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3D Printing in Medicine : A Practical Guide for Medical Professionals / / edited by Frank J. Rybicki, Gerald T. Grant
| 3D Printing in Medicine : A Practical Guide for Medical Professionals / / edited by Frank J. Rybicki, Gerald T. Grant |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (VIII, 138 p. 78 illus., 77 illus. in color.) |
| Disciplina | 616.0757 |
| Soggetto topico |
Radiology
Surgery Nervous system - Surgery Personal Protective Equipment Printing, Three-Dimensional COVID-19 Imaging / Radiology Neurosurgery |
| ISBN | 3-319-61924-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction.- 3D printing technologies -- Post-processing of DICOM images -- Establishing a 3D Service in your medical facility.- The 3D printing lab in a radiology practice -- Training and education of a 3D medical printing technologist -- Cranio-Maxillofacial 3D printing -- 3D printing in Neurosurgery and Neurointervention.- Cardiovascular 3D printing.- Musculoskeletal 3D printing.- Virtual Surgical applications.- Design and Fabrication of Customized Patient Devices.- 3D printing in Radiation Oncology.- FDA interests in the 3D printing of medical models and devices.- Bioprinting. |
| Record Nr. | UNINA-9910254494703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Intelligent Computing and Communication : Proceedings of ICAC 2020 / / edited by Swagatam Das, Mihir Narayan Mohanty
| Advances in Intelligent Computing and Communication : Proceedings of ICAC 2020 / / edited by Swagatam Das, Mihir Narayan Mohanty |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (713 pages) : illustrations (chiefly color) |
| Disciplina | 621.382 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Telecommunication Electric power production Computational Intelligence Artificial Intelligence Communications Engineering, Networks Electrical Power Engineering Processament digital d'imatges Intel·ligència artificial en medicina Informàtica mèdica COVID-19 |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 981-16-0695-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1.Classification and Detection of Leaves using Different Image processing Techniques -- Chapter 2. Covid-19 Detection :An Approach Using X-Ray Images and Deep Learning Techniques -- Chapter 3. Covid-19 Detection :An Approach Using X-Ray Images and Deep Learning Techniques -- Chapter 4. Realization of a vehicular robotic system using the principle of photonics -- Chapter 5. A Modified Hybrid Planar Antenna for Cognitive Radio Application -- Chapter 6. Detection of Broken and Good Medical Tablets Using Various Machine Learning Models -- Chapter 7. Lungs Nodule Prediction using Convolutional Neural Network and K-Nearest Neighbor -- Chapter 8. Quantitative Structure Activity Relationships (QSARs) Study for KCNQ Genes(Kv7) and Drug discovery -- Chapter 9. Apple fruit disease detection and classification using k-means clustering method -- Chapter 10. A Detailed Review of the Optimal Distributed Generation Placement in Smart Power Distribution Systems. |
| Record Nr. | UNINA-9910483988903321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Alternative medicine interventions for COVID-19 / / Muhammad Zia-Ul-Haq [and three others] editors
| Alternative medicine interventions for COVID-19 / / Muhammad Zia-Ul-Haq [and three others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (291 pages) |
| Disciplina | 614.592414 |
| Soggetto topico |
COVID-19 (Disease) - Alternative treatment
COVID-19 Medicina alternativa |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-67989-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- Chapter 1: Introduction to COVID-19 -- Introduction -- Origin of COVID -- Emergence and Epidemiology -- Human-Animal Interaction as Risk Factor -- Best Suitable Animal Model -- COVID and Animal Welfare -- Molecular Differences Between Different Animal Species -- Vaccines for COVID from Animals -- Conclusion -- References -- Chapter 2: Medicinal Plants as COVID-19 Remedy -- Introduction -- Existing Plants with Potential Therapeutic Applications for Coronavirus Family (SARS-CoV) -- Plants as Specific Inhibitors of HCoV Target Proteins -- Bupleurum Species -- Artemisia annua L. -- Isatis indigotica Fortune ex Lindl. -- Alcea digitata (Boiss.) Alef. -- Lycoris radiata (L'Hér.) Herb. -- Pyrrosia lingua (Thunb.) Farw. -- Houttuynia cordata Thunb. -- Torreya nucifera L. -- Lindera aggregata (Sims) Kosterm. -- Rheum palmatum L. -- Cerasus avium (L.) Moench. -- Polygonum multiflorum Thunb. -- Citrus aurantium L. -- Rubia tinctorum L. -- Onopordum acanthium L. -- Quercus infectoria G. Olivier -- Crataegus microphylla C. Koch -- Berberis integerrima Bunge -- Alnus japonica (Thunb.) Steud. -- Paulownia tomentosa (Thunb.) Steud. -- Psoralea corylifolia L. -- Tribulus terrestris L. -- Medicinal Plants as Potential Source of Natural Antiviral Agents Against COVID -19 -- Allium Sativum (Garlic) -- Azadirachta indica (Neem) -- Curcuma domestica (Turmeric, Haldi) -- Echinacea purpurea L. (Echinacea) -- Foeniculum vulgare Mill. (Fennel) -- Glycyrrhiza glabra (Licorice) -- Melissa officinalis L. (Lemon Balm) -- Mentha piperita L. (Peppermint) -- Nigella sativa L. (Black Seeds) -- Origanum vulgare L. (Oregano) -- Ocimum bacilicum L. (Basil) -- Rosmarinus officinalis L. (Rosemary) -- Salvia officinalis L. (Sage) -- Senna alexandrina Mill. (Senna Makki) -- Zingiber officinale Rosc. (Ginger) -- Limitations.
