Various starch-based hybrid nanomaterials and their applications in environmental remediation.
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"2059",leadTitle:null,fullTitle:"E-Learning - Engineering, On-Job Training and Interactive Teaching",title:"E-Learning",subtitle:"Engineering, On-Job Training and Interactive Teaching",reviewType:"peer-reviewed",abstract:"Adaptive E-learning was proposed to be suitable for students with unique profiles, particular interests, and from different domains of knowledge, so profiles may consider specific goals of the students, as well as different preferences, knowledge level, learning style, rendering psychological profile, and more. 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\r\n\tMolecular modeling methods in new drug discovery or 'repurposing' approaches of known approved drugs have gained a very important place in understanding the behavior of complex biological and chemical systems. The integration of experimental methods with computational methods is invaluable both in the development of promising new bioactive compounds and in the molecular characterization of protein-ligand or protein-protein interactions. Molecular docking, in one of these molecular modeling methods, is a unique approach to revealing key interactions in important molecular recognition processes and to accurately predict receptor-ligand binding free energies. Today, the molecular docking method has not only enabled the discovery of new drugs and therapeutic agents but also opened different windows to the submicroscopic world of interacting partner molecules by providing a unique atomistic perspective in the explanation of important intermolecular phenomena.
\r\n\r\n\tThe topics covered in this book are mainly:
\r\n\t1. Emphasizing the unique power of the molecular docking method in new drug discovery;
\r\n\t2. Demonstration of how the molecular docking technique has led to the discovery of new molecules in cancer therapy, proteasome, and STAT3 inhibition, and the treatment of Alzheimer's disease;
\r\n\t3. Underlining the importance of molecular docking-based modeling methods in the various branches of biotechnology
\r\n\tWe hope that this book will be a common point where researchers working in the fields of life sciences and drug development will eventually meet.
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Critical infrastructure was linked to aging public works in the 1980s: the National Council on Public Works Improvement in 1988 focused on public sector infrastructure. In the 1990s, infrastructure was redefined in terms of national security as a consequence of increased international terrorism. The number of critical infrastructure sectors in the National Infrastructure Protection Plan [1] has been enlarged to 17 since 9/11: it includes agriculture and food systems, the defense-industrial base, electricity systems, public health and health care facilities, national monuments, banking and financial systems, drinking water systems, chemical services, commercial buildings, dams, emergency services, nuclear power plants, information technology networks, telecommunications systems, postal and shipping services, transportation systems, and government facilities. Critical infrastructure is identified in Europe under the term “essential services” [2].
Shifting the concept of critical infrastructures has led to more flexibility and adaptability. The sophistication of an already complicated field, on the other hand, is increased, creating more confusion and more doubts. The definition of “lifeline system”, [3] was then established by some researchers to assess the efficiency of large, geographically distributed networks during crises caused by adverse events, such as natural disasters or cyber-attacks. Lifelines are classified into six major systems: electricity, gas and liquid fuels, telecommunications, transportation, waste management, and water provision. The economic well-being, security, and protection of our lives are closely related to those systems. Thinking of critical infrastructure across the sub-set of lifelines helps to simplify features common to important support structures and to enhance the performance of large networks, offering visibility into the technical challenges.
Lifeline systems, mostly on the basis of physical proximity and operational interaction, are interdependent. Cables and pipes are placed alongside each other in crowded area, resulting in an elevated risk due to proximity. Damage to one infrastructure component, such as an electrical cable, will easily ripple into damage to adjacent components, such as telecommunications cables and gas mains, with system-wide implications.
Lifeline systems are dependent on each other. Electric power networks, for example, supply electricity for pumping stations, storage facilities, and equipment control for transmission and distribution systems for oil and natural gas. Oil provides fuel and lubricants for generators, and natural gas provides energy for generating stations, compressors, and storage, all of which are required for the operation of electric power networks.
In the Merriam-Webster Dictionary, resilience is defined as “the capability of a strained body to recover its size and shape after deformation caused especially by compressive stress” [4]. Definitions vary slight, but all of them relate the principle of resilience to physical stress recovery.
A notable change from securing critical infrastructures to ensuring that communities are resilient has taken place following Hurricane Katrina. Furthermore, the concept of resilience is evolving, as the idea of critical infrastructures. In its present form, a society’s resilience is an overarching attribute that reflects the degree of community preparedness and the ability to respond to a crisis and rebound from it. Since lifelines are intimately linked to the economic well-being, security, and social fabric of a community, community resilience is closely related to the initial strength and gradual recovery of lifelines.
Debate over the concept of resilience is likely to persist, and refinements and elaborations of the term are to be expected. A framework for defining resilience has been suggested by the Multidisciplinary Center for Earthquake Engineering Research (MCEER) [5]. Resilience for both physical and social systems can be conceptualized as having four infrastructural qualities:
Robustness: the inherent strength or resistance in a system to withstand external demands without degradation or loss of functionality.
Redundancy: system property that under stress allows for alternate solutions, decisions, and substitutions.
Resourcefulness: the capacity to coordinate needed assets and services in crises.
Rapidity: the speed at which disruption can be overcome and safety, services, and financial stability restored.
As shown in Figure 1, an infrastructural performance, such as robustness,
The resilience profile.
For a community or an infrastructure, the loss of resilience,
The resilience indicator,
The modeling method used in this chapter is based on the methodology of Mixed Holistic Reductionist (MHR), where each infrastructure is divided into components (reductionist layer), services (service layer) and holistic nodes (holistic layer). The MHR approach is a guideline on how we can decompose each infrastructure and how we can define the interconnection among the different components. It also allows the identification of the right abstraction level due to the available information.
