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1. Introduction
To be effective, future regulation and organisational policy aimed at achieving trustworthy AI must be supported by some degree of standardisation in processes and technological interoperability. The rapid development of AI technologies and the growth of investment in AI applications presents a pacing problem, wherein the rapid change in characteristics of AI related to policy and regulatory issues outpaces ability of societies to legislate for or regulate the technology. At the same time, the multinational nature of the major commercial developers of AI, plus the expanding access to AI skills and computing resources means that standards must be agreed internationally to be of widespread use in supporting policy and regulations. While there has been an explosion in policy documents from national authorities, international organisations and the private sector on the ethical implications of AI, standards in this area have been slower to emerge. Understanding existing standardised ICT development and organisational management practices offer insight into the extent to which they may provide a basis for standardising practice in governing the development and use of more trustworthy and ethical AI. Standards Developing Organisations (SDOs) vary in their approach to addressing specific ethical issues.
The Institute of Electrical and Electronic Engineers (IEEE) global initiative on ethically aligned design for autonomous and intelligent systems has spawned the IEEE 7000 standards working group that places ethical issues at its heart [1]. This work was seeded from a set of principles defined in a comprehensive international export review on Ethically Aligned Design [2], which also highlighted the influence of classical ethics, professional ethics and different moral worldviews.
A different approach is taken by the ISO/IEC Joint Technical Committee 1 (JTC1). JTC1 which was established by the International Standardisation Organisation (ISO) and the International Electro-technical Commission (IEC) in 1987 to develop, maintain and promote standards in the fields of Information Technology (IT) and Information and Communications Technology (ICT). Expert contributions are made via national standards bodies and documents (over 3000 to date) are often used as technical interoperability and process guideline standards in national policies and international treaties, as well as being widely adopted by companies worldwide. Statements of relevance to UN Sustainable Development Goals and Social Responsibility Guidelines are an inherent part of all new standardisation projects proposed in JTC 1 [3]. AI standards are addressed together with big data technology standards by the JTC 1 subcommittee (SC) 42 which was first chartered in autumn 2017 and held its inaugural meeting in April 2018. As of the end of 2020 it has published six standards and has active projects addressing 23 others (https://www.iso.org/committee/6794475.html).
This chapter highlights the challenges facing companies and authorities worldwide in advancing from the growing body of work on ethical and trustworthy AI principles to a consensus on organisational practices that can deliver on these principles across the global marketplace for AI-based ICT. We review how SC 42 standardisation efforts benefit from building on established process standards in areas of management systems, IT governance, risk and system engineering. From this analysis, we identify a simple conceptual model that can be used to capture the semantic mapping between different SC 42 standards. An ontology is used as it allows a conceptual model to be defined that links together concepts via association into a network of concepts. This has the potential to establish an open ontology that can map between core concepts from standardisation and pre-standardisation deliverables in varied states development, formal approval, and international community consensus with concepts needed to address trustworthy AI. Such a network allows the definition of terms and concepts from different standards related documents to be interlinked and thereby the consistency of conceptual use between different can be analysed and improvements suggested. While this is not intended to replace the consistency checking that occurs naturally in the JTC1 standards development process, it does allow us to identify some mapping and comparisons between different forms of standard that have been applied to different areas of standardisation in SC 42. We conclude then by suggesting how this approach can be extended to enable similar comparisons with the use of concepts in documents being drafted by other SDO committees and by other bodies, including regulatory proposals, civic society policy proposals and guidelines developed by individual organisations.
2. Challenges of building international consensus on governing trustworthy AI
Since 2017 there has been an explosion in AI initiatives globally. As of February 2021, the Council of Europe’s tracker (https://www.coe.int/en/web/artificial-intelligence/national-initiatives) has identified over 450 such initiatives world wide, primarily from national authorities, international organisations and the private sector. The most frequently discussed address subjects include privacy, human rights, transparency, responsibility, trust, accountability, freedom, fairness and diversity. Influential works such as the IEEE EAD [2], the EU’s High Level Expert Group on AI [4] and the OECD [5] often present these issues under the banner of ethical or trustworthy AI.
