LEADER 01600nam 2200457 a 450 001 9910458882203321 005 20200520144314.0 010 $a9786613097682 010 $a0-19-154047-1 010 $a1-283-09768-0 035 $a(CKB)2560000000326388 035 $a(StDuBDS)EDZ0000155937 035 $a(MiAaPQ)EBC232908 035 $a(Au-PeEL)EBL232908 035 $a(CaPaEBR)ebr10460668 035 $a(CaONFJC)MIL309768 035 $a(OCoLC)712040290 035 $a(EXLCZ)992560000000326388 100 $a20011029d2001 uy 0 101 0 $aeng 135 $aur||||||||||| 200 10$aAugustine$b[electronic resource] $ea very short introduction /$fHenry Chadwick 210 $aOxford ;$aNew York $cOxford University Press$d2001 215 $a1 online resource (134 p.) $cill., facsim., ports 225 1 $aVery short introductions 300 $aOriginally published: 1986. 311 $a0-19-285452-6 311 $a0-19-177676-9 320 $aIncludes bibliographical references and index. 330 8 $aBy his writings, the surviving bulk of which exceed that of any other ancient author, Augustine came to influence not only his contemporaries but also modern theologists. This book gives a lucid introduction into Augustine's influential thoughts. 410 0$aVery short introductions. 608 $aElectronic books. 676 $a189/.2 700 $aChadwick$b Henry$f1920-2008.$0154652 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910458882203321 996 $aAugustine$930399 997 $aUNINA LEADER 01594nem 2200433Ia 450 001 9910700248703321 005 20110407135041.0 035 $a(CKB)5470000002408490 035 $a(OCoLC)711787695 035 $a(EXLCZ)995470000002408490 100 $a20110407d2009 da 101 0 $aeng 120 $ab|||||||||||| 121 $a||||||||| 124 $bd 135 $aurcn||||||||| 181 $ccrd$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGeologic map of the Yukon-Koyukuk Basin, Alaska$b[electronic resource] /$fWilliam W. Patton, Jr. ... [and others] 210 1$a[Reston, Va.] :$cU.S. Dept. of the Interior, U.S. Geological Survey,$d2009. 215 $a1 online resource (1 map) $ccolor$e1 pamphlet (iii, 26 pages) and 1 data sheet 225 1 $aScientific investigations map ;$v2909 300 $aRelief shown by contours, hachures, and spot heights. 300 $aTitle from PDF title screen (viewed on Apr. 7, 2011). 300 $aIncludes location map and col. ill. 300 $aData sheet shows a lithologic correlation , and 8 ancillary maps. 320 $aIncludes bibliographical references in pamphlet (pages 22-26). 606 $aGeology$zAlaska$zYukon-Koyukuk Census Area$vMaps 608 $aMaps.$2lcgft 615 0$aGeology 700 $aPatton$b William Wallace$f1923-$01392287 712 02$aGeological Survey (U.S.) 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910700248703321 996 $aGeologic map of the Yukon-Koyukuk Basin, Alaska$93456496 997 $aUNINA LEADER 04137nam 22006854a 450 001 9910778638103321 005 20200520144314.0 010 $a0-309-13280-0 010 $a0-309-51645-5 035 $a(CKB)110986584752994 035 $a(EBL)3375275 035 $a(MiAaPQ)EBC3375275 035 $a(Au-PeEL)EBL3375275 035 $a(CaPaEBR)ebr10038545 035 $a(OCoLC)923254820 035 $a(NjHacI)99110986584752994 035 $a(BIP)006474141 035 $a(EXLCZ)99110986584752994 100 $a20000329d2000 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNatural attenuation for groundwater remediation$b[electronic resource] /$fCommittee on Intrinsic Remediation, Water Science and Technology Board [and] Board on Radioactive Waste Management, Commission on Geosciences, Environment, and Resources 210 $aWashington, D.C. $cNational Academy Press$dc2000 210 1$aWashington, D.C. :$cNational Academy Press,$d2000. 215 $a1 online resource (288 p.) 300 $aDescription based upon print version of record. 311 $a0-309-06932-7 320 $aIncludes bibliographical references and index. 327 $a""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 Committeea???s Information-Gathering Meetings""; ""C Biographical Sketches of Committee Members and Staff""; ""Index"" 330 $aIn 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 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. 606 $aHazardous wastes$xNatural attenuation$xEvaluation 606 $aIn situ bioremediation$xEvaluation 606 $aHazardous waste site remediation$xEvaluation 606 $aGroundwater$xPurification 610 $aHazardous Wastes 610 $aIn Situ Bioremediation 610 $aGroundwater 610 $aHazardous Waste Site Remediation 610 $aTechnology & Engineering 610 $aScience 615 0$aHazardous wastes$xNatural attenuation$xEvaluation. 615 0$aIn situ bioremediation$xEvaluation. 615 0$aHazardous waste site remediation$xEvaluation. 615 0$aGroundwater$xPurification. 676 $a628.1/68 700 $aIntrinsic Remediation Committee$01523481 712 02$aBoard on Radioactive Waste Management Staff, 712 02$aDivision on Earth and Life Studies Staff, 712 02$aWater Science and Technology Board Staff, 712 02$aCommission on Geosciences, Environment, and Resources, 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910778638103321 996 $aNatural attenuation for groundwater remediation$93763711 997 $aUNINA LEADER 10357nam 22005653 450 001 9911006840803321 005 20231110233613.0 010 $a1-83724-472-3 010 $a1-5231-4666-4 010 $a1-83953-438-9 035 $a(MiAaPQ)EBC6885845 035 $a(Au-PeEL)EBL6885845 035 $a(CKB)21167814300041 035 $a(iet)PBHE038E 035 $a(OCoLC)1299384235 035 $a(EXLCZ)9921167814300041 100 $a20220216d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHealthcare Monitoring and Data Analysis Using IoT $eTechnologies and Applications 205 $a1st ed. 210 1$aStevenage :$cInstitution of Engineering & Technology,$d2022. 210 4$dİ2022. 215 $a1 online resource (427 pages) 225 1 $aHealthcare Technologies 311 08$a1-83953-437-0 327 $aIntro -- 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 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. 327 $a6.2 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. 327 $a11. 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 based fuzzy logic (FL) -- 14.7 Fourier transform wavelet transforms -- 14.8 Combination of machine learning and statistical algorithms. 327 $a14.9 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. 327 $a19.2 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. 330 $aThis edited book covers big data analysis methods of patient data gained via IoT-enabled monitoring systems. The information gathered can be processed to aid clinicians with diagnoses, prognoses and interventions. This book is a great reference to those using, designing, modelling and analysing intelligent healthcare services. 410 0$aHealthcare Technologies 606 $aPatient monitoring$xTechnological innovations 615 $aPatient monitoring$xTechnological innovations. 676 $a616.028 700 $aJain$b Vishal$01777669 701 $aChatterjee$b Jyotir Moy$01788804 701 $aKumar$b Pradeep$01369365 701 $aKose$b Utku$01076791 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911006840803321 996 $aHealthcare Monitoring and Data Analysis Using IoT$94389287 997 $aUNINA