The agent-based simulator, called CISIApro 2.0, is then used to implement this approach. This simulator presents the consequences of adverse and positive events in an interdependent scenario. In real-time, this simulator runs connected to a SCADA (Supervisory Control And Data Acquisition) control center to receive current information on faults and linked to an Intrusion Detection System (IDS) to acquire actual threats and on-going cyber-attacks. CISIApro 2.0 integrates heterogeneous data to improve the situational awareness of operators and their decision-making process. This version of the simulator has been improved considering the telecommunication features. Specifically they are:
Elements with multiple services
Dynamic links
Routing links
Propagation models for ring topologies
Continuous and discrete dynamics simulation inside the agents
The possibility of co-simulating external dynamics
The ability of revoke services
This chapter is composed of the following sections: Section 2 analyses the idea of risk and resilience; Section 3 reviews the literature on critical infrastructures simulator; Section 4 presents the MHR approach while the simulator CISIApro 2.0 is described in Section 5; a telecommunication case study is summarised in Section 6; conclusions and future works are in Section 7.
The concepts of risk and resilience are similar and generally closely linked: improving the system’s resilience requires reducing risk. Risk is commonly structured in terms of preparedness, mitigation measures, reaction capabilities, and recovery processes; anticipation, absorption, adaptation and recovery are the typical components of resilience.
Owners and operators can improve the resilience of critical infrastructures by specific operations: withstanding specific threats, reducing or mitigating potential impacts, returning to normal operations if such degradation occurs. A resilience methodology includes increasing preparedness for an incident, implementing redundancy to mitigate the effects of an incident, and strengthening the coordination and execution of response and recovery procedures, for emergency action and business continuity.
There are five main steps in the resilience cycle: prepare, prevent, protect, response and recover. The resilience cycle must consider the consequences of interdependencies among critical infrastructures. The tool we present in this chapter, called CISIApro 2.0, aims to assess the consequences of adverse events on critical infrastructures in terms of components, services and also holistic agents. CISIApro 2.0 usually helps the operators in the recovery phase, knowing which are the possible consequences of actual adverse events.
The Department of Homeland Security (DHS) defines risk as “the potential for an unwanted outcome resulting from an incident, event, or occurrence, as determined by its likelihood and the associated consequences” [6]. Thus, risk is historically characterized as a function of three elements: the threats to which an asset is susceptible, the vulnerabilities of the asset to the threat, and the consequences potentially generated by the asset’s deterioration.
Threat is a “natural or man-made occurrence, individual, entity, or action that has or indicates the potential to harm life, information, operations, the environment, and/or property” [6]. Sometimes the term hazard, which can be defined as a “natural or man-made source or cause of harm or difficulty” [6], is used instead of threat. However, a “hazard differs from a threat in that a threat is directed at an entity, asset, system, network, or geographic area, while a hazard is not directed” [6]. Vulnerability is a “physical feature or operational attribute that renders an entity open to exploitation or susceptible to a given hazard” [6]. Consequences are the “effects of an event, incident, or occurrence” [6].
The challenge is to determine where and how resilience integrates into risk assessment as risk is a feature of threats and hazards, weaknesses, and consequences. Resilience, as defined by DHS, is the “ability to resist, absorb, recover from or successfully adapt to adversity or a change in conditions” [6]. The DHS lexicon also states that “Resilience can be factored into vulnerability and consequence estimates when measuring risk” [6]. Therefore, the resilience will have an effect on both vulnerability and consequences.
On the basis of these characteristics, it is possible to develop specific indicators and metrics to assess the risk to an organization or an infrastructure. Considering a threat or hazard (man-made or natural), the vulnerability and resilience of an organization will impact the potential consequences of an event. The interaction between the elements of risk is complex and made more so when one considers the transfer of risk between assets in the case of a threat by an intelligent adversary.
In literature, three main methodologies for the modelling approaches of critical infrastructure modelling are presented: agent-based simulation, input–output analysis and network modelling. Please refer to [7] for heterogeneous and/or unclassified approaches.
Each infrastructure is considered by agent-based simulations to be a complex adaptive structure, consisting of agents representing single aspects of the infrastructure itself. Different agents can be modelled at different degrees of abstraction based on the proposed level of resolution modelling. The primary benefit of agent-based simulation is the ability to establish synergistic behaviors as agents begin to work together [8].
The second method is based on the economic theory of Input–Output proposed by Leontief in the early 1930s, but later adapted to modelling infrastructures. Haimes and Jiang developed the linear input–output inoperability model (IIM) to research the impact of interdependencies on the inoperability of interconnected networked systems [9]. The key benefit of the IIM and its improvements is that the suggested solution is simple and flexible. IIM is usually confined to the financial costs of interdependencies.
In recent years, researchers have investigated new approaches to interdependency modelling of infrastructures. The most promising technique is based on graph and network theory. This approach uses abstract graphs made of nodes and arcs to describe infrastructures, representing links between components within infrastructures. The key benefit is to leverage closed form expressions and numerical simulations to characterise their topology, performance and uncertainty.
In this chapter, we propose an already applied approach, for helping during the modelling phase. To maximize the benefits of holistic and reductionist approaches, the Mixed Holistic Reductionist (MHR) [10] methodology was developed. The key goal of MHR approach is to provide a potential road-map to model critical infrastructures and their interdependencies properly.
In holistic modeling, infrastructures are seen as specific agents with defined boundaries and functional properties, creating a global and overall analysis. The purpose of presenting an infrastructure as a single element is to define the various infrastructures and their geographical extent. The volume of data needed for modeling activities is very limited at this stage and can be found in public data-sets.
In the other hand, to better appreciate the overall infrastructure, the reductionist approach stresses the need to thoroughly understand the roles and behaviours of individual components. The reductionist approach drills down to each component in terms of inputs and outputs. At this level of abstraction is easy to find dependencies between equipment and single components.
Various levels of analysis are required in modelled systems and their boundaries are lost in the event of complex case studies. For the MHR model, either a top-down or bottom-up approach might see relationships between infrastructures at different levels. The other key benefit is to model infrastructures at at multiple complexity levels, taking into account the quantity of data available.
The connection point between the two abstraction levels, i.e. holistic and reductionist approaches, is the quality of services (in the following, abbreviated as “service”) which is a key element for operators. This layer describes functional relationships between components and infrastructure at different levels of granularity. Services to clients and to other interconnected infrastructures are specifically treated in MHR as a middle layer between holistic and reductionist agents.
