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Quantum Blockchain : An Emerging Cryptographic Paradigm
| Quantum Blockchain : An Emerging Cryptographic Paradigm |
| Autore | Rajasekar Vani |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (386 pages) |
| Altri autori (Persone) |
DhanarajRajesh Kumar
IslamS. K. Hafizul BalusamyBalamurugan HsuChing-Hsien |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-83672-7
1-119-83671-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910590094603321 |
Rajasekar Vani
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Smart healthcare system design : security and privacy aspects / / edited by S. K. Hafizul Islam, Debabrata Samanta
| Smart healthcare system design : security and privacy aspects / / edited by S. K. Hafizul Islam, Debabrata Samanta |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] |
| Descrizione fisica | 1 online resource (384 pages) |
| Disciplina | 610.28563 |
| Collana | Advances in Learning Analytics for Intelligent Cloud-IoT Systems Ser. |
| Soggetto topico |
Machine learning
Internet of things Artificial intelligence - Medical applications |
| ISBN |
1-119-79224-X
1-119-79223-1 1-119-79225-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Dedications -- Contents -- Preface -- Acknowledgments -- 1 Machine Learning Technologies in IoT EEG-Based Healthcare Prediction -- 1.1 Introduction -- 1.1.1 Descriptive Analytics -- 1.1.2 Analytical Methods -- 1.1.3 Predictive Analysis -- 1.1.4 Behavioral Analysis -- 1.1.5 Data Interpretation -- 1.1.6 Classification -- 1.2 Related Works -- 1.3 Problem Definition -- 1.4 Research Methodology -- 1.4.1 Components Used -- 1.4.2 Specifications and Description About Components -- 1.4.3 Cloud Feature Extraction -- 1.4.4 Feature Optimization -- 1.4.5 Classification and Validation -- 1.5 Result and Discussion -- 1.5.1 Result -- 1.5.2 Discussion -- 1.6 Conclusion -- 1.6.1 Future Scope -- References -- 2 Smart Health Application for Remote Tracking of Ambulatory Patients -- 2.1 Introduction -- 2.2 Literature Work -- 2.3 Smart Computing for Smart Health for Ambulatory Patients -- 2.4 Challenges With Smart Health -- 2.4.1 Emergency Support -- 2.4.2 The Issue With Chronic Disease Monitoring -- 2.4.3 An Issue With the Tele-Medication -- 2.4.4 Mobility of Doctor -- 2.4.5 Application User Interface Issue -- 2.5 Security Threats -- 2.5.1 Identity Privacy -- 2.5.2 Query Privacy -- 2.5.3 Location of Privacy -- 2.5.4 Footprint Privacy and Owner Privacy -- 2.6 Applications of Fuzzy Set Theory in Healthcare and Medical Problems -- 2.7 Conclusion -- References -- 3 Data-Driven Decision Making in IoT Healthcare Systems-COVID-19: A Case Study -- 3.1 Introduction -- 3.1.1 Pre-Processing -- 3.1.2 Classification Algorithms -- 3.2 Experimental Analysis -- 3.3 Multi-Criteria Decision Making (MCDM) Procedure -- 3.3.1 Simple Multi Attribute Rating Technique (SMART) -- 3.3.2 Weighted Product Model (WPM) -- 3.3.3 Method for Order Preference by Similarity to the Ideal Solution (TOPSIS) -- 3.4 Conclusion.