Conclusions -- References -- Chapter 3: Traditional Chinese Medicines as Possible Remedy Against SARS-CoV-2 -- Introduction -- Traditional Chinese Medical Practices -- Laws for TCM Formulation -- Antiviral Potential of Plant Metabolites -- Structure of SARS-CoV-2 -- Relevance of SARS-CoV with SARS-CoV-2 -- Entry of SARS-CoV vs. SARS-CoV-2 into Host Cell -- TCM and SARS-CoV -- TCM and SARS-CoV-2 (COVID-19) -- Most Practiced Herbs in Formulations Against COVID-19 -- Utilization Frequency of Herbs in the Formulation of (COVID-19) Decoctions -- Approved/Proposed Decoctions with Successful Clinical Trials -- Qingfei Paidu Decoction (QFPD) (Approved) -- Huashi Baidu Decoction (HBD) (Approved) -- Xiaochaihu Decoction (XCHD) -- Xuanfei Baidu Decoction (XFBD) (Approved) -- Jinhua Qinggan (JQ) (Approved) -- Huoxiang Zhengqi (HZ) Capsule -- Lianhua Qingwen (LQ) -- Xuebijing Injection (XbI) (Approved) -- Reduning Injection (RdI) -- Shufeng Jiedu Capsule (SFJC) -- Ma Xing Shi Gan Decoction (MXSGD) (Approved) -- Yin Qiao San (YQS) -- Yupingfeng San (YPFS) -- Therapeutic Logic of TCM as COVID-19 Inhibitor -- Mechanism of Direct Inhibition -- TCM Targeting ACE2 -- TCM Targeting Protease -- TCM Directly Targeting DNA Via Intercalation -- TCM Directly Targeting DNA via JAK-STAT Signaling Pathway -- TCM Targeting sEH -- Targeting VEGF -- Targeting DPP4 -- Targeting JUN -- Targeting EGFR -- Targeting IL1B -- Mechanism of Indirect Mechanisms -- Anti-inflammation and Immune Regulation to Avoid Cytokine Storm -- TCM Protect Organ Damage in COVID-19 Patients -- Regulation of Renin-Angiotensin-Aldosterone System (RAAS) and Potential TCM Targets -- Cytokine-Mediated Positive and Negative Immunity -- TCM Targeting MAPK (Cytokine) -- TCM Targeting Platinum Drug Resistance (Cytokine) -- TCM Targeting Tyrosine Kinase Resistance (Cytokine). TCM Targeting Interleukins (ILs) (Cytokine) -- TCM Targeting TNF (Cytokines) -- TCM Targeting Lymphocyte-Mediated Adaptive Immune System -- Regulation of Apoptosis and Potential TCM Targets -- TCM Targeting BCL Proteins -- TCM Targeting CASP -- 7TCM Targeting Akt or Protein Kinase B (PKB) -- Regulation of NF-κB Pathway and Potential TCM Targets -- TCM Targeting TP53 -- TCM Targeting ICAM1 -- TCM Targeting RELA -- Regulation of Arachidonic Acid Metabolism and Potential TCM Targets -- TCM Targeting cPLA2α of Arachidonic Acid Metabolism -- TCM Targeting Cyclooxygenase-2 of Arachidonic Acid Metabolism -- TCM Targeting LOX of Arachidonic Acid Metabolism -- TCM Targeting Cytochrome P450 -- TCM Targeting Leukotrienes (LTs) -- TCM Targeting CALM -- TCM Targeting HIF-1 Signaling Pathways -- TCM Targeting Endocrine Resistance -- TCM Targeting FOS -- TCM Targeting PTGS2 -- Regulation of Polyamine Metabolism and Potential TCM Targets -- TCM Targeting NOS2 and AOC1 -- TCM Targeting CCL2 -- Meta-analysis -- TCM from Chinese Combat Zone to World Combat Zone -- Shortcomings of Antiviral Medicine on COVID-19 -- Conclusions -- References -- Chapter 4: Plant-Based Natural Products: Potential Anti-COVID-19 Agents -- Introduction -- Antiviral Mechanistic Aspects of Phytochemicals -- Mechanism -- Plant Selection for Antiviral Screening -- Different Classes of Phytochemicals as Antiviral Agents -- Alkaloids -- Flavonoids -- Classification of Flavonoids -- Chalcones -- Dihydrochalcones -- Flavones -- Flavonones -- Dihydroflavonols -- Flavonol -- Isoflavonoids -- Isoflavones -- Isoflavanones -- Neoflavonoids -- Terpenoids -- Tannins -- Vitamins -- Chromones and Coumarins -- Organosulfur Compounds -- Selenium Compounds -- Miscellaneous Antiviral Phytochemicals -- Curcumin and Its Derivative -- Chlorophyllin -- Gingerols -- Chitin and Chitosan -- Anthraquinone -- Conclusion. References -- Chapter 5: Foods as First