Scholars and think tanks have analysed this growing body of documents on ethical and trustworthy AI. One extensive survey identifies an apparent consensus on the importance of ethical principles of transparency, justice, non-maleficence, responsibility, and privacy, whereas other issues of sustainability, dignity, and solidarity in relation to labour impact and distribution garner far less attention across works [6]. Public authority works are identifying gaps in relation to the use of AI by governments and in weapon systems [7]. Private sector outputs have been criticised as instruments to reduce demand for government regulation [8], as potential barriers to new market entrants [9] and failing to address tensions between ethical and commercial imperatives within organisations [10]. A general criticism is a focus on individual rather than collective harms such as loss of social cohesion and harm to democratic systems [11]. The required progression from approaches that propose broad principles, to specific and verifiable practices that can be implemented by organisations and, where deemed necessary, regulated by legislation, implies a focus on governance and management of AI. Appropriate governance, management, and risk management measures can reinforce benefits and mitigate the ethical and societal risks of employing AI technology. Governance approaches can be characterised as [12]: market-based, resulting from value-chain partner pressures, including from consumers; self-organisation, based on an organisation’s internal policies; self-regulation based on industry wide agreement on norms and practices; and co-regulation based on industry compliance with government regulation and legislation. There have been some proposals for possible regulatory structures including: new national [13] and international [14] co-regulatory bodies and internal (self-regulatory) ethics boards that may help organisations implement best practice [15, 16]. However, AI governance through co-regulation presents a number of major challenges [17]. These include: reaching stable consensus on what defines AI; widening access to AI skills and computing infrastructure obscuring developments from regulators; the diffusion of AI development over locations and jurisdictions globally; the emergence of impacts of an AI system only when assembled into a product or service; the opacity of modern subsymbolic machine learning methods and techniques, i.e. their unsuitability for clear human readable explanations; the potential for highly automated AI-driven systems to behave in unforeseeable ways that can escape the visibility or control of those responsible for them. More broadly, co-regulation is challenged by: the pacing problem, as AI technology develops faster than society’s ability to legislate for it; international cooperation needed for common standards being impeded by AI’s perceived role as a strategic economic or military resource; the perceived impediments of legislation to realising the competitive national economic and social benefits of AI; and the power asymmetry in AI capability being concentrated in digital platforms benefiting from network effects [9]. Over all types of works, a wide range of motivation have been identified [18], the incompatibility of some of which can further impede consensus on approaches to implementing trustworthy AI.
Nevertheless, there are multiple parallel standardisation activities ongoing internationally that are attempting to build some level of consensus, including the above-mentioned IEEE P7000 and ISO/IEC JTC 1/SC 42 activities and several national activities. This multiplicity of standards development may itself, however, contribute to inconsistencies and incompatibilities in how different organisations govern their AI activities. Reducing ambiguity in how different stakeholders in the AI value chain communicate with each other, and with society in general about their trustworthy AI practices is therefore critical to building trustworthiness of the resulting AI-based products and service. With both individual organisations developing their own AI policies and legislation for AI regulation starting to be considered in major jurisdictions such as the EU, there is a need to support the ongoing mapping of concepts between these different parallel activities so that harmful or expensive inconsistencies can be identified early and hopefully resolved.
The following requirements for semantic interoperability between concepts developed by different bodies can therefore be identified and are depicted in Figure 1:
Consistency between documents being developed within the same standards family.
Comparison between documents produced by parallel standards activities.
Mapping between standards and regulatory/legislative proposal to determine the extent to which a standard can address regulatory requirements (US “safe harbour” or EU’s harmonised standards approach).
Analysing the degree of compliance between a standard and an organisation’s AI policies and procedures, including their documentation outputs.
Assessing organisational policies against regulations.
Comparing different regulatory proposals internationally or comparing revisions to proposals in a single jurisdiction.
Comparing AI policies published by different organisations.
Figure 1.
Role of semantic interoperability between bodies involved in governance of trustworthy AI.
3. JTC1 standards related to trustworthy AI
Same as other ISO/IEC JTC 1 standardisation activities, SC 42 places a strong emphasis on ensuring consistency with existing process and interoperability standards as well as reuse of existing terms and concepts to provide industry with a consistent body of applicable standards. SC 42 is therefore addressing AI-related gaps within existing standards, including those for management systems, risk management, governance of IT in organisations, IT systems and software quality. Rather than addressing AI ethics directly as a normative issue, SC 42 addresses the broader issues of trustworthy AI, with a technical report that sets out some of the core concepts and issues for standardisation related to Trustworthy AI (ISO/IEC 24028:2020) [19]. In this report, trustworthiness is defined as the ability to meet stakeholders’ expectations in a verifiable way. When applied to AI, trustworthiness can be attributed to services, products, technology, data and information as well as organisations when considering their governance and management. This view considers trustworthy AI as realisable as part of a broader set of engineering, management, and governance process standards that can be employed together by organisations involved in AI and that can support mechanisms for conformity assessment including 3rd party certification and external oversight.
