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[null,"1.","Record Nr.",null,"UNINA9910458882203321"]
[null,null,"Autore",null,"Chadwick Henry <1920-2008.>"]
[null,null,"Titolo",null,"Augustine [[electronic resource] ] : a very short introduction / / Henry Chadwick"]
[null,null,"Pubbl/distr/stampa",null,"Oxford ; ; New York, : Oxford University Press, 2001"]
[null,null,"ISBN",null,"9786613097682"]
[{"$nulls":4},"0-19-154047-1"]
[{"$nulls":4},"1-283-09768-0"]
[null,null,"Descrizione fisica",null,"1 online resource (134 p.) : ill., facsim., ports"]
[null,null,"Collana",null,"Very short introductions"]
[null,null,"Disciplina",null,"189/.2"]
[null,null,"Soggetti",null,"Electronic books."]
[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","Originally published: 1986."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,null,"Sommario/riassunto","By his writings, the surviving bulk of which exceed that of any other ancient author, Augustine came to influence not only his contemporaries but also modern theologists. This book gives a lucid introduction into Augustine's influential thoughts."]
[null,"2.","Record Nr.",null,"UNINA9910778638103321"]
[null,null,"Autore",null,"Intrinsic Remediation Committee"]
[null,null,"Titolo",null,"Natural attenuation for groundwater remediation [[electronic resource] /] / Committee on Intrinsic Remediation, Water Science and Technology Board [and] Board on Radioactive Waste Management, Commission on Geosciences, Environment, and Resources"]
[null,null,"Pubbl/distr/stampa",null,"Washington, D.C., : National Academy Press, c2000"]
[{"$nulls":4},"Washington, D.C. : , : National Academy Press, , 2000"]
[null,null,"ISBN",null,"0-309-13280-0"]
[{"$nulls":4},"0-309-51645-5"]
[null,null,"Descrizione fisica",null,"1 online resource (288 p.)"]
[null,null,"Disciplina",null,"628.1/68"]
[null,null,"Soggetti",null,"Hazardous wastes - Natural attenuation - Evaluation"]
[{"$nulls":4},"In situ bioremediation - Evaluation"]
[{"$nulls":4},"Hazardous waste site remediation - Evaluation"]
[{"$nulls":4},"Groundwater - Purification"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"\"\"Front Matter\"\"; \"\"Preface\"\"; \"\"Contents\"\"; \"\"Executive Summary\"\"; \"\"Introduction: Using Natural Processes in Groundwater Restoration 1\"\"; \"\"Community Concerns About Natural Attenuation 2\"\"; \"\"Scientific Basis for Natural Attenuation 3\"\"; \"\"Approaches for Evaluating Natural Attenuation 4\"\"; \"\"Protocols for Documenting Natural Attenuation 5\"\"; \"\"A Acronyms\"\"; \"\"B Presenters at the Committeeâ€?s Information-Gathering Meetings\"\"; \"\"C Biographical Sketches of Committee Members and Staff\"\"; \"\"Index\"\""]
[null,null,null,"Sommario/riassunto","In the past decade, officials responsible for clean-up of contaminated groundwater have increasingly turned to natural attenuation -- essentially allowing naturally occurring processes to reduce the toxic potential of contaminants -- rather than engineered solutions. This saves both money and headaches. To the people in surrounding communities, though, it can appear that clean-up officials are simply walking away from contaminated sites.When is natural attenuation the appropriate approach to a clean-up? This book presents the consensus of a diverse committee, informed by the views of researchers, regulators, and community activists. The committee reviews the likely effectiveness of natural attenuation with different classes of "]
[{"$nulls":4},"contaminants -- and describes how to evaluate the \"footprints\" of natural attenuation at a site to determine whether natural processes will provide adequate clean-up. Included are recommendations for regulatory change.The book also emphasizes the importance of the public's belief and attitudes toward remediation and provides guidance on involving community stakeholders throughout the clean-up process."]