References -- 4 Touch and Voice-Assisted Multilingual Communication Prototype for ICU Patients Specific to COVID-19 -- 4.1 Introduction and Motivation -- 4.1.1 Existing Interaction Approaches and Technology -- 4.1.2 Challenges and Gaps -- 4.2 Proposed Prototype of Touch and Voice-Assisted Multilingual Communication -- 4.3 A Sample Case Study -- 4.4 Conclusion -- References -- 5 Cloud-Assisted IoT System for Epidemic Disease Detection and Spread Monitoring -- 5.1 Introduction -- 5.2 Background & -- Related Works -- 5.3 Proposed Model -- 5.3.1 ThinkSpeak -- 5.3.2 Blood Oxygen Saturation (SpO2) -- 5.3.3 Blood Pressure (BP) -- 5.3.4 Electrocardiogram (ECG) -- 5.3.5 Body Temperature (BT) -- 5.3.6 Respiration Rate (RR) -- 5.3.7 Environmental Parameters -- 5.4 Methodology -- 5.5 Performance Analysis -- 5.6 Future Research Direction -- 5.7 Conclusion -- References -- 6 Impact of Healthcare 4.0 Technologies for Future Capacity Building to Control Epidemic Diseases -- 6.1 Introduction -- 6.2 Background and Related Works -- 6.3 System Design and Architecture -- 6.4 Methodology -- 6.5 Performance Analysis -- 6.6 Future Research Direction -- 6.7 Conclusion -- References -- 7 Security and Privacy of IoT Devices in Healthcare Systems -- 7.1 Introduction -- 7.2 Background and Related Works -- 7.3 Proposed System Design and Architecture -- 7.3.1 Modules -- 7.4 Methodology -- 7.5 Performance Analysis -- 7.6 Future Research Direction -- 7.7 Conclusion -- References -- 8 An IoT-Based Diet Monitoring Healthcare System for Women -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Food Consumption -- 8.2.2 Food Consumption Monitoring -- 8.2.3 Health Monitoring Methods Using Physical Methodology -- 8.2.4 Methods for Health Tracking Using Automated Approach -- 8.3 Necessity of Wearable Approach? -- 8.4 Different Approaches for Wearable Sensing -- 8.4.1 Approach of Acoustics. 8.5 Description of the Methodology -- 8.6 Description of Various Components Used -- 8.6.1 Sensors -- 8.7 Strategy of Communication for Wearable Systems -- 8.8 Conclusion -- References -- 9 A Secure Framework for Protecting Clinical Data in Medical IoT Environment -- 9.1 Introduction -- 9.1.1 Medical IoT Background & -- Perspective -- 9.2 Medical IoT Application Domains -- 9.2.1 Smart Doctor -- 9.2.2 Smart Medical Practitioner -- 9.2.3 Smart Technology -- 9.2.4 Smart Receptionist -- 9.2.5 Disaster Response Systems (DRS) -- 9.3 Medical IoT Concerns -- 9.3.1 Security Concerns -- 9.3.2 Privacy Concerns -- 9.3.3 Trust Concerns -- 9.4 Need for Security in Medical IoT -- 9.5 Components for Enhancing Data Security in Medical IoT -- 9.5.1 Confidentiality -- 9.5.2 Integrity -- 9.5.3 Authentication -- 9.5.4 Non-Repudiation -- 9.5.5 Privacy -- 9.6 Vulnerabilities in Medical IoT Environment -- 9.6.1 Patient Privacy Protection -- 9.6.2 Patient Safety -- 9.6.3 Unauthorized Access -- 9.6.4 Medical IoT Security Constraints -- 9.7 Solutions for IoT Healthcare Cyber-Security -- 9.7.1 Architecture of the Smart Healthcare System -- 9.8 Execution of Trusted Environment -- 9.8.1 Root of Trust Security Services -- 9.8.2 Chain of Trust Security Services -- 9.9 Patient Registration