The Trustworthiness Working Group (WG 3) within SC 42 has a strong pipeline of pre-standardisation and standardisation activities. The road mapping activities within the group are driven by gap analyses of prior art as well as current policy documents (including the IEEE EAD [2], HLEG [4], and OECD [5]). WG 3 builds on foundational terminology and high-level life cycle notions elaborated within SC 42/WG 1 foundational deliverables ISO/IEC CD 22989 [20] on AI Concepts and Terminology and ISO/IEC CD 23053 [21] on a Framework for Artificial Intelligence (AI) Systems Using Machine Learning. WG 3 primarily looks at Trustworthiness high level characteristics and addresses them through elaboration of new project proposals of either pre-standardisation informative deliverables, surveying the state of the art in an area (before proceeding to normative coverage at a later stage) or of normative deliverables.
The fully fledged normative deliverable type within the ISO/IEC ecosystem is an International Standard (IS), however, not many areas in AI are mature enough to be addressed in international standards. This includes non-normative technical reports on current approaches to addressing societal and ethical aspects of AI [22] and bias in AI [23]. Thus WG 3 currently works only on three IS deliverables.
ISO/IEC CD 23894 Information Technology — Artificial Intelligence — Risk Management [24] is a specialisation of ISO 31000 Risk Management [25]. This is an example of SC 42’s respect for prior art and application of existing frameworks such as quality, risk management, or management systems framework in the newly standardised area of AI and ML.
Another IS deliverable within the group is ISO/IEC WD 25059 Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI-based systems [26]. This IS is an extension to the influential Systems and software Quality Requirements and Evaluation (SQuaRE) series owned by JTC 1/SC 7. Quality and trustworthiness are in a sense competing paradigms as they are looking at similar sets of high-level characteristics such as robustness, reliability, safety, security, transparency, explainability etc. but the distinctive difference is in the need for quality stakeholders to take explicit part in actively defining quality requirements, while trustworthiness stakeholders do not have to explicitly state their expectations in order to influence objective trustworthiness criteria. At any rate, the SQuaRE4AI standard sets a quality model that profiles the traditional quality and trustworthiness top level characteristics and their sub-characteristics for other normative deliverables in the area that are aiming at setting method and process requirements and recommendations.
The third IS in the making in WG 3 is ISO/IEC WD 24029–2 Artificial intelligence (AI) — Assessment of the robustness of neural networks — Part 2: Methodology for the use of formal methods [27]. This series aims to address the technical robustness pillar of AI trustworthiness, Part 2 specifically by looking at formally provable robustness and performance related properties of neural networks. While machine learning and neural networks in particular are an extremely active R&D field, the formal mathematical theory in which neural networks are based is well academically researched and stable. Therefore, it is possible to benefit from known and provable properties of neural networks in current and upcoming industrial applications.
Technical Specification is a normative deliverable that has a less rigorous approval process, essentially there is only one round of national bodies approval for a TS compared to two distinct (and repeatable) stages for an IS approval. While it is easier to approve and publish a TS, a TS needs to be transformed into an IS or withdrawn 3 years after its publication. TS are sometimes called experimental standards. This type of deliverable is used in areas that are in urgent need of normative standardisation as demonstrated by industry or societal demand, while the area to be standardised is still in flux from the research and development point of view. This is why WG 3 decided to ask SC 42 national bodies to approve development of ISO/IEC NP TS 6254 Information technology — Artificial intelligence — Objectives and methods for explainability of ML models and AI systems.
To develop an understanding of how these trustworthy AI standards relate to policies and processes defined by individual organisations and emerging as regulations in different jurisdictions requires an understanding of other aspects of AI standardisation under development in the other working groups of SC 42.
Working group 1 (WG 1) addresses foundational standards including the above-referenced AI Concepts and Terminology [20], which aims to provide consistency across the use of terms and concepts in other SC 42 documents and a Framework for Artificial Intelligence (AI) Systems Using Machine Learning [21] which reflects the central position of the machine learning area of AI in industry interoperability requirements.
