |
Gravitational fields - Mathematics
Gravity - Mathematical models Gravity - Measurement Gravitació Models matemàtics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-74353-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996479367803316 |
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Sansò F (Fernando), <1945->
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| Cham, Switzerland : , : Birkhäuser, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Application of mathematics and optimization in construction project management / / Hêriş Golpîra
| Application of mathematics and optimization in construction project management / / Hêriş Golpîra |
| Autore | Golpîra Hêriş |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (261 pages) |
| Disciplina | 338.47624 |
| Soggetto topico |
Gestió de projectes
Indústria de la construcció Models matemàtics Construction industry Project management Mathematical optimization Calculus of variations |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030811235
9783030811228 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editor -- 1 Overview of Project Management -- 1.1 What Are the Projects? -- 1.2 What Are Temporary Organizations? -- 1.3 Why Are Projects Created? -- 1.4 What Is Project Management? -- 1.5 Project Success/Failure -- 1.6 Project Management Main Processes -- 1.7 A Short Review of Applications of Project Management Techniques -- 1.8 Construction Project Management -- 1.9 Conclusion -- References -- 2 Optimization Models and Solution Techniques -- 2.1 Introduction -- 2.2 Project Scheduling Models -- 2.2.1 The Resource-Constrained Project Scheduling Problem -- 2.2.2 Multiple Modes -- 2.2.3 Further Generalizations of the Activity Concept -- 2.2.4 Generalized Temporal Constraints -- 2.2.5 Generalized Resource Constraints -- 2.2.6 Alternative Objectives -- 2.2.7 Multiple Objectives -- 2.2.8 Multiple Projects -- 2.2.9 Uncertainty -- 2.3 Solution Methods -- 2.3.1 Exact Algorithms -- 2.3.2 Priority Rule Heuristics -- 2.3.3 Metaheuristics -- 2.4 Developments in Resource-Constrained Project Scheduling -- 2.4.1 Publications -- 2.4.2 Trends -- 2.5 Conclusions -- References -- 3 Optimization for Project Scheduling -- 3.1 Introduction -- 3.2 The Importance of Project Scheduling -- 3.3 General Approaches in Project Scheduling Problem -- 3.4 Trends in Optimal Project Scheduling -- 3.5 Detailed Literature Review -- 3.5.1 Deterministic Approaches -- 3.5.2 Uncertain Approaches -- 3.6 Conclusion -- References -- 4 Optimization for Project Cost Management -- 4.1 Project Cost Management -- 4.2 General Approaches in Project Cost Management -- 4.2.1 The Time-Cost Relationship -- 4.2.2 The Time-Cost Tradeoff Curve -- 4.3 An Overview of the TCTP Studies -- 4.3.1 Publication Year -- 4.3.2 Publication Types and Publishing Outlets -- 4.3.3 Citation Network -- 4.3.4 Keyword co-Occurrence Network -- 4.4 The TCTP Mathematical Models.
4.4.1 The Discrete TCTP with Budget and Deadline Constraints -- 4.4.2 An Illustrative Example -- 4.5 Solution Techniques for the TCTP -- 4.5.1 Exact Methods -- 4.5.2 Non-exact Methods -- 4.6 Other Variants and Extensions -- 4.7 Concluding Remarks -- A.1 Appendix -- References -- 5 Time -Cost Trade-off Optimal Approaches -- 5.1 Introduction -- 5.2 General Approaches to Time-Cost Trade-off Problems -- 5.3 Optimal Approaches to Time-Cost Trade-off Problems -- 5.4 Conclusion -- References -- 6 Optimization for Project Quality Management -- 6.1 Definition of Project Quality Management -- 6.2 The Importance of Project Quality Management -- 6.3 General Approaches in Project Quality Management -- 6.4 Trends in Optimal Project Quality Management Models -- 6.5 Detailed Literature Review -- 6.5.1 Deterministic Single-Mode and Multimode Modeling Approaches -- 6.5.2 Nondeterministic Modeling Approaches -- 6.6 Conclusion -- References -- 7 Optimization for Construction Supply Chain Management -- 7.1 Introduction -- 7.2 Construction Supply Chian -- 7.3 Trends in Optimal Construction Supply Chain Network Design -- 7.4 Detailed Literature Review -- 7.5 Conclusion -- References -- 8 Optimization for Project Resource Management -- 8.1 The Definition of Project Resource Management -- 8.2 The Importance of Project Resource Management -- 8.3 Project Resources Classification -- 8.4 Heuristic Approaches in Project Resource Management -- 8.5 Optimal Project Resource Management Models -- 8.6 Need for a New Approach in Resource Management -- 8.7 Development of an Optimum Material Procurement Schedule -- 8.7.1 Step 1: Preparation of Construction Schedule -- 8.7.2 Step 2: Material Requirement Planning -- 8.7.3 Step 3: Development of the Optimization Model -- 8.7.3.1 Material Procurement Cost -- 8.7.3.2 Impact of the Shortage of Materials -- 8.7.3.3 Constraints. 8.7.4 Step 4: Optimization Process Using NSGA-II -- 8.7.5 Step 5: Development of Material Procurement Schedule -- 8.8 Benefits of Using the Developed Optimization Model for Material Procurement Schedule -- 8.9 Conclusion -- 8.9.1 Contribution -- 8.9.2 Limitation and Future Scope -- References -- 9 Project Stakeholder Management -- 9.1 Introduction -- 9.2 Trends in Construction Project Stakeholder Management -- 9.3 Detailed Review of the Literature on the Project Stakeholder Management -- 9.4 Conclusion and some Future Directions -- References -- 10 Optimization for Project Risk Management -- 10.1 Definition of Project Risk Management -- 10.2 The Importance of Project Risk Management -- 10.3 General Approaches in Project Risk Management -- 10.4 Optimal Project Risk Management Models -- 10.4.1 The Input Parameter Aspect -- 10.4.2 The Decision Variables Aspect -- 10.4.3 The Objective Function Aspect -- 10.4.4 The Constraints Aspect -- 10.4.5 Solution Techniques -- 10.5 Conclusions -- References -- Index. |
| Record Nr. | UNISA-996466558003316 |
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Golpîra Hêriş
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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