The MHR allows us to reach the right level of detail with minimal data and collected information. Some important considerations can be summarised in the following:
Each infrastructure is modelled starting from the identification of components and their interactions;
Each layer is defined with an appropriate level of abstraction based on information coming from end-users, stakeholders and open documents;
Each component (we called it entity or agent) must be described in a way to decouple it from other components: the behaviour of the component must depend on the valued explicitly exchanged with the other components;
The simulator must be able to represent any type of agent’s behaviour for adapting to the specific reference scenario.
MHR approach allows to define three different typologies of agents: holistic agent, service agent and reductionist agents.
The infrastructure as a whole (or its general organizational divisions) is represented by a holistic agent (Figure 2) to provide a model that can understand the global interactions between infrastructures.
The holistic agent representation.
A service agent represents a logical or organizational aspect, that provides an aggregate resource as the remote control: the remote control generally provides supervision, by means of software and data collection. Data can be collected through telecommunication network or field equipment in case of a geographically distributed infrastructure. In Figure 3, a service component is depicted considering the classical model of an agent in CISIApro 2.0. Some examples of service are: the ability to supply customers, the ability to produce resources, the ability to change topology, the aggregate state of a subset of specific and important components.
The service agent representation.
Finally, with a reductionist agent, we can represent, with the right degree of abstraction, all physical or aggregated entities of the overall system. In Figure 4, the representation of a reductionist component is depicted. The picture does not explicitly consider a cyber threat: this malicious event can be represented in the same way as an input failure with a suitable “cyber dynamic”.
The reductionist agent representation.
Finally, we can represent, with the right degree of abstraction, physical or aggregated components of the overall system with a reductionist agent. The representation of a reductionist aspect is represented in the Figure 4. The input failure contains natural disaster events, failures and faults, but also cyber threats.
In this chapter, CISIApro 2.0 simulates the impact of anomalies and security attacks on the communication infrastructure and on the interlinked CIs. It will also support the decision-making process allowing a “what-if analysis” by simulating the application of countermeasures and reconfiguration and their impact on system resilience.
CISIApro 2.0 (Critical Infrastructure Simulation by Interdependent Agents) [11] is a software engine able to calculate complex cascading effects, taking into account (inter)dependencies and faults propagation among the involved complex systems.
CISIApro 2.0 is an Agent-Based simulation software consisting primarily of two modules, see Figure 5. The first one is the off-line tool in which it is possible to design and implement complex and highly interdependent scenarios. While the second one is the on-line tool which is implemented in Simulink (Mathworks).
CISIApro 2.0 architecture.
CISIApro 2.0 is a database-centric architecture in which the database plays a key role as deonstrated in Figure 5. This implies a centralized asynchronous design that allows good modularity and scalability where each part of the IT infrastructure interacts, independently, with the centralized database in order to access the last data from the field (e.g. SCADA Systems), Complex Event Processing and generic IoT (Internet of Things) data systems, but also the simulation’s outputs.
Using the Mixed-Holistic-Reductionist (MHR) approach, modelling complex interdependent systems is a prerequisite to produce an effective model. Once modelled the involved scenario, with MHR methodology can be applied with CISIApro 2.0.
From this point of view, CISIApro 2.0 engine does not only analyze actual situation and calculate the risk projected in the possible near future but, first, it plays the important role of Hybrid Risk Evaluation Tool. Hybrid because it is able to get information of different natures (sensor and data acquisition and complex event processing systems) and translating them in operational levels of resources, faults or services for the entities introduced in the critical infrastructure model.
With the proposed architecture, through CISIApro 2.0 modelling software, it is possible to dynamically change the interdependencies model and plugin other modules in order to have a pseudo-real-time scalable and flexible system, which can be changed at any time. The DB stores the information needed for the representation of several Critical Infrastructures, such as:
Each entity is a specific instance of an entity type;
Each entity has a status made of variables with values;
Each entity has ports for exchanging resources;
Each resource is associated with a MHR layer/net;
Each layer has proper interdependencies;
Each interconnection is made of a couple of ports, associated to two entities.
It should be noted that CISIApro 2.0 has introduced efficient ways to model, execute and debug simulations and cascading effects. In particular, an intuitive Graphical User Interface, Figure 6, is provided to create entities and connect them in easy way.
CISIApro 2.0 Graphical User Interface.
The proposed scenario consists of three major components: the telecommunication network, the hospital ward and the smart factory. For industrial automation and possible remote operations, the fifth generation of telecommunication networks would be an essential improvement [12].
The telecommunication network of the reference scenario is represented in Figure 7. The purpose of this network is to manufacture and deliver services and it has a hierarchical structure consisting of three main sectors: backbone, metro and access networks.
The representation of the telecommunication network of the scenario.
The Optical Packet Backbone (OPB) is a multi-service network that exchanges voice, data and video services. This network is based on IP/MPLS (Multi-Protocol Label Switching) technology and the network is fully redundant in all its components and resistant to failure conditions to ensure a high level of the delivered services.
The Optical Packet Metro (OPM) network is a metropolitan and regional collection and aggregation network capable, depending on the configuration, of managing traffic flows at the Ethernet, IP or MPLS level. Like OPB, the OPM network is a multi-service network in which both fixed and mobile services combine and, as such, guarantee the requirements of scalability, reliability, availability, and flexibility. The access network meets end-users in the telecommunications industry and greatly influences the features of the service offered.
There are several systems, each with varying efficiency and coverage zones, to build “the last mile”, which is the part of the network that stretches from the client site to the first access node. The latest generation of access network (GPON-Gigabit Passive Optical Network) based on fiber optic infrastructure with OLT (Optical Line Terminal) and ONU (Optical Network Unit) is briefly described at the bottom left of Figure 7.
The distinctive aspect of this technology is the development of a network in which many recipients are reached by a single optical fiber: this enables you to prohibit the introduction of individual fiber ties between the control panel and the receiver, thus minimizing the cost of infrastructure.
In the central part of the figure, we have a broadband network. The strength of this technology, which has encouraged its growth and proliferation, lies in the fact that voice and data services use the same copper cables as the conventional telephone network. Data traffic received by the consumer is isolated by a splitter from voice traffic and processed by a Digital Subscriber Line Access Multiplexer (DSLAM) where the users’ broadband lines connected to that particular central station are terminated.