Of importance to the mapping of AI standards to industry practice and regulation was the approval in August 2020, after a justification study, to develop an AI Management System (AIMS) Standard [28]. Management System Standards have a distinct role on the ISO ecosystem of standards types, as they provide the basis for certifying organisational processes. This provides a basis for organisations to demonstrate their conformance to specific standardised behaviour for management and related technical operations processes. Regulatory authorities also can make reference to such standards in specifying compliance regimes in complex technical domains. This allows authorities to manage the complexity and risk of technological change in regulations and to do so in a way that aligns with international industry and society consensus established through international standards. In contrast with industry consortia active in standardisation, standards produced by ISO and IEC are driven by national bodies (ISO and IEC members) who are typically mandated by their governments to represent a wider range of societal stakeholders than just industry. The overarching goal of these member organisations is to ease doing business according to the United Nations World Trade Organisation’s charter, as well as achieving United Nations’ sustainable development goals (SDGs).
In recognition that big data plays a central role in the development of modern AI systems, Working Group 2 (WG 2) of SC 42 has developed a series of big data standards. This includes a Big Data Reference Architecture (BDRA) [29] that provides a structured set of functional areas related to Big Data processing. Currently, WG 2 is developing a process management framework for big data analytics [30].
Finally, SC 42 also hosts and leads a joint working group (JWG 1) with JTC 1/SC 40 which addresses IT service management and IT governance in a specification for governance implications of the use of artificial intelligence by organisations [31]. This builds on the existing SC 40 standard providing guidance and principles for the effective, efficient, and acceptable governance of IT in an organisation [32].
4. Conceptual modelling approach
The formalisation of terminology is already a key element of international standards development [33], Section 16. However, the use of ontologies to improve the consistency and coordination in international standards is not so well established, though it has been proposed [34]. There are some ambitious proposals to drive standards development together with implementation and verification from machine readable standards, with machine readable semantics of ontologies at its core [35], annex 11.4. The proposed application of ontologies to automated assistance in standards management and compliance, however, is outside the scope of this chapter.
ISO/IEC JTC 1/SC 32 has established requirements for ontologies to support semantic interoperability between information systems developed by different organisations and consortia across different application domains. These requirements address ontology definitions in a hierarchical manner with a top-level ontology that provides core concepts that can be used in defining more specialised domain-specific ontologies [36]. Such ontologies are expressed through a combination of natural language definitions and a machine-readable representation in a combination of description logic captured in the OWL2 language standard by the W3C [37] and Common Logic (CL) [38]. While CL is a full First Order Logic and therefore more expressive than OWL2, the latter offers the advantage of decidability by automated reasoners, therefore better supporting automated checking of specifications. For the top-level ontology, the Basic Formal Ontology (BFO) is used [39]. To support the mapping between different domain models, the BFO adopts a realist design philosophy that aims to capture the objective reality in a domain rather than existing data representations. The BFO places a strong emphasis on representing temporal and spatial characteristics of concepts. For example, at the highest level it distinguishes between conceptual hierarchies for continuant entities that persist over time, and occurrent entities that are time bound. Separate to its inclusion in ISO/IEC standardisation’s use of ontologies, the BFO has been used for a set of biomedical ontologies [40] and for a collection of general common core ontologies addressing both horizontal concepts, known as mid-level ontologies, e.g., on event, information, currency, and domain-specific ontologies such as spacecraft and ethnicity [41].
While the BFO provides a precise conceptual structure for modelling a wide range of concepts, its realist approach to ontology engineering implies a need for a centrally controlled programme of ontology development to ensure consistent use of top-level concepts. While this would suit the objective of checking for and resolving consistency between a set of documents being developed under a common authority e.g. within SC 42, the requirements for checking consistency between regulatory and organisation policy drafts raises the needs to support a broad cohort of conceptual modellers who can operate without a common coordinating authority. An ontological approach that will be accessible for analysis and development by this wider range of conceptual modellers points therefore to demand less stringent conceptual modelling skills and is more accommodating to decentralised, domain-specific, and parallel development than BFO would enable.
The World Wide Web Consortium’s (W3C) Data Activity (https://www.w3.org/2013/data/)buildson its earlier Semantic Web activity in developing industry standards for semantic interoperability that works to the scale and decentralised nature of the WWW. Grounded in the Resource Description Framework (RDF) [42] which allows an unlimited knowledge graph of nodes and links to exist as online resources on the WWW. Nodes and associations in this knowledge graph are typed according to ontologies, also known as data vocabularies, that can be developed independently and published to a distinct namespace on the WWW. This name-spaced typing allows the free combination of types and associated conceptual knowledge from any published vocabulary. This has enabled the development and integration of a global network of over 1200 open and interlinked data sets, known as the linked open data cloud [43]. Further logical axioms can be introduced to a vocabulary by including constructs from the Web Ontology Language (OWL) [37]. The W3C restricts its ontology standardisation activities to those vocabularies that support the development, location, interlinking and use of datasets on the web and to application areas that are well aligned to decentralised data publishing e.g. geospatial data. Organisations and consortia that develop vocabularies that address specific domains are free to publish them under their own name space, reusing aspects of other ontologies as needed. This highly decentralised approach therefore aligns well with the goal of promoting participation of those generating standards, organisational policies and regulations as well as those with an interest in how these documents develop and map to each other.