Defense Against COVID-19 -- Introduction -- Role of Nutrition and Diet in Prevention and Management of COVID-19 -- Prioritizing Nutrition Interventions During COVID-19 -- Grains -- Vegetables and Fruits -- Nuts -- Legumes -- Meat -- Milk and Milk Products -- Herbs -- Role of Vitamins in COVID-19 -- Vitamin A -- Vitamin B -- Vitamin C -- Vitamin D -- Vitamin E -- Role of Minerals in COVID-19 -- Iron -- Selenium -- Zinc -- Magnesium -- Role of Omega-3 Polyunsaturated Fatty Acids -- Nutrients for Immune System and Resilience in COVID-19 -- Nutrition in Lowering Stress and Depression in COVID-19 -- Food Choices for Diabetic Patients During COVID-19 -- Food Choices for Cardiovascular Patients During COVID-19 -- Food Choices for Cancer Patients During COVID-19 -- Food Choices for Patients with Digestive Disorders During COVID-19 -- Food Choices for Pulmonary Disease Patients During COVID-19 -- Malnutrition May Exacerbate COVID-19 -- Nutrition and Post-COVID Recovery -- Future Perspectives -- Conclusion -- References -- Chapter 6: Drugs for the Treatment of COVID-19 -- Introduction -- Dexamethasone -- Chloroquine -- Remdesivir -- Angiotensin-Converting Enzyme Inhibitors -- Spike Protein Inhibitors -- Conclusions -- References -- Chapter 7: COVID-19 Pandemic and Vaccines -- Introduction -- COVID-19 Vaccines: A Need of Time -- Coronavirus Vaccine Development: Challenges of the Past -- Vaccine Development Strategies -- Nucleic Acid Vaccines -- Protein Subunit Vaccines -- Inactivated or Live-Attenuated Virus Vaccines -- Virus Vector-Based Vaccines -- Repurposed Vaccines for COVID-19 and Off-Target Effects of Other Vaccines -- Plant-Based Vaccines -- Animal Models for SARS-CoV-2 Studies -- Cell Culture Systems for SARS-CoV-2 Studies -- Clinical and Immunological Endpoints -- Vaccine Development Landscape. Global and Equitable Distribution of Vaccines -- Vaccine Developers and Geographical Distribution -- Conclusions -- References -- Chapter 8: Updates in Vaccine Development Against COVID-19 -- Introduction -- Sinovac Biotech, China -- Conclusions -- Bibliography -- Chapter 9: COVID-19: Recent Developments in Therapeutic Approaches -- Introduction -- Morphology of SARS-CoV-2 -- Genome Organization of SARS-CoV-2 -- Replication of SARS-CoV-2 -- Transmission and Pathogenesis of SARS-CoV-2 -- Clinical Characterization of SARS-CoV-2 -- Clinical and Laboratory Diagnosis of SARS-CoV-2 -- Therapeutic Options for the Treatment of COVID-19 -- Antivirals or Immunomodulatory Drugs for COVID-19 -- Vaccine for COVID-19 -- Plasma/Serotherapy for COVID-19 -- Alternative Therapies -- Anticoagulant Therapy -- Glucocorticoids (GC) -- Stem Cell-Based Therapy -- ACE-2-Mediated Therapies -- CRISPR/Cas System -- Conclusion -- References -- Index. |
| Record Nr. | UNINA-9910483536303321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact / / edited by Praveen Agarwal, Juan J. Nieto, Michael Ruzhansky, Delfim F. M. Torres
| Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact / / edited by Praveen Agarwal, Juan J. Nieto, Michael Ruzhansky, Delfim F. M. Torres |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (635 pages) |
| Disciplina | 616.241400285 |
| Collana | Infosys Science Foundation Series in Mathematical Sciences |
| Soggetto topico |
Mathematical models