[null,"3.","Record Nr.",null,"UNINA9911006840803321"]
[null,null,"Autore",null,"Jain Vishal"]
[null,null,"Titolo",null,"Healthcare Monitoring and Data Analysis Using IoT : Technologies and Applications"]
[null,null,"Pubbl/distr/stampa",null,"Stevenage : , : Institution of Engineering & Technology, , 2022"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-83724-472-3"]
[{"$nulls":4},"1-5231-4666-4"]
[{"$nulls":4},"1-83953-438-9"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (427 pages)"]
[null,null,"Collana",null,"Healthcare Technologies"]
[null,null,"Disciplina",null,"616.028"]
[null,null,"Soggetti",null,"Patient monitoring - Technological innovations"]
[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 -- Contents -- About the editors -- Preface -- Acknowledgments -- 1. COVID-19 pandemic analysis using application of AI | Pawan Whig, Rahul Reddy Nadikattu and Arun Velu -- 1.1 Introduction -- 1.2 Literature survey -- 1.3 Dataset used for analysis -- 1.4 Various machine learning libraries -- 1.5 Training and testing -- 1.6 Bias and variance -- 1.7 Result -- 1.8 Conclusion -- References -- 2. M-health: a revolution due to technology in healthcare sector | Mayuri Diwakar Kulkarni, Ashish Suresh Awate and Jyotir Moy Chatterje -- 2.1 Introduction -- 2.2 Discussion -- 2.3 Conclusion and future work -- References -- 3. Analysis of Big Data in electroencephalography (EEG) | Sagar Motdhare, Garima Mathur and Ravi Kant -- 3.1 Introduction -- 3.2 Methodology -- 3.3 EEG signal recording -- 3.4 Activity/action of EEG -- 3.5 EEG applications -- 3.6 Mathematical model -- 3.7 Across the boundaries of small sample sizes -- 3.8 EEG signal analytics and seizure analysis -- 3.9 EEG digital video -- 3.10 EEG data storage and its management -- 3.11 Big Data in epileptic EEG analysis -- 3.12 Conclusion -- 3.13 Future scope -- References -- 4. An analytical study of COVID-19 outbreak | Shipra Gupta, Vijay Kumar, P. Patil and Lajwanti Kishnani -- 4.1 Introduction -- 4.2 Review of literature -- 4.3 Method -- 4.4 Results -- 4.5 Discussions -- 4.6 Precautions -- 4.7 Conclusions and future scope -- Acknowledgment -- References -- 5. IoT-based smart healthcare monitoring system | Hakan Yuksel -- 5.1 Introduction -- 5.2 Related work -- 5.3 Proposed method -- 5.4 Result "]
[{"$nulls":4},"and discussion -- 5.5 Conclusion and future scope -- References -- 6. Development of a secured IoMT device with prioritized medical information for tracking and monitoring COVID patients in rural areas | P.K. Jawahar, K. Indragandhi, G. Kannan and Yiu-Wing Leung -- 6.1 Introduction."]
[{"$nulls":4},"6.2 Security threats in IoMT -- 6.3 Introduction to COVID-19 -- 6.4 Proposed system architecture -- 6.5 Conclusion and future scope -- References -- 7. An IoT-based system for a volumetric estimation of human brain morphological features from magnetic resonance images | S.N. Kumar, A. Lenin Fred, L.R. Jonisha Miriam, H. Ajay Kumar, I. Christina Jane, Parasuraman Padmanabhan and Balazs Gulyas -- 7.1 Introduction -- 7.2 Materials and methods -- 7.3 Results and discussion -- 7.4 Conclusion and future scope -- Acknowledgments -- References -- 8. Healthcare monitoring through IoT: security challenges and privacy issues | S.O. Owoeye, A.S. Akinade, K.I. Adenuga and F.O. Durodola -- 8.1 Introduction -- 8.2 IoT applications in personalized healthcare -- 8.3 Challenges of IoT in personalized healthcare -- 8.4 Security of IoT in personalized healthcare -- 8.5 Privacy -- 8.6 Conclusion and future scope -- References -- 9. E-health natural language processing | Saman Hina, Raheela Asif and Pardeep Kumar -- 9.1 Unstructured datasets for E-health NLP research -- 9.2 Annotation challenges dealing with health-care corpora -- 9.3 NLP methods that can be adopted to tackle semantics for medical text analysis -- 9.4 E-health and Internet of Things (IoT) -- 9.5 Contributions required from NLP researchers in health-care applications -- 9.6 Conclusion and future work -- References -- 10. Blockchain of things for healthcare asset management | Sajid Nazir, Mohammad Kaleem, Hassan Hamdoun, Jafar Alzubi and Hua Tianfield -- 10.1 Introduction -- 10.2 Healthcare asset management -- 10.3 Challenges and opportunities in healthcare -- 10.4 Blockchain: concepts and frameworks -- 10.5 Blockchain of things architecture for healthcare asset management -- 10.6 Major healthcare application areas -- 10.7 Conclusion and future work -- References."]