There is extensive research conducted on the use of ontologies in assessing compliance of organisational policies and practices with regulation [44]. Such assessment can be categorised as ex-ante compliance activities conducted before the regulated activity is performed or as ex-post compliance which is conducted after the regulated activities have occurred. The Provenance Ontology (PROV-O) is a W3C standard for the representation of provenance information using semantics provided by RDF and OWL [45]. PROV-O provides classes, properties, and restrictions to represent and interchange provenance information within different contexts. Its conceptual model is focussed on provenance of activities, the entities that those activities use and generate, and the actors associated with those entities or to whom activities are attributed. PROV-O can be specialised to create new classes and properties to model provenance information for different applications and domains. This has been utilised to extend its conceptual model to specify ‘plans’ for ex-ante representations, scientific workflows, and domain-specific requirements for activities. An example of its usefulness is the representation of ex-ante and ex-post activities for evaluating compliance with EU’s General Data Protection Regulation [46], which is one of the more relevant extant regulations for AI. This existing capability, coupled with the decentralised conceptual modelling enables by the underlying RDF/OWL-based approach, leads us to adopt PROV-O, and its modelling of activities in particular, as the basis for conceptual modelling to support semantic interoperability between different sources of trustworthy AI concepts outline in Section 2.
5. Ontology for semantic interoperability between trustworthy AI documents
The approach therefore taken in this chapter combines elements of BFO and PROV-O to provide a minimal and easy to understand ontology for mapping between different standard, regulations and organisational policies. Specifically, we retain the distinction from BFO between continuant and occurrent entities. For the continuant concept we adopt the PROV-O concept of Entity, which is defined as: a physical, digital, conceptual, or other kind of thing with some fixed aspects; entities may be real or imaginary. For the occurrent concept we adopt the PROV-O concept of Activity, which is defined as: something that occurs over a period of time and acts upon or with Entities. It may include consuming, processing, transforming, modifying, relocating, using, or generating entities. We complement these core concepts of entity and activity with the PROV-O concept of Actor which is defined as: something that is involved in and may bear some form of responsibility for an Activity taking place, for the existence of an Entity, or for another Agent’s Activity. BFO captures the organisational concept of a role as a subclass of continuant. However, the centrality of ethics and responsibility to the conceptual modelling of trustworthiness as well as for practical mapping to normative rules that are essential to standards, regulation and organisation policies demands that the concept of an Agent is made core alongside Entity and Activity. For the core relationships between these core concepts, we again draw from PROV-O relationships between these concepts, but recognising the benefit of the realist philosophy of BFO, these are phased in the present tense, rather than the past tense expression of PROV-O, which has a narrower focus on recording provenance i.e. what has existed and occurred. Therefore, we introduce the following associations:
Activity uses Entity: where usage is defined in PROV-O as the beginning of utilisation of an Entity by an Activity, where before usage, the Activity had not begun to utilise this Entity and could not have been affected by the Entity.
Activity generates Entity: where generation is defined by PROV-O as the completion of production of a new Entity by an Activity, where this Entity did not exist before generation and becomes available for usage after this generation. This therefore allows dependency chains to be assembled between Activities.
Entity attributedTo Agent: which is the ascribing of an Entity to an Agent, thereby conveying some sense of responsibility for the Entity to that Agent. This provides a short form for relating an Entity to an Agent when the Activity associated with the Actor that generates or uses the Entity is not specified.
Activity associatedWith Agent: which PROV defines as an involvement by an Agent in an Activity.
Activity informedBy Activity: which allows a dependency between two Activities to be defined without needing to specify the generation and use of an Entity between them.
Agent actsOnBehalfOf Agent: which is used in PROV-O to express delegation, which is the assignment of authority and responsibility to an Agent to act as delegate or representative of another Agent. The delegation can be assigned to a specific Activity which implies that the delegate Agent therefore takes on a degree of responsibility for the Activity.