Medicine, Preventive Health promotion Mathematical Modeling and Industrial Mathematics Health Promotion and Disease Prevention COVID-19 Models matemàtics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-2450-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | PART-1 GENERAL ANALYSIS: Arni S.R. Srinivasa Rao and Steven G. Krantz:Mathematical Models for Understanding Social Distancing Measures: General Setting and Analysis -- A. Lamrani Alaoui, M. Tilioua, M. R. Sidi and P. Agarwal: Dynamical Analysis of a Caputo Fractional Order SIR Epidemic Model with General Treatment Function -- Zalimkhan Nagoev, Viktor Narozhnov, Arsen Pskhu, and Sergo Rekhviashvili: Protective Face Shield Effectiveness: Mathematical Modelling -- Jonathan Blackledge: The Biodynamics Hypothesis and its Applications for the Mathematical Modelling of Biological Growth and the Analysis of Infectious Diseases -- Saratha Sathasivam, Teoh Poh Lin and Woon Le Yu: Modelling the Dynamics of Fake News Spreading Transmission During COVID-19 Through Social Media -- Renaud Di Francesco: Agent Based Model for COVID-19 Active Propagation Through Interaction: Use Cases of Elderly Care Homes, Households in Lockdown and Large Events -- Emmanuel Afolabi Bakare: Analysis of Optimal Control AgainstAge-Structured Hepatitis-B Virus Model -- Daniele Ritelli: Generalized Logistic Equations in COVID Epidemic Models -- Mohd Aman and Bushra Miftah:Impact on the Transition of Shared Mobility in Metro Cities in India: A Challenge Post-Lockdown of COVID-19 -- Abdelmajid Belafhal, S. Chib and Talha Usman: Analysis of COVID-19 Virus Spreading Statistics by the Use of a New Modified Weibull Distribution -- Redouane Qesmi and Aayah Hammoumi: From Finite to Infinite Dimensional Epidemic Models for COVID-19: Lockdown and Lifting Control Measures -- Malú Grave, Rodrigo Bird Burgos, Lisandro Lovisolo and Americo Cunha Jr: Data-Driven Graphical Analysis of Epidemic Trends -- Hoang Anh Ngo, Thai Nam Hoang and Mehmet Dik: Introduction to the Grey Systems Theory and Its Application in Mathematical Modelling and Pandemic Prediction of COVID-19 -- Mohammad Ali Akour and Shikhar Kumar Sarma: Developing an Intelligent Health Report Generating System -- Nasser Al-Salti, Ibrahim M. ELmojtaba, Maryam AlYahyai, Jaqueline Mesquita and Dayse Pastore: Recent Developments in the Mathematical Modelling and Analysis of Infectious Disease Problems (COVID-19) -- Americo Cunha Jr, Adriano Cortês, Roberto Velho and Rebecca E. Morrison: Uncertainty Quantification in Epidemiological Compartmental Models -- Jorge Fernando Camacho and Cruz Vargas-De-León: Conserved Quantities in Epidemic Systems for the Spread of the Coronavirus Disease -- Rashmi Bhardwaj and Debabrata Datta: Development of Epidemiological Modeling RD_COVID-19 of Coronavirus Infectious Disease and its Numerical Simulation -- Anka Trajkovska Petkoska and Anita Trajkovska: Mediterranean Diet for Boosting Immunity -- Jochen Merker: Rate-Dependent Tipping Phenomena in Compartment Models of Epidemics -- Olumuyiwa James Peter, Tolulope Latunde and Adejimi Adesola Adeniji: Mathematical Modelling and Analysis for Prediction of COVID-19 Transmission and Control -- Alexander Kreinin, Oleksandr Romanko, Rafael Mendoza, Hamed Rasouli, and Curt Burmeister: Analysis of Impact of COVID-19 Pandemic on Financial Markets -- Ferenc A. Bartha, Ferenc A. Bartha, Janos Karsai, Gergely Rost and Tamas Tekeli: The Effects of Symptom-based Testing -- Altaf H. Khan: Challenges in Modeling of an Outbreak’s Prediction, Forecasting and Decision Making for Policy Makers -- PART-2 COUNTRY-SPECIFIC ANALYSIS: Iván Area, Juan J. Nieto, Cristiana J. Silva and Delfim F. M. Torres: Optimal Control of COVID-19: A Case Study in Portugal -- Ludmila Kirianova, Temirkhan Aleroev and Vladimir Griguletskiy: The Fractional Diffusion Equation as a Mathematical Model of Morbidity and Mortality Rates in the Russian Federation -- Juan E. Santos, Jose M. Carcione, Gabriela B. Savioli and Patricia M. Gauzellino: An SEIR Epidemic Model of Fractional Order to Analyze the Evolution of the COVID-19 Epidemic in Argentina -- Su Yean Teh and Hock Lye Koh: Modeling Effectiveness of Partial Lockdown in Breaking COVID-19 Transmission Chain in Malaysia -- G. Bhuju, G. R. Phaijoo and D. B. Gurung: Mathematical Analysis of COVID-19 in Nepal -- Subhas Khajanchi, Kankan Sarkar and Sandip Banerjee: Transmission Dynamics of the COVID-19 Outbreak and Implementation of Intervention Strategies in India -- Alya Saif Al Shehhia, Abdessamad Tridanea, Sabir Bin Muzaffarb and Gulfaraz Khan: Systematic Review of the Mathematical Modeling of Coronaviruses in the Middle East -- Hasan Soyler: Dynamic Simulation and Scenario Analysis of Spreading COVID-19 Pandemic in Turkey. |