Using Medical IoT Devices -- 9.9.1 Encryption -- 9.9.2 Key Generation -- 9.9.3 Security by Isolation -- 9.9.4 Virtualization -- 9.10 Trusted Communication Using Block Chain -- 9.10.1 Record Creation Using IoT Gateways -- 9.10.2 Accessibility to Patient Medical History -- 9.10.3 Patient Enquiry With Hospital Authority -- 9.10.4 Block Chain Based IoT System Architecture -- 9.11 Conclusion -- References -- 10 Efficient Data Transmission and Remote Monitoring System for IoT Applications -- 10.1 Introduction -- 10.2 Network Configuration -- 10.2.1 Message Queuing Telemetry Transport (MQTT) Protocol. 10.2.2 Embedded Database SQLite -- 10.2.3 Eclipse Paho Library -- 10.2.4 Raspberry Pi Single Board Computer -- 10.2.5 Custard Pi Add-On Board -- 10.2.6 Pressure Transmitter (Type 663) -- 10.3 Data Filtering and Predicting Processes -- 10.3.1 Filtering Process -- 10.3.2 Predicting Process -- 10.3.3 Remote Monitoring Systems -- 10.4 Experimental Setup -- 10.4.1 Implementation Using Python -- 10.4.2 Monitoring Data -- 10.4.3 Experimental Results -- 10.5 Conclusion -- References -- 11 IoT in the Current Times and its Prospective Advancements -- 11.1 Introduction -- 11.1.1 Introduction to Industry 4.0 -- 11.1.2 Introduction to IoT -- 11.1.3 Introduction to IIoT -- 11.2 How IIoT Advances Industrial Engineering in Industry 4.0 Era -- 11.3 IoT and its Current Applications -- 11.3.1 Home Automation -- 11.3.2 Wearables -- 11.3.3 Connected Cars -- 11.3.4 Smart Grid -- 11.4 Application Areas of IIoT -- 11.4.1 IIoT in Healthcare -- 11.4.2 IIoT in Mining -- 11.4.3 IIoT in Agriculture -- 11.4.4 IIoT in Aerospace -- 11.4.5 IIoT in Smart Cities -- 11.4.6 IIoT in Supply Chain Management -- 11.5 Challenges of Existing Systems -- 11.5.1 Security -- 11.5.2 Integration -- 11.5.3 Connectivity Issues -- 11.6 Future Advancements -- 11.6.1 Data Analytics in IoT -- 11.6.2 Edge Computing -- 11.6.3 Secured IoT Through Blockchain -- 11.6.4 A Fusion of AR and IoT -- 11.6.5 Accelerating IoT Through 5G -- 11.7 Case Study of DeWalt -- 11.8 Conclusion -- References -- 12 Reliance on Artificial Intelligence, Machine Learning and Deep Learning in the Era of Industry 4.0 -- 12.1 Introduction to Artificial Intelligence -- 12.1.1 History of AI -- 12.1.2 Views of AI -- 12.1.3 Types of AI -- 12.1.4 Intelligent Agents -- 12.2 AI and its Related Fields -- 12.3 What is Industry 4.0? -- 12.4 Industrial Revolutions -- 12.4.1 First Industrial Revolution (1765). 12.4.2 Second Industrial Revolution (1870) -- 12.4.3 Third Industrial Revolution (1969) -- 12.4.4 Fourth Industrial Revolution -- 12.5 Reasons for Shifting Towards Industry 4.0 -- 12.6 Role of AI in Industry 4.0 -- 12.7 Role of ML in Industry 4.0 -- 12.8 Role of Deep Learning in Industry 4.0 -- 12.9 Applications of AI, ML, and DL in Industry 4.0 -- 12.10 Challenges -- 12.11 Top Companies That Use AI to Augment Manufacturing Processes in the Era of Industry 4.0 -- 12.12 Conclusion -- References -- 13 The Implementation of AI and AI-Empowered Imaging Systems to Fight Against COVID-19-A Review -- 13.1 Introduction -- 13.2 AI-Assisted Methods -- 13.2.1 AI-Driven Tools to Diagnose COVID-19 and Drug