Entity derivesFrom Entity: where PROV-O defines a derivation as a transformation of one Entity into another, an update of an Entity resulting in a new one, or the construction of a new Entity based on a pre-existing Entity. This relationship can be associated with a specific Activity, offering a similar chaining relationship to generates and uses.
In addressing these core concepts and relationships, we express a scope related to the activities of an organisation that relate to the trustworthiness of AI. Trustworthiness can be manifested through Trustworthy Characteristics of Entities that make up a product or service employing AI. Such AI trustworthiness characteristics can be for example associated with: the datasets used to train data-driven machine learning AI [47, 48, 49]; trained ML models [50, 51] or AI-based product or services [52]. While some of these existing approaches share specific type of trustworthy characteristics for an Entity, e.g. those related to bias in ML models or the processing of personal data, these differ according to the needs of the specific link in the AI value chain that is being served, i.e. is the organisation providing dataset, AI model or completed AI-based products and service to its customers. However, the common feature of the above use of Trustworthy Characteristics is that they are communicated between one actor in a value chain and another. As the SC 42 technical report on an Overview of AI Trustworthiness [22] states that it is insufficient for one to simply refer to the ‘trustworthy AI’, but instead one must specify who trusts whom in what aspects of AI development and use. In this ontology, therefore, we define that the Actor that is providing an Entity to another Actor takes responsibility for ensuring that the Trustworthy Characteristics of the Entity in question is exhibited, and not necessarily just to the Actor receiving them but to any interested parties.
Trustworthiness can also be manifested through the Activities of an organisation involved in providing Entities that exhibit some Trustworthy Characteristics. The existing examples of approaches to expressing trustworthy characteristics of Entities referenced above define many of those characteristics in terms of Activities that were conducted to arrive at the characteristic, e.g. risk assessment.
To date, SC 42 has focussed on developing process-oriented standards, which is in line with the approach to developing Management Systems Standards (MSS) [53] annex SL. This approach is now being applied by SC 42 to develop a standard for an AI Management Systems (AIMS) [28]. A Management System is defined as a set of interrelated or interacting elements of an organisation to establish policies and objectives and processes to achieve those objectives. An MSS aims to provide organisations with guidance on standards activities in a manner that may be subject to independent certification. Such certification can also play an important role in establishing trust in an organisation’s implementation of complex technical processes as part of a regulatory framework. Therefore, the following concepts from [28] are added to the model as subclasses of Agent:
Organisation: person or group of people that has its own functions with responsibilities, authorities and relationships to achieve its objectives [28]. It is the Organisation which is the Actor disclosing Trustworthy Characteristics.
Stakeholder (used synonymously in [28] with an ‘interested party’): person or organisation that can affect, be affected by, or perceive itself to be affected by a decision or activity, that are conducted by the Organisation implementing an AIMS. The Stakeholder is therefore the Actor towards which any Trustworthy Characteristic is exhibited, with the aim of contributing to how that Actor determines its level of trust in what the Organisation claims possesses that characteristic.
Stakeholders can be internal or external to the Organisation. Therefore, an Organisation will be involved in disclosing Trustworthy Characteristics to both internal and external stakeholder. Internal stakeholders include those with overall organisational governance responsibilities, i.e., the governing body, management and employees. Internal stakeholder also includes those bound to an Organisation by contracts, which would include shareholders and supply chain partners, i.e., providers, customer and individual consumers. External Stakeholders can include regulators, government, potential supply chain partners, consumers, civic society, NGOs, the media and society in general.
As Trustworthy Characteristics are defined as a relationship between Organisations and Stakeholders, Entities being characterised are subclassed Assets. Assets are Entities that represent some value to a Stakeholder. For AI internal stakeholders, Assets could include the data used to train or test an AI system, a trained AI model, a product or service that uses one or more AI systems, data consumed by an AI system in operation, software, computing and human resources used in training and operating an AI system [22].
In considering the trustworthiness of an AIMS we would be focussed on the Activities conducted by the Organisation operating the AIMS. However, this subclassing of Agents also allows Activities with Trustworthy Characteristics to be associated with Stakeholder, and in particular external stakeholders, which allows complementary activities, e.g., in value chain partners or regulators, to be modelled. While external Activities may not be an explicit part of an AIMS specification, they may be referenced in regulations or organisational policies which aim to clarify the interactions and share of responsibilities between Activities of the Organisation and those of such external stakeholders.