| Record Nr. | UNINA-9910502987903321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applications of Artificial Intelligence in COVID-19 / / edited by Sachi Nandan Mohanty, Shailendra K. Saxena, Suneeta Satpathy, Jyotir Moy Chatterjee
| Applications of Artificial Intelligence in COVID-19 / / edited by Sachi Nandan Mohanty, Shailendra K. Saxena, Suneeta Satpathy, Jyotir Moy Chatterjee |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (593 pages) |
| Disciplina | 610.285 |
| Collana | Medical Virology: From Pathogenesis to Disease Control |
| Soggetto topico |
Virology
Epidemiology Artificial intelligence Artificial Intelligence COVID-19 Intel·ligència artificial en medicina |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-15-7317-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1.Comprehensive Claims of AI for Healthcare Applications-Coherence towards COVID-19 -- Chapter 2. Artificial Intelligence based systems for combating COVID-19 -- Chapter 3. Artificial intelligence mediated medical diagnosis of COVID-19 -- Chapter 4. AI combined with medical imaging enables rapid diagnosis for COVID-19 -- Chapter 5. Role of Artificial Intelligence in COVID-19 prediction based on Statistical Methods -- Chapter 6. Data Driven symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Development for COVID 19 -- Chapter 7. A decision support System using Rule based Expert System For COVID -19 Prediction and Diagnosis -- Chapter 8. A Predictive Mechanism to Intimate the Danger of Infection via nCOVID-19 through Unsupervised Learning -- Chapter 9. AI-enabled prognosis technologies for SARS Co-2. Chapter 10. Intelligent agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 analysis of Recovery, Infectious Patients -- Chapter 11. Epidemic Analysis of COVID 19 Using Machine Learning -- Chapter 12. Machine learning application in COVID-19 drug development -- Chapter 13. COVID 19 Epidemic Analysis Using Linear and Polynomial Regression Approach -- Chapter 14. Prediction & Analysis of outbreak of COVID-19 Pandemic Using Machine Learning -- Chapter 15. Predictive Risk Analysis by using Machine Learning during Covid-19 -- Chapter 16. Analysis and Validation of Risk Prediction by Stochastic Gradient Boosting Along With Recursive Feature Elimination for COVID-19 -- Chapter 17. Artificial intelligence in mental healthcare during COVID-19 pandemic -- Chapter 18. Effect of Covid-19 on Autism Spectrum Disorder: Prognosis, diagnosis and therapeutics based On AI -- Chapter 19. Use of mobile phone apps for contact tracing to control the COVID-19 pandemic: A Literature Review -- Chapter 20. Role of IoT and Social Networking in Mental Healthcare of Transgender Community in Covid-19 Pandemic -- Chapter 21. TECHNOLOGY ACCEPTANCE AND USE OF IOT DURING COVID 19 PANDEMIC-CASE STUDY OF HEALTH SECTOR IN INDIA. Chapter 22. Artificial Intelligence – The Strategies used in COVID-19 for Diagnosis -- Chapter 23. Impact of Isolation and Quarantine on Covid-19 Patients and Potential Role of Technology in Mitigation -- Chapter 24. Impact of loneliness and Quarantine on COVID-19 patients with artificial intelligence applications -- Chapter 25. Can Technology fight the loneliness Lockdown: A