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[null,"1.","Record Nr.",null,"UNINA9910716669803321"]
[null,null,"Autore",null,"Rose William C. <1940->"]
[null,null,"Titolo",null,"The behavior of a compressible turbulent boundary layer in a shock-wave-induced adverse pressure gradient / / by William C. Rose"]
[null,null,"Pubbl/distr/stampa",null,"Washington, D.C. : , : National Aeronautics and Space Administration, , March 1973"]
[null,null,"Descrizione fisica",null,"1 online resource (vii, 229 pages) : illustrations"]
[null,null,"Collana",null,"NASA/TN ; ; D-7092"]
[null,null,"Soggetti",null,"Shock waves"]
[{"$nulls":4},"Turbulent boundary layer"]
[{"$nulls":4},"Pressure gradients"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"March 1973.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (pages 90-95)."]
[null,"2.","Record Nr.",null,"UNINA9911004718703321"]
[null,null,"Autore",null,"Chakraborty Chinmay"]
[null,null,"Titolo",null,"Smart Health Technologies for the COVID-19 Pandemic : Internet of Medical Things Perspectives"]
[null,null,"Pubbl/distr/stampa",null,"Stevenage : , : Institution of Engineering & Technology, , 2022"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-83724-474-X"]
[{"$nulls":4},"1-5231-4717-2"]
[{"$nulls":4},"1-83953-519-9"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (502 pages)"]
[null,null,"Collana",null,"Healthcare Technologies"]
[null,null,"Disciplina",null,"610.285"]
[null,null,"Soggetti",null,"Internet of things - Industrial applications"]
[{"$nulls":4},"Internet of things - Health aspects"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Title -- Copyright -- Contents -- About the editors -- Preface -- 1 Internet of Things (IoT) and blockchain-based solutions to confront COVID-19 pandemic -- 1.1 Introduction -- 1.2 Internet of Things (IoT) and blockchain overview -- 1.2.1 Internet of Things -- 1.2.2 Blockchain -- 1.3 IoT technologies to confront COVID-19 -- 1.3.1 Health monitoring systems -- 1.3.2 Tracking and detecting possible patients -- 1.3.3 Disinfecting area -- 1.3.4 Telemedicine -- 1.3.5 Logistics delivery -- 1.4 Blockchain technologies to confront COVID-19 -- 1.4.1 Contact tracing -- 1.4.2 Database security -- 1.4.3 Information sharing -- 1.4.4 Prevention of data fabrication -- 1.4.5 Internet of Medical Things -- 1.5 Challenges, solutions, and deliverables -- 1.5.1 Challenges of IoT and blockchain technology -- 1.5.2 Possible solutions and deliverables -- 1.6 Key findings and discussion -- 1.7 Conclusion and future scopes -- References -- 2 Application of big data and computational intelligence in fighting COVID-19 epidemic -- 2.1 Introduction -- 2.2 Applicability of computational intelligence in combating COVID-19 pandemic -- 2.3 Big data and analytics in battling COVID-19 outbreak -- 2.4 The limitations of using big data and computational intelligence to fight the COVID-19 pandemic -- 2.5 The practical case of using computational intelligence in fighting COVID-19 pandemic -- 2.5.1 Confusion matrix -- 2.5.2 ROC curves -- 2.5.3 Precision-recall curve -- 2.6 Conclusion "]
[{"$nulls":4},"-- References -- 3 Cloud-based IoMT for early COVID-19 diagnosis and monitoring -- 3.1 Introduction -- 3.2 Overview about COVID-19 treatments -- 3.2.1 Symptoms -- 3.2.2 Methodologies in COVID-19 diagnosis -- 3.2.3 Treatment approaches -- 3.2.4 Available vaccine -- 3.2.5 COVID-19 timeline -- 3.3 Related work -- 3.3.1 Lightweight block encryption__amp__#8211."]