On the right side of the picture, we insert the mobile network with the Base Transceiver Station (BTS) of the GSM networks that consist of antennas and transceivers responsible for the radio coverage of the territory.
The security fabric and data-center layer are achieved using a few next-generation security devices and application controllers as:
Fortinet FortiGate (URL Filtering, Centralised Antivirus, Intrusion Detection and Protection System, E-mail filtering, Layer 4 Firewall)
F5 BIGIP (Web Application Firewall).
Linked to the telecommunication network, we have a hospital ward represented in Figure 8 that has been simplified to be modeled. This ward consists of a portion of the electrical grid in the yellow blocks, the water networks in blue blocks, the HVAC (Heating, Ventilation, and Air Conditioning) system in green blocks. We also add the building, made of eight rooms, where two are the operating rooms, and six are other rooms. These are the physicians’ room, the staff room, the rooms used for visits, the surgery, and the waiting room, and the storage of medication and surgical supplies. These two types of rooms are modeled distinctly to underline their different relevance in the ward: while the medical and operating rooms are dedicated to patient care, must continue to provide the services requested optimally even after a failure, on the contrary, a malfunction of ordinary rooms does not drastically affect the quality of the service offered by the entire department.
The hospital in CISIApro 2.0 simulator.
The telecommunication network facilitates electrical hospital records to be processed in the clouds and relies on network-connected medical devices and systems.
Linked to the telecommunication network, a smart factor is present and is modeled in Figure 9. The smart factory for this scenario was modeled with reference to the radio access network architecture implemented in the factories of the future. Figure 9 shows a completely autonomous local architecture, characterized by a pico site and an on-premises data center hub, which stores and performs data processing locally. The pico site is a small cellular base station typically covering a small area.
The factory in CISIApro 2.0 simulator.
The 5G network is the best solution for this scenario [13, 14], which also makes it possible to incorporate the remote control of robots: according to this model, in a cloud environment, rather than in the robot itself, various functions aimed at regulating motion can be stored. It is thus assumed that the security of the networks in which the control modules work from cyber attacks is of vital importance.
The scenario contains also several services, modeled as service agents in CISIApro 2.0. Among those services, we focus our attention on the “5G Service”, which is also included in Figure 7. 5G technology helps you to manage and control the movements of the programmable robotic arms remotely, increase human-machine interaction, capture the information processed by these intelligent systems and handle them in real-time. With regards to the hospital, the goal is to pervasively interconnect healthcare structures, doctors, patients, and healthcare personnel, to increase efficiency and effectiveness. In this context, the capabilities of 5G are useful for remote surgery, for remote control of the vital parameters of patients recovering from or suffering from chronic conditions and for exchanging medical data in real-time between the different technical figures.
The case study aims to examine the effects of a cyber-attack on the 5G core component, explicitly a DoS (Denial of Service). In this situation, we are not interested in how this attack was carried out, but we are more interested in the possible consequences of interconnected facilities.
The operative level of the “5G Core” agent is zero, as depicted in Figure 10, because it is the node that can not produce any output resource. The other entities of the telecommunications are not affected by this cyber-attack, because they don’t need this service to properly work.
The consequences on the “5G Core” component.
Different consequences affect the hospital and the smart factory. The domino effect on the smart factory is depicted in Figure 11. In the factory, there are four entities that need the 5G Core services to work: those entities are 5G-PGW-SGW, 5G-Pico, and the two antennas RU. Those elements are the red blocks in Figure 11, and they have an operative level equal to zero because they can not properly produce their outputs.
The consequences on the factory section in CISIApro 2.0.
Unlike the aforementioned elements, the two robots have an operative level of 0.4: although they cannot be controlled remotely or the information processed by them can be collected, however, these intelligent systems continue to operate.
In Figure 12, the output for the hospital is depicted. The absence of the 5G service has a more significant impact on medical rooms and operating rooms, due to the importance that hospital infrastructure has. In fact, despite following the cyber attack, it is no longer possible to carry out remote surgery, remotely monitor the vital parameters of patients and manage electronic medical records, these health rooms are still available for use and to ensure adequate care for patients.
The consequences on the hospital section.
This chapter analyses the concept of risk and resilience for critical infrastructures. The two concepts are tied together: minimizing risk means improving resilience. In critical infrastructure protection world, assessing risk is very complex due to, among the others, due to interdependency: managing risk is well-established in each infrastructure, but the risk of interconnected infrastructures is still an open problem without a single solution.
Modelling infrastructures and their interdependencies could help in managing risk and also resilience. The proposed approach is called MHR and it is implemented with CISIApro 2.0, an agent-based simulator, which assesses the consequences of events on the reference scenario. We test the proposed approach into a telecommunication scenario, with a hospital ward and a smart factory. The results demonstrate the correctness of this approach that is currently under validation within the EU H2020 RESISTO project. During the project, the system will be integrated into real test-bed provided by various telecommunication providers.
This chapter is partially supported by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 786409 (RESISTO - RESIlience enhancement and risk control platform for communication infraSTructure Operators).
The authors declare no conflict of interest.
Environmental pollution is becoming a serious global problem that society faces today. Ongoing anthropogenic activities, extensive food and agriculture practices, industrialization, and urbanization release huge amounts of pollutants into the environment that can cause air, water, and land pollution, consequently threatening to human, animal health, and ecosystem [1, 2]. These toxic pollutants can enter the human body either through inhalation, ingestion, or absorption and adversely affect health. Further, bioaccumulation of some heavy metals through the food chain and persistent organic pollutants in biota and fishes poses a huge threat to humans and wildlife and requires sustainable, efficient, and low-cost technologies to detect, monitor, and remediate the hazardous pollutants [1].
Different forms of pollutants are released into the environment; soil, water, and air. Organic substances (pesticides, insecticides, fertilizers, oil spills, phenols, chloroform, hydrocarbons), heavy metals and metalloids (Cr2+, Pb2+, Co2+, Cd2+, Cu2+, Zn2+, Mn2+, Ni2+, As, Hg), dyes, industrial effluents, sewage, as well as microbial pathogens are few contaminants in soil and water. While, contaminants such as toxic gases (nitrogen oxides, sulfur oxides, carbon oxides, ozone), suspended airborne particles, and volatile organic compounds are found in the atmosphere [3, 4].