Discovery -- 13.2.2 AI-Empowered Image Processing to Diagnosis -- 13.3 Optimistic Treatments and Cures -- 13.4 Challenges and Future Research Issues -- 13.5 Conclusion -- References -- 14 Implementation of Machine Learning Techniques for the Analysis of Transmission Dynamics of COVID-19 -- 14.1 Introduction -- 14.2 Data Analysis -- 14.3 Methodology -- 14.3.1 Linear Regression Model -- 14.3.2 Time Series Model -- 14.4 Results and Discussions -- 14.4.1 Model Estimation and Studying its Adequacy -- 14.4.2 Regression Model for Daily New Cases and New Deaths -- 14.5 Conclusions -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910554873203321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Smart healthcare system design : security and privacy aspects / / edited by S. K. Hafizul Islam, Debabrata Samanta
| Smart healthcare system design : security and privacy aspects / / edited by S. K. Hafizul Islam, Debabrata Samanta |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] |
| Descrizione fisica | 1 online resource (384 pages) |
| Disciplina | 610.28563 |
| Collana | Advances in Learning Analytics for Intelligent Cloud-IoT Systems |
| Soggetto topico |
Machine learning
Internet of things Artificial intelligence - Medical applications |
| ISBN |
1-119-79224-X
1-119-79223-1 1-119-79225-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Dedications -- Contents -- Preface -- Acknowledgments -- 1 Machine Learning Technologies in IoT EEG-Based Healthcare Prediction -- 1.1 Introduction -- 1.1.1 Descriptive Analytics -- 1.1.2 Analytical Methods -- 1.1.3 Predictive Analysis -- 1.1.4 Behavioral Analysis -- 1.1.5 Data Interpretation -- 1.1.6 Classification -- 1.2 Related Works -- 1.3 Problem Definition -- 1.4 Research Methodology -- 1.4.1 Components Used -- 1.4.2 Specifications and Description About Components -- 1.4.3 Cloud Feature Extraction -- 1.4.4 Feature Optimization -- 1.4.5 Classification and Validation -- 1.5 Result and Discussion -- 1.5.1 Result -- 1.5.2 Discussion -- 1.6 Conclusion -- 1.6.1 Future Scope -- References -- 2 Smart Health Application for Remote Tracking of Ambulatory Patients -- 2.1 Introduction -- 2.2 Literature Work -- 2.3 Smart Computing for Smart Health for Ambulatory Patients -- 2.4 Challenges With Smart Health -- 2.4.1 Emergency Support -- 2.4.2 The Issue With Chronic Disease Monitoring -- 2.4.3 An Issue With the Tele-Medication -- 2.4.4 Mobility of Doctor -- 2.4.5 Application User Interface Issue -- 2.5 Security Threats -- 2.5.1 Identity Privacy -- 2.5.2 Query Privacy -- 2.5.3 Location of Privacy -- 2.5.4 Footprint Privacy and Owner Privacy -- 2.6 Applications of Fuzzy Set Theory in Healthcare and Medical Problems -- 2.7 Conclusion -- References -- 3 Data-Driven Decision Making in IoT Healthcare Systems-COVID-19: A Case Study -- 3.1 Introduction -- 3.1.1 Pre-Processing -- 3.1.2 Classification Algorithms -- 3.2 Experimental Analysis -- 3.3 Multi-Criteria Decision Making (MCDM) Procedure -- 3.3.1 Simple Multi Attribute Rating Technique (SMART) -- 3.3.2 Weighted Product Model (WPM) -- 3.3.3 Method for Order Preference by Similarity to the Ideal Solution (TOPSIS) -- 3.4 Conclusion.