[{"$nulls":4},"based secure health monitoring system for data management -- 3.3.2 Smart diagnostic/therapeutic framework for COVID-19 patients -- 3.3.3 IoT-based framework for collecting real-time symptom data using machine learning algorithms -- 3.4 Proposed methodology -- 3.4.1 Architecture of proposed IoT framework -- 3.4.2 Data acquisition using wearables devices -- 3.5 Implementation of proposed framework -- 3.6 Results and discussion -- 3.7 Conclusion and future scopes -- References -- 4 Assessment analysis of COVID-19 on the global economics and trades -- 4.1 Introduction -- 4.2 Backgrounds -- 4.3 Social impacts on finance -- 4.4 Framework for the international financial system, bionetworks, and maintainability on pandemic -- 4.4.1 Assessment strategy constructions to fight COVID-19 -- 4.4.2 Macro-finance impacts -- 4.4.3 Econometric effects: consumer preferences -- 4.4.4 Nonpositive impacts of COVID-19 -- 4.4.5 Impact of international commercial trading -- 4.4.6 COVID-19__amp__#8217 -- s effect on the aviation industry -- 4.4.7 Significant collision on the travel sector -- 4.4.8 Significant reduction in primary energy usage -- 4.4.9 Record decrease in CO2 emissions -- 4.4.10 Rise in digitalization -- 4.5 The role of circular economy -- 4.5.1 The circular economy for slowing the onset of climate collapse -- 4.5.2 Social finance system -- 4.5.3 Hurdles to CE for context of COVID-19 -- 4.6 Chances financial support after COVID-19 -- 4.6.1 Several solutions to manage hospital medical and general waste -- 4.6.2 Facilities for CE in communication sector -- 4.6.3 Use digitalization after COVID-19 -- 4.7 Conclusions -- References -- 5 Early diagnosis and remote monitoring using cloud-based IoMT for COVID-19 -- 5.1 Introduction -- 5.2 Detection techniques -- 5.3 Internet of Medical Things."]
[{"$nulls":4},"5.4 IoMT devices for the identification of COVID-19 symptoms and remote monitoring -- 5.4.1 Wearables -- 5.4.2 Smartphone applications -- 5.5 Early diagnosis of COVID-19 and remote monitoring procedures -- 5.6 Machine learning and deep learning in COVID-19 diagnosis -- 5.7 Related works -- 5.8 Experimental case study -- 5.8.1 Dataset description -- 5.8.2 Methodology -- 5.8.3 Training -- 5.8.4 Experimental setup and results -- 5.9 Measures for monitoring and tracking COVID-19 -- 5.10 Limitations of using IoMT devices -- 5.11 Conclusion and future scope -- References -- 6 Blockchain technology for secure COVID-19 pandemic data handling -- 6.1 Introduction -- 6.2 Recent developments in blockchain technology -- 6.2.1 Healthcare data systems -- 6.2.2 Healthcare data exchanges -- 6.2.3 Healthcare administration -- 6.2.4 Pharmaceuticals -- 6.3 Potential benefits of blockchain technology in data handling -- 6.3.1 Better exchange of healthcare data records -- 6.3.2 Validating trust in medical research and supplies -- 6.3.3 Validating correct billing management -- 6.3.4 Internet of Things (IoT) in healthcare -- 6.3.5 Optimized privacy and data security -- 6.4 Key challenges of blockchain technology in data handling -- 6.4.1 Security -- 6.4.2 Speed -- 6.4.3 Interoperability -- 6.4.4 Stringent data protection regulation -- 6.4.5 Scalability -- 6.4.6 Privacy -- 6.5 Prospects of blockchain technology -- 6.6 Research on blockchain technology in COVID-19 healthcare -- 6.7 Real-time analysis of COVID-19 pandemic data -- 6.7.1 The susceptible recovered infectious (SIR) model -- 6.7.2 Standard logistic regression model -- 6.7.3 Time-to-event analytics "]
[{"$nulls":4},"model -- 6.7.4 Results of major real-time analysis -- 6.8 Recommendations and future directions -- 6.9 Conclusion and future scopes -- Acknowledgments -- References -- 7 Social distancing technologies for COVID-19."]