study of factors Affecting Loneliness in NCR during COVID 19 -- Chapter 26. Psycho-economic Impact of Obligatory Job Switching during Covid-19 Pandemic: A Study of Hawkers in Bhubaneswar (India) -- Chapter 27. AI’s Role in Essential Commodities during a Pandemic Situation -- Chapter 28.Impact of COVID-19 on Manufacturing and Operational Ecosystem in India -- Chapter 29. Impact of Repatriated Migrants on the Production Possibility of Agricultural Sector owing to Covid: A Study on the basis of Inferential Statistics -- Chapter 30. Nicotine in Covid-19: Friend or Foe”;? -- Chapter 31. Artificial Intelligence in Covid’19: Application and Legal Conundrums. |
| Record Nr. | UNINA-9910502987803321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence for COVID-19 / / edited by Diego Oliva, Said Ali Hassan, Ali Mohamed
| Artificial Intelligence for COVID-19 / / edited by Diego Oliva, Said Ali Hassan, Ali Mohamed |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (585 pages) |
| Disciplina | 610.285 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Computational intelligence
Biomedical engineering Medical informatics Engineering - Data processing Computational Intelligence Biomedical Engineering and Bioengineering Health Informatics Data Engineering COVID-19 Intel·ligència artificial en medicina Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-69744-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Simulation of the relation between the number of COVID-19 death cases as a result of the number of handwashing facilities by using artificial intelligence -- Big data and big data analytics for COVID-19 epidemic -- Big data in COVID-19 assistance – Concepts, motivations, advances and applications in real-world -- Two Layer Hybrid Scheme of IMO and PSO for Optimization of Local Aligner: COVID-19 as a case study -- Factors affecting medical mask purchase decision in COVID-19 pandemic -- Classification approach for COVID-19 gene based on Harris hawks optimization. . |
| Record Nr. | UNINA-9910495348203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence in Covid-19 / / edited by Niklas Lidströmer, Yonina C. Eldar
| Artificial Intelligence in Covid-19 / / edited by Niklas Lidströmer, Yonina C. Eldar |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (346 pages) |
| Disciplina |
362.1962414
362.19624140028563 |
| Collana | Medicine Series |
| Soggetto topico |
Medicine
Artificial intelligence Virology Immunology Epidemiology Clinical Medicine Artificial Intelligence COVID-19 Epidèmies Processament de dades Informàtica mèdica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-08506-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 AI and Pooling Tests for COVID-19 -- 2 AI for Drug Repurposing in the Pandemic Response -- 3 AI and Point Of Care Image Analysis For COVID-19 -- 4 Machine Learning and Laboratory Values in the Diagnosis, Prognosis and Vaccination Strategy of COVID-19 -- 5 AI and the Infectious Medicine of COVID-19 -- 6 AI and ICU Monitoring in COVID-19 -- 7 Symptom Based Detection Models of COVID-19 Infection Using AI -- 8 AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice -- 9 Regulatory Aspects on AI and Pharmacovigilance for COVID-19 -- 10 AI and the Clinical Immunology / Immunoinformatics for COVID-19 -- 11 AI, Epidemiology and Public Health in the Covid Pandemic -- 12 AI and Dynamic Sepsis Prediction in Covid-19. |
| Record Nr. | UNINA-9910624309003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Atlas of chest imaging in COVID-19 patients / / Jinxin Liu, Xiaoping Tang, Chunliang Lei, editors
| Atlas of chest imaging in COVID-19 patients / / Jinxin Liu, Xiaoping Tang, Chunliang Lei, editors |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XII, 192 p. 165 illus., 6 illus. in color.) |