References -- 4 Touch and Voice-Assisted Multilingual Communication Prototype for ICU Patients Specific to COVID-19 -- 4.1 Introduction and Motivation -- 4.1.1 Existing Interaction Approaches and Technology -- 4.1.2 Challenges and Gaps -- 4.2 Proposed Prototype of Touch and Voice-Assisted Multilingual Communication -- 4.3 A Sample Case Study -- 4.4 Conclusion -- References -- 5 Cloud-Assisted IoT System for Epidemic Disease Detection and Spread Monitoring -- 5.1 Introduction -- 5.2 Background & -- Related Works -- 5.3 Proposed Model -- 5.3.1 ThinkSpeak -- 5.3.2 Blood Oxygen Saturation (SpO2) -- 5.3.3 Blood Pressure (BP) -- 5.3.4 Electrocardiogram (ECG) -- 5.3.5 Body Temperature (BT) -- 5.3.6 Respiration Rate (RR) -- 5.3.7 Environmental Parameters -- 5.4 Methodology -- 5.5 Performance Analysis -- 5.6 Future Research Direction -- 5.7 Conclusion -- References -- 6 Impact of Healthcare 4.0 Technologies for Future Capacity Building to Control Epidemic Diseases -- 6.1 Introduction -- 6.2 Background and Related Works -- 6.3 System Design and Architecture -- 6.4 Methodology -- 6.5 Performance Analysis -- 6.6 Future Research Direction -- 6.7 Conclusion -- References -- 7 Security and Privacy of IoT Devices in Healthcare Systems -- 7.1 Introduction -- 7.2 Background and Related Works -- 7.3 Proposed System Design and Architecture -- 7.3.1 Modules -- 7.4 Methodology -- 7.5 Performance Analysis -- 7.6 Future Research Direction -- 7.7 Conclusion -- References -- 8 An IoT-Based Diet Monitoring Healthcare System for Women -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Food Consumption -- 8.2.2 Food Consumption Monitoring -- 8.2.3 Health Monitoring Methods Using Physical Methodology -- 8.2.4 Methods for Health Tracking Using Automated Approach -- 8.3 Necessity of Wearable Approach? -- 8.4 Different Approaches for Wearable Sensing -- 8.4.1 Approach of Acoustics. 8.5 Description of the Methodology -- 8.6 Description of Various Components Used -- 8.6.1 Sensors -- 8.7 Strategy of Communication for Wearable Systems -- 8.8 Conclusion -- References -- 9 A Secure Framework for Protecting Clinical Data in Medical IoT Environment -- 9.1 Introduction -- 9.1.1 Medical IoT Background & -- Perspective -- 9.2 Medical IoT Application Domains -- 9.2.1 Smart Doctor -- 9.2.2 Smart Medical Practitioner -- 9.2.3 Smart Technology -- 9.2.4 Smart Receptionist -- 9.2.5 Disaster Response Systems (DRS) -- 9.3 Medical IoT Concerns -- 9.3.1 Security Concerns -- 9.3.2 Privacy Concerns -- 9.3.3 Trust Concerns -- 9.4 Need for Security in Medical IoT -- 9.5 Components for Enhancing Data Security in Medical IoT -- 9.5.1 Confidentiality -- 9.5.2 Integrity -- 9.5.3 Authentication -- 9.5.4 Non-Repudiation -- 9.5.5 Privacy -- 9.6 Vulnerabilities in Medical IoT Environment -- 9.6.1 Patient Privacy Protection -- 9.6.2 Patient Safety -- 9.6.3 Unauthorized Access -- 9.6.4 Medical IoT Security Constraints -- 9.7 Solutions for IoT Healthcare Cyber-Security -- 9.7.1 Architecture of the Smart Healthcare System -- 9.8 Execution of Trusted Environment -- 9.8.1 Root of Trust Security Services -- 9.8.2 Chain of Trust Security Services -- 9.9 Patient Registration Using Medical IoT Devices -- 9.9.1 Encryption -- 9.9.2 Key Generation -- 9.9.3 Security by Isolation -- 9.9.4 Virtualization -- 9.10 Trusted Communication Using Block Chain -- 9.10.1 Record Creation Using IoT Gateways -- 9.10.2 Accessibility to Patient Medical History -- 9.10.3 Patient Enquiry With Hospital Authority -- 9.10.4 Block Chain Based IoT System Architecture -- 9.11 Conclusion -- References -- 10 Efficient Data Transmission and Remote Monitoring System for IoT Applications -- 10.1 Introduction -- 10.2 Network Configuration -- 10.2.1 Message Queuing Telemetry Transport (MQTT) Protocol. 