To capture to different responsibilities within an Organisation operating an AIMS, additional semantics for the relationships between Activities (beyond dependencies via the use and generation of Entities and the dependsOn relationship) are required. Reference architectures and process model specifications often group activities into groups representing closely related or commonly co-occurrent process. Activities are also sometime grouped into roles, which are a set of activities servicing some common purpose, the responsibility for which is often allocated together to a specific organisational unit. We therefore include a compositional relationship between Activities called partOf that allows for various forms of Activity groupings.
Within the context of an AIMS, and its mapping to regulations and policies of specific organisations, it is also important to capture some sense of different levels of responsibility and corresponding accountability. SC40 distinguishes between an organisation’s governance function and its management function as part of guidelines the governance of IT [32]. The governance function is responsible for understanding the external pressures in forming an organisation’s direction, strategy, objectives and policy in adopting IT, including customer, competitive, stakeholder expectation and regulatory pressures. The management function is then responsible for planning and achieving those objectives within the constraints of the strategy and policy. The governance function is structured into three activities, which are elaborated in the governance of IT implementation guide standards [54], Evaluate, Direct and Monitor. The evaluate activity addresses internal and external considerations in assessing plans and proposals from the management function. The direct activity sets direction through strategy and policy and assigns responsibilities for their realisation by the management function. The monitor activity assesses the performance of the realisation of governance direction on a regular basis, triggering further evaluation and direction if deficiencies are identified. Management functions are responsible for the control of technical operations activities and may do so through delegation between appropriate levels of control. To capture the relationship between governance and management activities and between different levels of control between management activities, the following relationships between Activities in the ontology are defined: directedBy, evaluatedBy, monitoredBy and controlledBy.
Figure 2 captures the above concepts and relationships into a core ontology that is intended to support the mapping of concepts between different emerging AI standards in SC 42 and between those standards and emerging organisational policies or governmental regulations as indicated in Figure 1. By restricting these concepts to a small core that is already established in existing standards, upon which some of the SC 42 standards are based, we anticipate this ontology will provide a robust basis for identifying the relationships between such concepts in different specifications.
Figure 2.
Core concepts and relationship for semantic interoperability for trustworthy AI.
SC 42 has already identified characteristics that a trustworthy AI system or organisation involved in their implementation could exhibit [22]. These include technical characteristics such as reliability, robustness, verifiability, availability, resilience, quality, bias and robustness; stakeholder-related characteristics such as ethics, fairness and privacy; as well as management- and governance-related characteristics such as transparency, explainability, accountability and certification. However, the definition of many of these characteristics are still not yet well defined in relation to AI. By focussing on Activities, Entities and Agents we aim to identify mappings between concepts in different standards and therefore any gaps that can directly inform more consistent and comprehensive standards. In this way we hope to assist in the progression from broad statements of principles and areas of concern by the private sector, international bodies and governments, towards the develop of commonly understood process framework for the governance and management of Trustworthy AI, the ontology aims to do this is a way that accommodates the current range of definitions and interpretation of many of these characteristics and supports their convergence over time into concrete and internationally recognised governance and management processes and policies. In the following sections we show how this process can be undertaken by using this ontology to map between activities in the core anticipated AIMs and other relevant SC 42 specifications.
6. Modelling of AI management system activities
In 2020, SC 42 completed a justification study for a Management System Standard for AI. This was accepted in August and led to the initiation of a project to develop as AI Management System (AIMS) Standard [28], following the guidelines set out in the ISO/IEC Directive 1 [53]. All MSS should follow a consistent high-level structure which includes common text and terminology as presented in Annex SL of this Directive. This is to allow different MSS addressing different horizontal and domain-specific areas to be integrated into the same overall management system within an organisation that implements these MSS. It is intended that MSS should be developed in a manner open to different stakeholders, including accreditation bodies, certification bodies, enterprises and the user community. The high-level structure for MSS therefore provides a well-defined source of concepts that are likely to be reflected in the MSS being developed for AI, which must also address management aspects of trustworthiness. The use of MSS for organisational process certification means it is also a suitable source of concepts that will be useful to track against those being developed for public and organisational policy and processes.
Table 1 presents a mapping of the MSS high level structure as a set of 17 AIMS activities, with each concept given an identifier, a label attribute, and a ‘see also’ attribute referencing the relevant section in the AI MSS draft, the numbering for which is mandated in the MSS high level structure. Each of these AIMS activities is attributed to either the organisation overall (O) or top management (TM) as defined in the MSS high level structure, where the former attribution implies that activity spans governance and management levels. Relationship between these activities is captured by generates and uses attributes referencing 21 AIMS entities derived from the text in the MSS high level structure.