These contaminants in soil, water, and air are remediated by using different conventional techniques, such as physical, chemical, and biological methods [4, 5, 6]. These techniques may be used in combination with one another to remediate contaminated sites. Adsorption (clay minerals, industrial wastes, biomass, biochar, activated carbon, biopolymer), chemical treatments, bioremediation, coagulation and flocculation, ion exchange, membrane-filtration, solidification/stabilization, electrokinetics, and electrochemical treatments technologies have been used in heavy metal removal from soil and water [7]. Bioremediation using microorganisms and plants helps to detoxify or remove crude oil, heavy metal removal, and pesticide degradation from soil and water [4, 5].
However, the majority of these conventional techniques are expensive, laborious, environmentally destructive, time-consuming methods, also involved in the consumption of chemicals and the generation of undesirable toxic by-products that are hazardous to the environment. Further, complexities of the mixture of different compounds, high volatility, and low reactivity of contaminants also limit the applications in environmental remediation [3, 5, 8]. New environmental remediation technologies are constantly being explored, and recent studies have focused on developing new environmental remediation technologies using various nanomaterials [3].
Nanotechnology has gained much attention in environmental remediation over the last few decades [1]. Nanotechnology is an advanced technology that works on the material in nanometer scale (1–100 nm) and produces materials, devices, and systems with specific and novel properties and functions by controlling the size and the shape of matters [1, 4, 9]. The nanomaterials are broadly categorized as organic and inorganic nanomaterials. Some literatures is classified based on materials used in the synthesis process; inorganic (metal, metal oxide, zero-valent metals), carbon-based [graphene, carbon nanotubes (CNTs)], polymer-based (dendrimers or polyamidoamine), and composite based nanomaterials [3, 10].
Nanomaterials have several advantages in environmental remediation over conventional methods; cost-effective, simple to use, energy conservative, sustainable, and more effective methods. Due to the properties such as smaller size (1–100 nm) and higher surface area to volume ratio of nanomaterials, they provide more reaction surface area, which increases reactivity and thus its sensitivity and effectiveness. Nanoparticles have a high sorption capacity for inorganic and organic compounds because of their specific characteristics; large surface area, an increased number of surface activation sites, a good affinity to other species [11]. Further, nanotechnology helps in the development of remediation technologies that are specific and efficient for a particular pollutant [3, 9].
Nanotechnology has potential applications in many fields, including food and agriculture, packaging, pharmaceutical, drug delivery, energy, and pollution treatment [1, 12]. Of which, the application of nanotechnology in pollution control and environmental remediation has gained popularity over the last decade; wastewater treatment, cleaning groundwater, and remediation of soil contaminated with pollutants. In the field of environment, nanotechnology has been used in pollution detection (sensing and detection), prevention of pollution, and purification/remediation of contamination [9]. Thus, nanotechnology provides a sustainable solution to the global challenges of protecting water, soil and providing cleaner air [13].
Various nanomaterials such as inorganic, carbonaceous nanomaterials, polymer-based nanomaterial are used in environmental remediation (air, soil, and water) as adsorbents, catalyst, photocatalyst, membrane (filtration), disinfectants, and sensors [1, 3, 14].
Metal (silver, gold), metal oxides (iron oxides, TiO2, MgO, Fe2O3, Al2O3), and zero-valent metals (Fe0, Zn0, Sn0, and Al0) based nanoparticles are mostly studied for environmental remediation including disinfection of water, treatment of drinking water, groundwater, wastewater, and air, because of their adsorption, antibacterial, antimicrobial, photocatalytic, reductive dehalogenation, desulfurization, and catalytic reduction activities [3, 15, 16]. Carbonaceous materials in different structural configurations; fullerene, single-walled carbon nanotubes (SWCNTs), multi-walled carbon nanotubes (MWCNTs), and graphene and used in the removal of organic and inorganic contaminants from air and water due to its adsorption and photocatalytic property [3].
Nanoscale zero-valent iron (nZVI) is the most widely studied nanoparticle in soil remediation [12] and is used for reductive immobilization of heavy metals in soil that decreases the bioavailability and mobility of heavy metals and prevents leaching into groundwater and transfers to the food chain [1]. Further, nanomaterials such as nanoparticles (NPs) (metal; Au, Ag, Fe, bimetal; Fe/Ni, Ag/Cu, metal oxides; TiO2, ZnO, Fe2O3), nanotubes (carbon nanotubes, halloysite nanotubes), and nanocomposites (graphene oxide) have been reported to utilize in detection, degradation, and removal by adsorption of pesticides [17].
Emission of greenhouse gases (carbon dioxide, methane, nitrous oxide, and fluorinated gases), volatile organic compounds (ethylene, aniline, benzene), are controlled either by separation or capturing, such as filtration, absorption in liquids, adsorption on solids, or a combination of these processes. In addition, bioaerosols (aerosols of biological origin such as viruses, bacteria, and fungi), an indoor air pollutant, can rapidly spread with airflow and can cause numerous diseases, including infections and allergies. The air filtration process using antimicrobial materials such as Ag NPs, Cu NPs, CNTs, and natural products is the most applied and effective technique to remove bioaerosols [1].
Various nanomaterials have also been studied for the treatment of drinking water and industrial wastewater, including adsorbents (nZVI or Fe, MnO, ZnO, MgO, Al2O3, TiO2, Magnetite or Fe3O4, CNT), photocatalysts (ZnO, TiO2, metal-based nanocomposites such as Ag/ZnO and Pt/ZnO, CdS, ZnS: Cu, CdS: Eu, CdS: Mn), electrocatalysts (Pt, Pd, Au/metal oxides TiO2, MgO, Fe2O3, Al2O3), nano-membranes (MWCNTs, electrospun PVDF, PVC, sodium titanate nanobelt membrane), disinfectants with antibacterial effects (Ag NPs, chitosan NPs, TiO2), nanosensors (Au NPs, Ag NPs) [14, 16, 18].