[{"$nulls":4},"11. Artificial intelligence: practical primer for clinical research in cardiovascular disease | Shivendra Dubey, Chetan Gupta and Kalpana Rai -- 11.1 Artificial intelligence -- 11.2 Traditional statistics versus AI -- 11.3 Representative algorithms of AI -- 11.4 Machine power along with big data -- 11.5 Challenges to implementation -- 11.6 Conclusion and future work -- References -- 12. Deep data analysis for COVID-19 outbreak | S.O. Owoeye, O.J. Odeyemi, F.O. Durodola and K.I. Adenuga -- 12.1 Introduction to deep data analysis -- 12.2 Deep data analysis for COVID-19 -- 12.3 CNN architectures -- 12.4 Building the neural network -- 12.5 Neural network architecture -- 12.6 Other parameters used to configure the neural network -- 12.7 Model summary -- 12.8 Metrics used for evaluation -- 12.9 Results and evaluation -- 12.10 Conclusion and future scope -- References -- 13. Healthcare system using deep learning | J.B. Shajilin Loret and P.C. Sherimon -- 13.1 Introduction -- 13.2 History of healthcare deep learning -- 13.3 Deep learning benefits -- 13.4 Components of deep learning -- 13.5 The role of deep learning in healthcare in the future -- 13.6 Deep learning applications in healthcare -- 13.7 Conclusion and future work -- References -- 14. Intelligent classification of ECG signals using machine learning techniques | Kuldeep Singh Kaswan, Anupam Baliyan, Jagjit Singh Dhatterwal, Vishal Jain and Jyotir Moy Chatterjee -- 14.1 Introduction -- 14.2 Heart-generated ECG signal -- 14.3 Filtering parameters least-mean-square algorithm -- 14.4 Retrieve and classify ECG signals utilizing ML-based techniques -- 14.5 Artificial neural network (ANN)-based ECG signals -- 14.6 Classification of ECG signals "]
[{"$nulls":4},"based fuzzy logic (FL) -- 14.7 Fourier transform wavelet transforms -- 14.8 Combination of machine learning and statistical algorithms."]
[{"$nulls":4},"14.9 Conclusion and future work -- References -- 15. A survey and taxonomy on mutual interference mitigation techniques in wireless body area networks | Neethu Suman and P.C. Neelakantan -- 15.1 Introduction -- 15.2 Interference issues in WBAN -- 15.3 Mutual interference mitigation schemes -- 15.4 Conclusion and future scope -- References -- 16. Predicting COVID cases using machine learning, android, and firebase cloud storage | Ritesh Kumar Sinha, Sukant Kishoro Bisoy, Saurabh Kumar, Sai Prasad Sarangi and Utku Kose -- 16.1 Introduction -- 16.2 Literature survey -- 16.3 Implementation and methodology -- 16.4 Machine learning models -- 16.5 Introduction to android app -- 16.6 Result analysis -- 16.7 Conclusion and future work -- References -- 17. Technological advancement with artificial intelligence in healthcare | Manas Kumar Yogi, Jyotsna Garikipati and Jyotir Moy Chatterjee -- 17.1 Introduction -- 17.2 Literature review -- 17.3 Disease identification and diagnosis -- 17.4 Drug discovery and manufacturing -- 17.5 Electronic health records -- 17.6 Disease prediction using machine learning -- 17.7 Fairness -- 17.8 Data analytics role in healthcare -- 17.9 Deep learning applications in healthcare -- 17.10 Conclusion and future scope -- References -- 18. Changing dynamics on the Internet of Medical Things: challenges and opportunities | Imtiaz Ali Brohi, Najma Imtiaz Ali and Pardeep Kumar -- 18.1 Introduction -- 18.2 The applications of Internet of Things -- 18.3 Healthcare and Internet of Things -- 18.4 Security in Internet of Medical Things -- 18.5 Privacy in Internet of Medical Things -- 18.6 Perception of trust and risk in IoMT -- 18.7 Conclusion and future scope -- References -- 19. Internet of Drones (IOD) in medical transport application | G. Prasad, J. Kavya and J. Sahana -- 19.1 Introduction to unmanned aerial vehicle."]
[{"$nulls":4},"19.2 Internet of Things in Industry 5.0 -- 19.3 Applications in medical transport -- 19.4 Methodology and approach -- 19.5 Conclusion and future -- Acknowledgment -- References -- 20. Blockchain-based Internet of Things (IoT) for healthcare systems: COVID-19 perspective | Anand Sharma, S.R. Biradar, H.K.D. Sarma and N.P. Rana -- 20.1 Introduction -- 20.2 IoT in healthcare system -- 20.3 COVID-19 outbreak -- 20.4 Blockchain -- 20.5 Blockchain-based IoT for healthcare systems -- 20.6 Advantages of proposed system -- 20.7 Conclusion and future scope -- References -- 21. Artificial intelligence-based diseases detection and diagnosis in healthcare | Said El Kafhali and Iman El Mir -- 21.1 Introduction -- 21.2 Overview of diseases detection and diagnosis techniques -- 21.3 Supervised learning models -- 21.4 Unsupervised learning models -- 21.5 Reinforcement learning models -- 21.6 Summary of some applications for disease diagnosis in healthcare -- 21.7 Some open research problems -- 21.8 Conclusions -- References -- Index."]
[null,null,null,"Sommario/riassunto","This edited book covers big data analysis methods of patient data gained via IoT-enabled monitoring systems. The information gathered can be processed to aid clinicians with diagnoses, prognoses and interventions. This book is a great reference to those using, designing, modelling and analysing intelligent healthcare services."]