[{"$nulls":4},"7.1 Introduction -- 7.2 Methodology -- 7.3 Social distancing technologies for education -- 7.3.1 Learning management system -- 7.3.2 Social networking and conference software for education -- 7.4 Social distancing technology in healthcare -- 7.4.1 Wearable technology -- 7.4.2 Screening system -- 7.4.3 Queue systems -- 7.4.4 Payment system -- 7.4.5 Social distancing notified people in public -- 7.5 Social distancing technology in manufacturing -- 7.5.1 Checking the distance using wearable device -- 7.5.2 Distance monitoring using Wi-Fi -- 7.5.3 Distance monitoring using video analytics -- 7.5.4 Social distancing by replacing some work with a robot -- 7.6 Social-distancing technologies for supporting everyday life -- 7.6.1 Technologies support working at home -- 7.6.2 Applications support work from home (WFH) service -- 7.6.3 Conferencing application -- 7.7 Social distancing and smart city -- 7.7.1 AI and big data -- 7.7.2 Implementation and usability -- 7.7.3 Privacy and security -- 7.7.4 Policy and legislation -- 7.8 Conclusion and future works -- References -- 8 Social health protection in touristic destinations during COVID-19 -- 8.1 Introduction -- 8.2 Related work -- 8.3 Proposal of software solution for health protection -- 8.3.1 System architecture -- 8.3.2 Healthcare service -- 8.3.3 Tourist service -- 8.3.4 Local government service -- 8.3.5 Border control -- 8.4 Data protection -- 8.5 Conclusion and future works -- References -- 9 Analysis of Artificial Intelligence and Internet of Things in biomedical imaging and sequential data for COVID-19 -- 9.1 Introduction -- 9.2 Definition of biomedical keywords -- 9.2.1 Microarray and RNA-seq data -- 9.2.2 De novo mutation -- 9.2.3 ChiP-seq data -- 9.2.4 Biomedical imaging -- 9.3 Categories of computational algorithms in biomedical data -- 9.3.1 Biomedical data analysis."]
[{"$nulls":4},"9.3.2 Array-based data analysis -- 9.3.3 Hybrid data analysis -- 9.4 Different techniques for diagnosis using biomedical imaging -- 9.4.1 Brain -- 9.4.2 Breast -- 9.4.3 Kidney -- 9.4.4 Ovary -- 9.4.5 Skin cancer -- 9.4.6 Soft tissue sarcoma -- 9.5 Comparative review of computational algorithms -- 9.6 Role of CT in COVID-19 pandemic -- 9.7 Advent of smart technologies during COVID-19 -- 9.7.1 Building ML models to diagnose COVID-19 -- 9.7.2 Impact of IoT in healthcare -- 9.8 Conclusion -- References -- 10 Review of medical imaging with machine learning and deep learning-based approaches for COVID-19 -- 10.1 Introduction -- 10.2 Literature review -- 10.2.1 Reviewed work -- 10.3 Comparative analysis of existing work -- 10.4 Research gaps -- 10.4.1 Unavailability of large datasets -- 10.4.2 Imbalanced datasets -- 10.4.3 Multiple image sources -- 10.5 Conclusion -- References -- 11 Machine-based drug design to inhibit SARS-CoV-2 virus -- 11.1 Introduction -- 11.2 What is SARS-coronavirus-2? -- 11.3 Mechanism of SARS-coronavirus-2 infection in human -- 11.4 How SARS-coronavirus-2 multiplies? -- 11.5 Human antibody generation and role of vaccine -- 11.5.1 Immediate action of human antibody -- 11.5.2 Role of synthetic vaccine on COVID-19 -- 11.6 Real-time COVID-19 identification test (RT-PCR) -- 11.6.1 Limitations of RT-PCR tool -- 11.7 Discussion on in silico methods in COVID-19 drug research -- 11.7.1 In silico-assisted anchoring site analysis -- 11.7.2 Machine-assisted designing and evaluation of COVID-19 drug -- 11.8 Machine-integrated advanced techniques for COVID-19 -- 11.8.1 Computerized tomography in COVID-19 detection -- 11.8.2 Advanced MRI for COVID-19 treatment -- 11.9 Summary -- 11.10 "]
[{"$nulls":4},"Conclusion and future scopes -- 11.10.1 Future scope -- References -- 12 Stress detection for cognitive rehabilitation in COVID-19 scenario -- 12.1 Introduction."]
[{"$nulls":4},"12.2 Related works."]
[null,null,null,"Sommario/riassunto","This edited book looks at the role technology has played to monitor, map and fight the COVID-19 global pandemic. The vital role that intelligent advanced healthcare informatics has played during this crucial time are explored, as well as e-healthcare, telemedicine, and life support systems."]