| Disciplina | 617.540754 |
| Soggetto topico |
Chest - Imaging
COVID-19 Imatges mèdiques |
| Soggetto genere / forma |
Atles (Científic)
Llibres electrònics |
| ISBN | 981-16-1082-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Overview of COVID-19 Pneumonia -- Common CT Features of COVID-19 Pneumonia -- CT Features of Early COVID-19 Pneumonia (PCR-positive) -- CT features of intermediate stage of COVID-19 Pneumonia -- CT Features of late stage of COVID-19 Pneumonia -- Chest Features of Severe and Critical Patients with COVID-19 Pneumonia -- Role of CT and CT features of Suspected COVID-19 Patients (PCR negative) -- Follow-up CT of Patients with First Negative CT but Positive PCR for COVID-19 -- Imaging Analysis of Family Clustering -- Residual CT Features in Recovery stage of COVID-19 Pneumonia. |
| Record Nr. | UNINA-9910483011603321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cardiovascular Complications of COVID-19 : Acute and Long-Term Impacts / / edited by Maciej Banach
| Cardiovascular Complications of COVID-19 : Acute and Long-Term Impacts / / edited by Maciej Banach |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Humana, , 2022 |
| Descrizione fisica | 1 online resource (434 pages) |
| Disciplina |
616.980214
614.592414 |
| Collana | Contemporary Cardiology |
| Soggetto topico |
Cardiology
Malalties cardiovasculars COVID-19 |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 9783031154782 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Introduction -- Biology of SARS-CoV-2 coronavirus; origin, structure, and variants -- Epidemiology of COVID-19 -- Clinical symptoms and course of COVID-19 -- Risk factors of developing COVID-19 and its severe course -- Prognosis in COVID-19 patients - statistics, risk factors -- Part II: Cardiovascular Implications of the Acute Phase of COVID-19 -- Myocardial injury in COVID-19 (epidemiology, influence on prognosis, pathogenesis, treatment) -- Acute coronary syndrome – destabilization of atherosclerotic plaque in COVID-19 (epidemiology, influence on prognosis, pathogen-esis and treatment) -- Acute Vascular Injury in COVID-19 -- Heart failure and acute circulatory failure in COVID-19 (epidemiology, influence on prognosis, pathogenesis, treatment) -- Cardiomyopathy in COVID-19 (epidemiology, influence on prognosis, pathogenesis, treatment) -- Arrhythmias in COVID-19 -- Thromboembolic events in COVID-19 -- Stroke in COVID-19 -- Pulmonary embolism after COVID-19 (epidemiology, influence on prognosis, pathogenesis, treatment) -- Interaction of anti-COVID-19 drugs with cardiovascular therapy -- Part III: Influence of the Treatment of Cardiovascular Diseases on the Course of COVID-19 -- Beyond the vaccines-Bioactive lipids in COVID-19 -- Statins and COVID-19 (mechanism of action, effect on prognosis) -- Covid-19 And Antihypertensive Treatment -- Colchicine in COVID-19 (mechanism of action, effect on prognosis) -- Antiplatelet drugs in COVID-19: mechanism of action and effect on prognosis -- Antidiabetic drugs in COVID-19 -- Part IV: Cardiovascular Complications of Long-COVID-19 -- Long COVID-19 - Definition, Epidemiology, and Clinical Implications -- Cardiovascular complications of long COVID-19 - prevalence, diagnosis and risk factors -- Cardiovascular complications of Long-COVID – management -- Part V The impact of COVID-19 pandemic on the e-services and digital tools development in medicine -- The impact of the COVID-19 pandemic on e-services and digital tools development in medicine. |
| Record Nr. | UNINA-9910637709603321 |
| Cham : , : Springer International Publishing : , : Imprint : Humana, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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