10.2.2 Embedded Database SQLite -- 10.2.3 Eclipse Paho Library -- 10.2.4 Raspberry Pi Single Board Computer -- 10.2.5 Custard Pi Add-On Board -- 10.2.6 Pressure Transmitter (Type 663) -- 10.3 Data Filtering and Predicting Processes -- 10.3.1 Filtering Process -- 10.3.2 Predicting Process -- 10.3.3 Remote Monitoring Systems -- 10.4 Experimental Setup -- 10.4.1 Implementation Using Python -- 10.4.2 Monitoring Data -- 10.4.3 Experimental Results -- 10.5 Conclusion -- References -- 11 IoT in the Current Times and its Prospective Advancements -- 11.1 Introduction -- 11.1.1 Introduction to Industry 4.0 -- 11.1.2 Introduction to IoT -- 11.1.3 Introduction to IIoT -- 11.2 How IIoT Advances Industrial Engineering in Industry 4.0 Era -- 11.3 IoT and its Current Applications -- 11.3.1 Home Automation -- 11.3.2 Wearables -- 11.3.3 Connected Cars -- 11.3.4 Smart Grid -- 11.4 Application Areas of IIoT -- 11.4.1 IIoT in Healthcare -- 11.4.2 IIoT in Mining -- 11.4.3 IIoT in Agriculture -- 11.4.4 IIoT in Aerospace -- 11.4.5 IIoT in Smart Cities -- 11.4.6 IIoT in Supply Chain Management -- 11.5 Challenges of Existing Systems -- 11.5.1 Security -- 11.5.2 Integration -- 11.5.3 Connectivity Issues -- 11.6 Future Advancements -- 11.6.1 Data Analytics in IoT -- 11.6.2 Edge Computing -- 11.6.3 Secured IoT Through Blockchain -- 11.6.4 A Fusion of AR and IoT -- 11.6.5 Accelerating IoT Through 5G -- 11.7 Case Study of DeWalt -- 11.8 Conclusion -- References -- 12 Reliance on Artificial Intelligence, Machine Learning and Deep Learning in the Era of Industry 4.0 -- 12.1 Introduction to Artificial Intelligence -- 12.1.1 History of AI -- 12.1.2 Views of AI -- 12.1.3 Types of AI -- 12.1.4 Intelligent Agents -- 12.2 AI and its Related Fields -- 12.3 What is Industry 4.0? -- 12.4 Industrial Revolutions -- 12.4.1 First Industrial Revolution (1765). 12.4.2 Second Industrial Revolution (1870) -- 12.4.3 Third Industrial Revolution (1969) -- 12.4.4 Fourth Industrial Revolution -- 12.5 Reasons for Shifting Towards Industry 4.0 -- 12.6 Role of AI in Industry 4.0 -- 12.7 Role of ML in Industry 4.0 -- 12.8 Role of Deep Learning in Industry 4.0 -- 12.9 Applications of AI, ML, and DL in Industry 4.0 -- 12.10 Challenges -- 12.11 Top Companies That Use AI to Augment Manufacturing Processes in the Era of Industry 4.0 -- 12.12 Conclusion -- References -- 13 The Implementation of AI and AI-Empowered Imaging Systems to Fight Against COVID-19-A Review -- 13.1 Introduction -- 13.2 AI-Assisted Methods -- 13.2.1 AI-Driven Tools to Diagnose COVID-19 and Drug Discovery -- 13.2.2 AI-Empowered Image Processing to Diagnosis -- 13.3 Optimistic Treatments and Cures -- 13.4 Challenges and Future Research Issues -- 13.5 Conclusion -- References -- 14 Implementation of Machine Learning Techniques for the Analysis of Transmission Dynamics of COVID-19 -- 14.1 Introduction -- 14.2 Data Analysis -- 14.3 Methodology -- 14.3.1 Linear Regression Model -- 14.3.2 Time Series Model -- 14.4 Results and Discussions -- 14.4.1 Model Estimation and Studying its Adequacy -- 14.4.2 Regression Model for Daily New Cases and New Deaths -- 14.5 Conclusions -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910830812803321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||