LEADER 03702oam 22005294a 450 001 9910494646303321 005 20211014023344.0 010 $a963-386-132-2 035 $a(CKB)3710000001353837 035 $a(MiAaPQ)EBC4875990 035 $a(OCoLC)986942284 035 $a(MdBmJHUP)muse56988 035 $a(EXLCZ)993710000001353837 100 $a20160812d2016 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aTotalitarian societies and democratic transition $eEssays in memory of Victor Zaslavsky /$fedited by Tommaso Piffer and Vladislav Zubok 210 1$aNew York :$cCentral European University Press,$d2016. 210 3$aBaltimore, Md. :$cProject MUSE,$d2017 210 4$d©2016. 215 $a1 online resource (437 pages) 311 $a963-386-130-6 320 $aIncludes bibliographical references and index. 327 $aTommaso Piffer and Vladislav Zubok, Introduction -- Part I: Theory and Debate -- Peter Baehr, Movement, Formation, and Maintenance in the Soviet Union : Victor Zaslavsky's Challenge to the Arendtian Theory of Totalitarianism -- Giovanni Orsina, European Liberalism in the Age of Totalitarianism -- Vittorio Strada, Totalitaranism avant la lettre -- Vladimir Tismaneanu, Totalitarianism and Ideological Hubris -- Emilio Gentile, From Facts to Words : From Militia Party to Fascist Totalitarianism -- Part II: History and Society -- Vladimir Pechatnov, Stalin the Statesman : A Historian's Notes -- Oleg Khlevniuk, Stalin's Dictatorship : Priorities, Policies, and Results -- Andrea Graziosi, The "National Question" in the Soviet Union -- Inessa Yazhborovskaia, The Katyn Case : History and Articulation of Official Discourse in Russia -- David Holloway, Totalitarianism and Science : The Nazi and the Soviet Experience -- Maria Teresa Giusti, From Fascism to Communism : The History of a Conversion -- Part III: Beyond Totalitarianism -- Veljco Vujacic, Aleksandr Solzhenitsyn and Vasily Grossman : Slavophile and Westernizer Against the Totalitarian Soviet State -- Antonella d'Amelia, "Without the free word, there are no free people" : Lydia Chukovskaya's Writings on Terror and Censorship -- Lev Gudkov, The Transition from Totalitarianism to Authoritarianism in Russia -- Gail Lapidus, Totalitarianism, Nationalism, and Challenges for Democratic Transition -- Mark Kramer, Public Memory and the Difficulty of Overcoming the Communist Legacy : Poland and Russia in Comparative Perspective. 330 2 $a"Originally published on the occasion of the second anniversary of the death of Prof. Victor Zaslavsky. The book deals with the theory and the history of totalitarian society with a comparative approach. Consists of three sections: Theory and debate; History and society; Beyond Totalitarianism. The authors are among the leading European, American and Russian scholars"--Provided by publisher. 606 $aPost-communism$zRussia (Federation) 606 $aTotalitarianism$xPhilosophy 606 $aTotalitarianism$xHistory 607 $aRussia (Federation)$xPolitics and government 607 $aSoviet Union$xPolitics and government 608 $aElectronic books. 615 0$aPost-communism 615 0$aTotalitarianism$xPhilosophy. 615 0$aTotalitarianism$xHistory. 676 $a320.53 702 $aZaslavsky$b Victor$f1937-2009, 702 $aZubok$b V. M$g(Vladislav Martinovich), 702 $aPiffer$b Tommaso 801 0$bMdBmJHUP 801 1$bMdBmJHUP 906 $aBOOK 912 $a9910494646303321 996 $aTotalitarian societies and democratic transition$92478013 997 $aUNINA LEADER 09112nam 22006375 450 001 9910739416303321 005 20251009101923.0 010 $a9783031357152 010 $a3031357159 024 7 $a10.1007/978-3-031-35715-2 035 $a(MiAaPQ)EBC30702961 035 $a(Au-PeEL)EBL30702961 035 $a(DE-He213)978-3-031-35715-2 035 $a(PPN)272263761 035 $a(CKB)27991721400041 035 $a(OCoLC)1395066859 035 $a(EXLCZ)9927991721400041 100 $a20230816d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMathematical Models and Computer Simulations for Biomedical Applications /$fedited by Gabriella Bretti, Roberto Natalini, Pasquale Palumbo, Luigi Preziosi 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (261 pages) 225 1 $aSEMA SIMAI Springer Series,$x2199-305X ;$v33 311 08$aPrint version: Bretti, Gabriella Mathematical Models and Computer Simulations for Biomedical Applications Cham : Springer International Publishing AG,c2023 9783031357145 327 $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 -- Merging 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 -- 3.2.6 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 -- 5.1.1 Time Evolution of Macroscopic Densities -- 5.2 Simulation Results Obtained by Hybrid Macro-Micro Model. 