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001476170 001__ 1476170
001476170 003__ OCoLC
001476170 005__ 20231003174636.0
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001476170 019__ $$a1394115094$$a1394117825
001476170 020__ $$a9783031357152$$q(electronic bk.)
001476170 020__ $$a3031357159$$q(electronic bk.)
001476170 020__ $$z9783031357145
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001476170 0247_ $$a10.1007/978-3-031-35715-2$$2doi
001476170 035__ $$aSP(OCoLC)1395066859
001476170 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dOCLCQ
001476170 049__ $$aISEA
001476170 050_4 $$aR857.M34
001476170 08204 $$a610.28$$223/eng/20230824
001476170 24500 $$aMathematical models and computer simulations for biomedical applications /$$cGabriella Bretti, Roberto Natalini, Pasquale Palumbo, Luigi Preziosi, editors.
001476170 264_1 $$aCham :$$bSpringer,$$c2023.
001476170 300__ $$a1 online resource (viii, 257 pages) :$$billustrations (some color).
001476170 336__ $$atext$$btxt$$2rdacontent
001476170 337__ $$acomputer$$bc$$2rdamedia
001476170 338__ $$aonline resource$$bcr$$2rdacarrier
001476170 4901_ $$aSEMA SIMAI Springer series,$$x2199-305X ;$$vvolume 33
001476170 5050_ $$aIntro -- Preface -- Contents -- An Application of the Grünwald-Letinkov Fractional Derivative to a Study of Drug Diffusion in Pharmacokinetic CompartmentalModels -- 1 Introduction -- 2 Pharmacokinetic Two Compartmental Model -- 2.1 Grünwald-Letinkov Approximation for Bicompartmental Model (14) -- 2.2 Non-standard Discretization of Bicompartmental Model (14) -- 2.3 Fractional Bicompartmental Model -- 3 Bicompartmental Model with NPs Infusion -- 4 Applications of Fractional Calculus to Model Drug Diffusion in a Three Compartmental Pharmacokinetic Model -- 5 Discussion -- References
001476170 5058_ $$aMerging On-chip and In-silico Modelling for Improved Understanding of Complex Biological Systems -- 1 Introduction -- 2 The Organs-on-Chip Technology -- 2.1 Setting of the Laboratory Experiments -- 3 Mathematical Modeling of OoC -- 3.1 Macroscopic Model for CoC Experiment BBN -- 3.1.1 Interface Between 2D-1D Models in (1)-(4) -- 3.2 Hybrid Macro-Micro Model for CoC Experiment BDNPR -- 3.2.1 Function F1: Chemotactic Term -- 3.2.2 Function F2: ICs/TCs Repulsion -- 3.2.3 Function F3: ICs Adhesion/Repulsion -- 3.2.4 Friction -- 3.2.5 Function F4: Production of Chemical Signal
001476170 5058_ $$a3.2.6 Initial Conditions -- 3.2.7 Boundary Conditions -- 3.2.8 Stochastic Model -- 3.3 Future Directions: Mean-Field Limits and Nonlocal Models NP2022 -- 4 Numerical Approximation -- 4.1 Numerical Schemes for the Approximation of the Models (1)-(4) -- 4.1.1 Stability at Interfaces -- 4.2 Numerical Schemes for the Approximation of the Model (7)-(8) -- 4.2.1 Discretization of the PDE (Eq.(7)) -- 4.2.2 Boundary Conditions -- 4.2.3 Discretization of the ODE (8) -- 4.3 Discretization of the SDE (20) -- 5 Simulation Results -- 5.1 Simulation Results Obtained by Macroscopic Model
001476170 5058_ $$a5.1.1 Time Evolution of Macroscopic Densities -- 5.2 Simulation Results Obtained by Hybrid Macro-Micro Model -- 5.2.1 Scenario 1: Deterministic Motion -- 5.2.2 Scenario 2: Deterministic Motion Including Cell Death -- 5.2.3 Scenario 3: Stochastic Motion -- 6 Conclusions -- References -- A Particle Model to Reproduce Collective Migrationand Aggregation of Cells with Different Phenotypes -- 1 Introduction -- 2 Mathematical Framework and Representative Simulations -- 2.1 Cell Proliferation -- 2.2 Cell Movement -- 2.2.1 Cell Repulsive Behavior and Random Movement
001476170 506__ $$aAccess limited to authorized users.
001476170 520__ $$aMathematical modelling and computer simulations are playing a crucial role in the solution of the complex problems arising in the field of biomedical sciences and provide a support to clinical and experimental practices in an interdisciplinary framework. Indeed, the development of mathematical models and efficient numerical simulation tools is of key importance when dealing with such applications. Moreover, since the parameters in biomedical models have peculiar scientific interpretations and their values are often unknown, accurate estimation techniques need to be developed for parameter identification against the measured data of observed phenomena. In the light of the new challenges brought by the biomedical applications, computational mathematics paves the way for the validation of the mathematical models and the investigation of control problems. The volume hosts high-quality selected contributions containing original research results as well as comprehensive papers and survey articles including prospective discussion focusing on some topical biomedical problems. It is addressed, but not limited to: research institutes, academia, and pharmaceutical industries.
001476170 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 24, 2023).
001476170 650_0 $$aBiomedical engineering$$xMathematical models.
001476170 650_0 $$aBiomedical engineering$$xComputer simulation.
001476170 650_0 $$aMathematical models$$xIndustrial applications.
001476170 650_0 $$aComputer simulation$$xIndustrial applications.
001476170 655_0 $$aElectronic books.
001476170 7001_ $$aBretti, Gabriella,$$eeditor.
001476170 7001_ $$aNatalini, R.,$$eeditor.
001476170 7001_ $$aPalumbo, Pasquale,$$eeditor.
001476170 7001_ $$aPreziosi, Luigi,$$eeditor.
001476170 77608 $$iPrint version:$$aBretti, Gabriella$$tMathematical Models and Computer Simulations for Biomedical Applications$$dCham : Springer International Publishing AG,c2023$$z9783031357145
001476170 830_0 $$aSEMA SIMAI Springer series ;$$vv.33.$$x2199-305X
001476170 852__ $$bebk
001476170 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-35715-2$$zOnline Access$$91397441.1
001476170 909CO $$ooai:library.usi.edu:1476170$$pGLOBAL_SET
001476170 980__ $$aBIB
001476170 980__ $$aEBOOK
001476170 982__ $$aEbook
001476170 983__ $$aOnline
001476170 994__ $$a92$$bISE