The term hybrid refers to fusion, joining, or mixing of characteristics at the molecular level, which generates a hybrid material owning the effective functionality of single components and eliminates undesirable characteristics [19, 20]. In this context, hybrid nanomaterials are defined as materials that are made up of two or more organic or inorganic components such as organic-organic (starch-cellulose), inorganic-inorganic (TiO2-Ag), and organic-inorganic (starch-TiO2) compounds, connected at the nanometer scale, combine the intrinsic characteristics of its individual constituents to additional properties due to synergistic effects between the components [21, 22]. These hybrid materials are synthesized by different methods such as covalent immobilization, electrostatic binding, polymerization methods, among others [21]. The properties of the hybrid material vary with the material (organic or inorganic), structure, and different component interface, and the optimum combination can enhance mechanical strength and thermosensitivity, improve thermal and chemical stability, and regulate optical, anticorrosive, magnetic, electrical, and thermal properties as well as fire retardancy [23]. Because of their excellent mechanical, physical, and tribological characteristics, hybrid nanomaterials are widely used in the area of food packaging, plant protection, electrochemistry, and various additional applications in the environmental, biotechnological, and agri-food sectors [19].
Generally, hybrid materials are classified into two categories depending on the intra- and intermolecular interactions among the organic matrix and cross-linking agent [21, 23];
Class I (organic and inorganic exhibiting weaker interactions such as noncovalent interactions; van der Waals and hydrogen bonding).
Class II (organic and inorganic exhibiting strong interactions such as covalent, ionic, ionocovalent, and coordinative bonding).
“Polymer-based composites” or “nanocomposites” can be defined as hybrid organic-inorganic composites when incorporating either component in nanoscale and generally obtained by incorporation of a small quantity of an inorganic component into an organic or a polymer matrix in order to form a new component with enhanced properties [24]. The “bio-nano composites” are the materials that comprise particles with at least one dimension in the range of 1–100 nm and a constituent(s) of the biological origin or maybe biopolymers.
Biopolymers (natural polymers) have received much attention in recent last decades due to their abundance, low toxicity, low cost, biodegradability, biocompatibility, and multiple functionalities [25]. A variety of biopolymers such as polysaccharides (cellulose, chitin, chitosan, pectin, starch, dextran, xanthan, guar gum, fucoidan, heparin, hyaluronan, and pullulan), proteins (albumin, casein, collagen, fibrinogen, and gelatin), polylactic acid (PLA), and nucleic acids have been used as alternative eco-friendly materials to replace synthetic polymers or petroleum-based polymers (PP, PE, and epoxies) partially or even totally [25, 26, 27]. Polysaccharide-based hybrid nanocomposites have become increasingly essential materials over the past decades [25, 27]. Many studies have reported the application of polysaccharide-based nanocomposites (natural polymer) in various fields such as food, biomedical, ecofriendly and sustainable food packaging, and environmental pollution control and remediation [28, 29, 30].
Due to the poor barrier, mechanical, and processing properties, natural polymers (biopolymers) are incorporated with other synthetic polymers or nanomaterials to improve their properties and applications [31]. Polysaccharides such as cellulose, chitin, chitosan, and starch are the most studied biopolymers and used in biodegradable nanocomposites with metal nanoparticles (Au, Ag, Cu, and Pd), metal oxide nanoparticles (TiO2, ZnO, CuO, Cu2O, SiO2, Fe2O3, and Fe3O4) and carbon nanomaterials (graphene and carbon nanotubes, CNTs) [25].
Starch, a natural, abundant, renewable, biocompatible, and biodegradable biopolymer, is naturally found in many plants as the primary source of energy and reserved in many parts of plants such as stalks, stems, roots, tubers, and seeds; main sources being cassava, wheat, rice, barley, maize or corn, banana, and potatoes, among others. Starch is a heteropolysaccharide that comprises d-glucose monomers joined with glycosidic bonds and can be denoted as (C6H10O5)n with the basic chemical formula. Starch is a heteropolysaccharide composed of two types of macromolecules: linear amylase (around 10–30% of starch granule) and branched amylopectin (remaining 70–90% of starch granule). Amylose is a linear polysaccharide chain of d-glucose units linked by α-(1,4)-glycosidic bond with a degree of polymerization in a range of 300–10,000. Amylopectin is a very high-molecular-weight polymer with a backbone structure of amylase cross-linked through α-(1,6) glycosidic bonds. The basic structure of amylose and amylopectin are shown in Figure 1 [25, 32, 33].
Structures of starch: (a) amylose and (b) amylose pectin.
Starch-based nanocomposites have wide applications in the fields of food and agriculture, packaging, biomedical, and environmental remediation as emulsion stabilizers, fat replacers, flexible films, carriers of bioactive compounds, drug delivery, and adsorbents in sewage treatment or wastewater treatment [34, 35, 36]. Starch nanoparticles are usually smaller than 300 nm in dimension with a high specific surface area. The various forms of starch-based nanoparticles are starch nanoparticles, starch nanospheres, starch micelles, starch vesicles, starch nanogels, and starch nanofibers [36].
Starch is a natural polymer, gained much attention because of its renewability, biodegradability, abundance, eco-friendly, relatively low cost, non-toxic, high adsorptive capacities, amenable to various chemical modifications, and cohesive film-forming properties. Starch molecules can bind with the heavy metal ions or contaminants through the functional (hydroxyl) groups on the starch structure [37, 38]. Further, high amylopectin content in starch has powerful swelling properties that are important in sorption-based applications [39]. In most published works, carbohydrates have been used as reducing, stabilizing, and/or complexing agents [40].
However, starch in a pure or native form has drawbacks such as poor processability, high brittleness, susceptibility to retrogradation, high viscosity, low adsorption capacity, and greater hydrophilicity or high-water absorption capacity, which limits its many applications in the environmental field. To overcome this problem and to obtain water-insoluble materials, starch is modified by physically [hydrothermal processing (i.e. gelatinization)] or chemically (etherification, esterification, crosslinking, grafting, oxidation, and enzymatic hydrolysis) or a combination of these two methods [41, 42, 43, 44]. Polysaccharides exhibit a great number of reactive hydroxyl groups, which can be exploited for direct esterification, etherification, and various chemical modifications [41].