327 $a5.2.1 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 -- 2.2.2 Phenotypic-Related Cell Behavior -- 3 Model Application: Wound Healing Assay -- 4 Conclusions -- References -- Modelling HIF-PHD Dynamics and Related Downstream Pathways -- 1 Introduction -- 2 HIFs and PHDs -- 2.1 Equilibrium States -- 2.2 The Limit ?0 -- 2.3 The Anoxic Limit -- 2.4 HIF-PHD Dynamics -- 3 Hypoxia and Inflammation -- 3.1 HIF-Alarmin-NFkB Dynamics -- 3.2 HIF-Interleukine Dynamics -- 4 Modelling Other HIF-Related Downstream Pathways -- 4.1 HIF and Metabolism -- 4.2 HIF and pH -- 4.3 HIF and Cell Cycle -- 4.4 HIF and ECM-Stiffening -- 4.5 HIF and VEGF -- 4.6 HIF and High Altitude -- References -- An Imaging-Informed Mechanical Framework to Providea Quantitative Description of Brain Tumour Growthand the Subsequent Deformation of White Matter Tracts -- 1 Introduction -- 2 A Multiphase Model for Brain Tumour Growth -- 2.1 Eulerian Formulation -- 2.1.1 Balance Equations -- 2.1.2 Stress Tensor and Constitutive Equations -- 2.1.3 Nutrients -- 2.1.4 Diffusion Tensor D and Preferential Directions Tensor A -- 2.1.5 Interface Conditions at the Boundary Between the Tumour and the Healthy Tissue -- 2.2 Lagrangian Formulation of the Model -- 3 Numerical Implementation -- 3.1 Weak Formulation of the Lagrangian Model -- 3.2 Discrete Formulation of the Continuous Variational Problems -- 3.3 Parameters Estimation -- 3.4 Mesh Preparation -- 4 Numerical Simulations in the Brain. 327 $a5 Conclusions and Future Developments -- References -- A Multi-Scale Immune System Simulator for the Onset of Type2 Diabetes -- 1 Introduction -- 2 Mathematical Models -- 2.1 The Model of Metabolism -- 2.2 The Hormonal Glucagon/Insulin Model -- 2.3 The Model of the Physical Exercise -- 2.4 The Model of Food Intake, Stomach Emptying and Macronutrient Absorption -- 2.5 Modeling Total Daily Energy Balance and Body Weight -- 2.6 Modeling the Effect of a Calorie Excess on the Adipocytes -- 2.7 The Model of IL-6 Release -- 2.8 The Model of Inflammation -- 3 Results -- 3.1 Setting the Parameters for the Glucagon/Insulin Model -- 3.2 Simulating Different Lifestyle Scenarios -- 4 Discussion and Conclusions -- References -- Molecular Fingerprint Based and Machine Learning Driven QSAR for Bioconcentration Pathways Determination -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Processing -- 2.2 Machine Learning Models -- 2.2.1 Extreme Gradient Boosting -- 2.2.2 Support Vector Machines -- 2.2.3 Neural Networks -- 2.2.4 Spiking Neural Networks -- 3 Results -- 4 Discussion -- 5 Conclusions -- Appendix -- Author contributions -- References -- Advanced Models for COVID-19 Variant Dynamicsand Pandemic Waves -- 1 Introduction -- 2 Description of Data -- 3 Drivers of Case Count -- 4 Data Analysis -- 4.1 Computation of ``Switching Time'' -- 4.2 Days Between Variants Dominance and Cases Peak -- 4.3 Comparing the Trend of Variant Progression with Cases Progression -- 5 Modeling a Virus with Mutation -- 5.1 Epidemiological Modeling -- 5.2 Definition of MC-ODE System -- 5.3 Simulations -- 6 Discussion -- References -- Multifractal Spectrum Based Classification for Breast Cancer -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Patient-Based Breast Cancer Identification -- 4.1 Image Processing -- 4.2 Fractal Dimension -- 4.3 Multifractal Spectrum. 327 $a5 Experiments and Results -- 5.1 The Extended Dataset: Structure and Preprocessing -- 5.2 Classification Results -- 5.3 Discussion -- 6 Conclusions -- References. 330 $aMathematical modelling and computer simulations are playing a crucial role in the solution of the complex problems arising in the field of biomedical sciences and provide a support to clinical and experimental practices in an interdisciplinary framework. Indeed, the development of mathematical models and efficient numerical simulation tools is of key importance when dealing with such applications. Moreover, since the parameters in biomedical models have peculiar scientific interpretations and their values are often unknown, accurate estimation techniques need to be developed for parameter identification against the measured data of observed phenomena. In the light of the new challenges brought by the biomedical applications, computational mathematics paves the way for the validation of the mathematical models and the investigation of control problems. The volume hosts high-quality selected contributions containing original research results as well as comprehensive papers and survey articles including prospective discussion focusing on some topical biomedical problems. It is addressed, but not limited to: research institutes, academia, and pharmaceutical industries. 410 0$aSEMA SIMAI Springer Series,$x2199-305X ;$v33 606 $aMathematics 606 $aMathematics$xData processing 606 $aApplications of Mathematics 606 $aComputational Mathematics and Numerical Analysis 615 0$aMathematics. 615 0$aMathematics$xData processing. 615 14$aApplications of Mathematics. 615 24$aComputational Mathematics and Numerical Analysis. 676 $a511.8 676 $a511.8 700 $aBretti$b Gabriella$01332373 701 $aNatalini$b Roberto$01424052 701 $aPalumbo$b Pasquale$01424053 701 $aPreziosi$b Luigi$032041 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739416303321 996 $aMathematical Models and Computer Simulations for Biomedical Applications$93552897 997 $aUNINA