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1. |
Record Nr. |
UNINA9910811373203321 |
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Autore |
Matthiessen Peter |
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Titolo |
Sal si puedes : cesar chavez and the new american revolution / / Peter Matthiessen ; with a new foreword by Marc Grossman |
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Pubbl/distr/stampa |
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Berkeley, California : , : University of California Press, , 2000 |
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©2014 |
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ISBN |
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Descrizione fisica |
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1 online resource (416 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Labor unions - United States |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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Front matter -- Foreword -- Foreword -- Introduction -- Epilogue -- Postscript |
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Sommario/riassunto |
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In the summer of 1968 Peter Matthiessen met Cesar Chavez for the first time. They were the same age: forty-one. Matthiessen lived in New York City, while Chavez lived in the Central Valley farm town of Delano, where the grape strike was unfolding. This book is Matthiessen's panoramic yet finely detailed account of the three years he spent working and traveling with Chavez, including to Sal Si Puedes, the San Jose barrio where Chavez began his organizing. Matthiessen provides a candid look into the many sides of this enigmatic and charismatic leader who lived by the laws of nonviolence. Sal Si Puedes is less reportage than living history. In its pages a whole era comes alive: the Chicano, Black Power, and antiwar movements; the browning of the labor movement; Chavez's fasts; the nationwide boycott of California grapes. When Chavez died in 1993, tens of thousands gathered at his funeral. It was a clear sign of how beloved he was and how important his life had been. A new foreword by Marc Grossman considers the significance of Chavez's legacy for our time. As well as serving as an indispensable guide to the 1960's, this book rejuvenates the extraordinary vitality of Chavez's life and spirit, giving his message a renewed and much-needed urgency. |
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2. |
Record Nr. |
UNINA9911004718703321 |
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Autore |
Chakraborty Chinmay |
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Titolo |
Smart Health Technologies for the COVID-19 Pandemic : Internet of Medical Things Perspectives |
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Pubbl/distr/stampa |
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Stevenage : , : Institution of Engineering & Technology, , 2022 |
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©2022 |
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ISBN |
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1-83724-474-X |
1-5231-4717-2 |
1-83953-519-9 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (502 pages) |
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Collana |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Internet of things - Industrial applications |
Internet of things - Health aspects |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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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 -- 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. |
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. |
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 |
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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 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. |
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. |
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 |
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-- 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 Conclusion and future scopes -- 11.10.1 Future scope -- References -- 12 Stress detection for cognitive rehabilitation in COVID-19 scenario -- 12.1 Introduction. |
12.2 Related works. |
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Sommario/riassunto |
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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. |
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