Starch-based hybrid materials have numerous functionalities and/or novel properties due to the interactions between the individual constituents, mostly associated with synergetic effects, and have been reported in environmental remediation applications [25]. Several starch-based composites have been reported to have a remarkable adsorption tendency for the removal of heavy metals and dyes [45].
Table 1 shows the recent examples of the combination of starch and different metal, metal oxide, zero-valent metal, CNTs, and other polymers nanoparticles, such as Au, Ag, Cu, Pd, ZnO, TiO2, nZVI, among others. Nanomaterials are widely used to treat different contamination because of their high specific surface area to volume ratio, rapid kinetics, and high reactivity. However, pure or unmodified nanoparticles tend to agglomerate easily into larger particles that decrease the available specific surface area and reactivity. To improve the colloidal stability of nanoparticles, surface modification has been done by coating with various polymers. Of which starch is one of the relatively cheap and green polysaccharides [53, 59].
Starch hybrid nanomaterials | Application | Reference |
---|---|---|
Starch-stabilized Fe/Cu | As2+ and As5+ removal from the contaminated water | [46] |
Starch/Fe3O4 | Removal of perfluorooctanoic acid (PFOA) in soil and groundwater | [47] |
Starch/nZVI | Soil remediation—heavy metal removal, particularly Pb and Zn | [48] |
Starch/nZVI | Removal of As3+ and As5+ from aqueous solutions | [49] |
Starch-modified nZVI | Removal of Cr6+ | [50] |
Starch/SnO2 | Water treatment—Hg2+ | [51] |
Starch/TiO2 | Removal and determination of heavy metals such as Cd, Co, Cu, Pb, and Ni | [11] |
Starch/glycidyl methacrylate | Adsorption of Pb2+, Cd2+, Cu2+ and Cr3+ in wastewater | [52] |
Modified tapioca starch coated nZVI | Aqueous nitrate remediation | [53] |
Starch-Fe-Pd | Water decontamination—trichloroethene | [37] |
Starch xanthate | Water decontamination—Ni2+, Cu2+, Cr3+ | |
Carboxymethyl starch | Water decontamination—Pb2+, Cd2+, Cu2+ | |
Polymerized starch with epichlorohydrin | Water decontamination—dyes | |
Starch/polyvinyl alcohol (PVA) | Textile wastewater treatment—dye including methylene blue (cationic dye), methylene orange (anionic dye), starch, PVA | [54, 55] |
PVA/corn starch hydrogel | Iron (Fe3+) and arsenic (As3+) removal in aqueous solution | [56] |
Starch/TiO2 | Removal of dye from water/wastewater | [32] |
Starch-NiFe-layered double hydroxide | Adsorption of anionic dye methyl orange (MO) from aqueous solution | [45] |
Starch/poly(alginic acid-cl-acrylamide) nanohydrogel | Adsorption of coomassie brilliant blue R-250 dye from the aqueous solution | [57] |
Polyaniline/starch/hematite (PANI/starch/Fe2O3) | Wastewater remediation—adsorption of different heavy metals, including As3+, Zn2+, and Co2+ and antibacterial effect | [38] |
MWCNT-starch-iron oxide | Adsorbent for removing methyl orange (MO) and methylene blue (MB) from aqueous solutions | [58] |
Various starch-based hybrid nanomaterials and their applications in environmental remediation.
Rashid et al. reported that modified tapioca starch could be used as an effective surface modifier for nZVI particles for aqueous nitrate removal [53]. Starch-stabilized Fe/Cu nanoparticles in arsenic (As2+ and As5+) removal from the contaminated water where Cu as a metal catalyst was incorporated with Fe0 (nZVI) to form an iron bimetallic nanoparticle; then, the surface was modified to prevent the agglomeration [46]. Well stabilized (dispersed) iron oxides nanoparticles offer greater specific surface area and sorption capacity than the nanoparticles without any stabilizer towards a wide range of pollutants. Starch-functionalized magnetite (Fe3O4) nanoparticles showed much higher As2+ and As5+ sorption capacity than pristine magnetite nanoparticles [59]. Starch-stabilized Fe3O4 nanoparticles can be used as a “green” adsorbent for the effective removal of perfluorooctanoic acid (PFOA) in soil and groundwater [47]. Baysal et al. reported that starch-coated TiO2 NPs can be successfully used as adsorbents for the removal and determination of heavy metals such as Cd, Co, Cu, Pb, and Ni [11]. The starch-based SnO2 nanocomposite material can be used as an adsorbent for the removal of highly toxic Hg2+ metal ions from an aqueous medium [51].
CNTs have gained increased attention in multidisciplinary studies because of their unique physical and chemical properties. However, the hydrophobicity of CNTs may limit their application. The hydrophilicity and biocompatibility of CNTs can be improved by incorporating biopolymers such as starch in the composite system. Incorporating CNTs with starch also helps to overcome the limitation of starch, i.e. weak mechanical properties and poor long-term stability [60, 61]. MWCNT-starch-iron oxide has been reported as a better adsorbent for removing anionic dye methyl orange (MO) and cationic dye methylene blue (MB) from aqueous solutions than MWCNT-iron oxide. The hydrophilic property of soluble starch improved the hydrophilicity of MWCNTs and the dispersion of MWCNT-starch-iron oxide in the aqueous solution. In addition, the increased contact surface between magnetic MWCNT and dyes reduced the aggregates of MWCNTs and facilitated the diffusion of dye molecules to the surface of MWCNTs. Nanoparticles, ZnO, TiO2, or Ag or their complex decompose the adsorbed organic contaminants on MWCNTs as the photocatalysts [60].
Starch-based hydrogels have a good adsorption capacity, which can be used for wastewater treatment by removing various cationic or anionic dyes after modification with functional groups [44]. The incorporation of starch into synthetic polymer hydrogel networks improves their swelling and adsorption capacity [44]. Hydrogel as an adsorbent is one of the best candidates for removing soluble dyes from an aqueous solution. The study of methylene blue (MB) adsorption efficiency of NaOH-treated starch/ acrylic acid hydrogel showed high dye-capturing coefficients, which increase with the starch ratio and indicates the possibility of the hydrogels’ application for removing dyes from aqueous solution. In which, starch can be a natural-polymer superabsorbent because of a large number of hydrophilic groups (–OH) and other benefits such as renewable, very cheap, and biodegradable [62]. Biodegradable polymers, starch/cellulose nanowhiskers hydrogel composite, showed outstanding adsorption capacity to be employed in the remediation of methylene blue contaminated wastewaters [63]. Pectin-starch magnetite hybrid nanoparticles could be potential adsorbents for methylene blue dye with higher adsorption efficiency at a low polymer concentration and starch-pectin ratio and can be used to recycle water from the textile industry [58].
Increased nano-waste release in the environment, bioaccumulation, occupational exposure, and nanotoxicity are the major problems associated with the increased use of nanomaterials in environmental remediation. Nanoparticles incorporated in starch-based hybrid nanomaterials such as Ag, Au, nZVI, TiO2, SiO2, ZnO, Al2O3, CNTs, metal chalcogenides (CdS, CdSe), polymeric nanoparticles, among others, shows toxicity (acute or chronic) in high dose; growth inhibition of microalgae, disruption of membrane integrity, reactive oxygen species generation, oxidative stress, genotoxicity, and mutagenicity up to reproduction impairment in aquatic species and many health complications in human [41, 64, 65, 66, 67].
Because of the very small size, nanoparticles are capable of entering the human body by inhalation, ingestion via food, drink, and drugs, skin penetration, or injections and they have the potential to interact with intracellular structures and macromolecules for long periods [68]. Exposure to nanoparticles is associated with a range of acute and chronic effects ranging from inflammation, exacerbation of asthma, and metal fume fever to fibrosis, chronic inflammatory lung diseases, and carcinogenesis [64].
The effect of surface modification of nanoparticles such as nZVI is not clear. Sun et al. reported that surface modifiers enhance the stability of the nZVI that either increase the toxicity due to prolonged exposure to the living organisms or decrease the toxicity via reducing the adhesion of nZVI to living organisms or preventing the release of toxic ions. Starch stabilized nZVI produced higher phytotoxicity compared to bare nZVI, this may be due to the higher dispersity, hydrophilicity, and anti-aggregation of starch/nZVI that enhances their affinity to root surfaces and the oxidability of the Fe0, forming a coating of insoluble Fe3+ compounds on the root surface, and thus interferes the absorption of water and nutrients [69].
In the future, attention will be given to the green synthesis of nanomaterials because not all nanomaterials are produced in an eco-friendly way, as involves acid hydrolysis in multiple steps. There are several systems and methods for the green synthesis of nanoparticles, particularly enzymes, vitamins, microwave, bio-based methods, and from plants and phytochemicals [67, 70]. Green synthesis of nanoparticles using various natural sources, non-toxic solvents, and techniques (ultrasound, microwave, hydrothermal, magnetic, and bioproduction by fungi and other microorganisms) promote eco-friendly, sustainable, less expensive, and free of chemical contaminant production and applications [68].
Nano-wastes should be diluted and neutralized before disposal as they are extraordinarily toxic, hazardous, and/or chemically reactive. Proactive nano-waste management strategies need to be adopted to prevent long-term unintended consequences, and, where possible, nano-waste should be recycled [64].
Remediation is the science of removal or reduction of pollutants from the environment using chemical or biological means. Starch-based hybrid materials are a cost-effective and eco-friendly solution over petroleum-based polymers in environmental remediation. Though starch is a natural polymer with many benefits, including renewability, biodegradability, abundance, eco-friendly, relatively low cost, non-toxic, poor barrier, and mechanical properties, poor processability, high brittleness, and high hydrophilicity are major drawbacks of raw starch. Therefore, starch is modified by physical and/or chemical methods, including gelatinization, etherification, esterification, crosslinking, grafting, oxidation, and enzymatic hydrolysis.
Starch-based hybrid materials have numerous functionalities and/or novel properties, mainly associated with synergetic effects and reported in environmental remediation applications. Starches are incorporated with metal NPs, metal oxide NPs, zero-valet metals, CNTs, and other polymers as reducing, stabilizing, and/or complexing agents to remove various toxic contaminants such as heavy metal, organic contaminants, and dye wastewater and groundwater.
In future studies, various natural starch sources, green synthesis of nanomaterials, recyclability, and toxicity effect of nano-waste should be considered. Further development of biodegradable starch-based hybrids and nanomaterials focusing on new functional materials, processing technology, and cost reduction needs to be studied for commercial application.
The authors declare no conflict of interest.
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He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"94",type:"subseries",title:"Climate Change and Environmental Sustainability",keywords:"Environmental protection, Socio-economic development, Resource exploitation, Environmental degradation, Climate change, Degraded ecosystems, Biodiversity loss",scope:"\r\n\tSustainable development focuses on linking economic development with environmental protection and social development to ensure future prosperity for people and the planet. To tackle global challenges of development and environment, the United Nations General Assembly in 2015 adopted the 17 Sustainable Development Goals. SDGs emphasize that environmental sustainability should be strongly linked to socio-economic development, which should be decoupled from escalating resource use and environmental degradation for the purpose of reducing environmental stress, enhancing human welfare, and improving regional equity. Moreover, sustainable development seeks a balance between human development and decrease in ecological/environmental marginal benefits. Under the increasing stress of climate change, many environmental problems have emerged causing severe impacts at both global and local scales, driving ecosystem service reduction and biodiversity loss. Humanity’s relationship with resource exploitation and environment protection is a major global concern, as new threats to human and environmental security emerge in the Anthropocene. Currently, the world is facing significant challenges in environmental sustainability to protect global environments and to restore degraded ecosystems, while maintaining human development with regional equality. Thus, environmental sustainability with healthy natural ecosystems is critical to maintaining human prosperity in our warming planet.
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