Activity ID
label
See also “42001-*”
attributedTo
generates (type Entity)
uses (type Entity, “aims*”)
aimsA1
Understanding organisation and its concepts
4.1
O
aimsE1 - External and internal issues affecting AIMS outcomes
aimsE20 - AIMS non conformity and corrective action report; aimsE21 - AIMS corrective action evaluation
E5, E8, E7
aimsA17
AIMS Continual improvement
10.2
O
aimsE1 - aimsE15; aimsE17; aimsE20; aimsE21
E1-E15
Table 1.
Activity and entities identified for an AIMS.
* = wildcard representing row value(s) in the column.
7. Modelling of AIMs technical operation activities
The foundational standard on AI terms on concepts [20] entered its second committee draft ballot review towards the end of 2020 and is therefore still under development. However, Section 6 of the draft does provide an outline of the lifecycle for an AI system made up of activities: inception; design and development, verification and validation; deployment; operation and monitoring; continuous validation; re-evaluation; and retirement. Table 2 shows a partially expanded breakdown of sub-activities that are part of these activities, exemplifying how the partOf attribute of these activities are used to identify the constituent activities. It also identifies certain sub-activities as also part of specific AIMS activities from Table 1. Sub-activities under the Inception activity are identified as part of specific AIMS activities which would form part of the governance and management activities. The operations and monitoring activity (OM in Table 2) of the AI lifecycle also contains some sub-activities that are part of AIMS activities, related to monitoring (aimsA13), communication (aimsA13) and risk (aimsA6). Further, all the AI lifecycle activities except Inception, are classified as technical operations activities, meaning within the AIMS they are directed by activity aimsA3 and aimsA4, evaluated by activities aimsA8 and aimsA9, monitored by activities aimsA13, aimsA14, aimsA15, aimsA16 and aimsA17 and controlled by activities aimsA12 (plan and control AI processes). The transitive nature of the partOf relationship therefore implies that the sub-activities of these AI lifecycle activities are also classed as technical operation activities with the same directedBy/evaluatedBy/monitoredBy/controlledBy relationship to the corresponding AIMS activities. As AIMS activities can operate at different levels of management delegation, technical operations activities such as operations and monitoring (OM) therefore both play a part in conducting AIMS activities, but at a much narrower level of abstraction, as well as being subject to direction, evaluation and monitoring of other AIMS activities. It is notable that all the top layer activities in this lifecycle model, apart from retirement activities (RT) are part of the AIMS activity address risk (aimsA6).
ID 22989*
CD2 22989 - AI terms and concepts
partOf
IC v
Inception
IC1
Determine stakeholders’ objectives
IC; aimsA1
IC2
Determine stakeholders’ requirements
IC; aimsA2
IC3
Risk assessment and treatment planning
IC; aimsA6
IC4
Policies and compliance planning
IC; aimsA4
DD ^*
Design and development
VV ^*
Verification and validation
DE ^*
Deployment
OM v*
Operation and monitoring
OM1
Monitor AI system
OM; aimsA13
OM2
Repair AI system
OM
OM3
Update AI system
OM
OM4
Support AI system users
OM; aimsA11
OM5
OM5: Risk monitoring and review
OM; aimsA6
CV ^*
CV: Continuous validation
RE ^*
RE: Re-evaluation
RT ^*
RT:: Retirement
Table 2.
Partially expanded view of AI lifecycle activities and sub-activities from CD22989 [20]: Activity ID key: v = constituent activities expanded, ^ = constituent activities collapsed, * = AI technological operation activities.
Though this lifecycle model from [20] is still a draft, it is being used in other SC 42 drafting activities, specifically the technical report on AI bias [23], which in Section 9 breaks down bias treatment at each of the top levels activities of the lifecycle model, and a new work item on AI system life cycle processes to support their definition, control and improvement within an organisation or project [55].
As well supporting the mapping of AIMS activities into standards that explicitly define processes that we can model as activities, the ontology from Figure 2 can also be used to map AIMS into role-based frameworks. Specifically, SC 42 Big Data Reference Architecture [29], which is structured into big data provider roles of: application provider, framework provider, service provider, data provider and consumer. These roles further are subdivided into sub-roles for which constituent activities are defined. This conforms with the notion of a role being a set of activities, so again this containment structure can be expressed as partOf relationship between the different levels of activity sets corresponds to those roles. All these are categories as technical operations activities in relation to the AIMS activities, though some also map to specific AIMs activities, with roles-based activities related to auditing (aimsA14) and requirements capture (aimsA7).
This mapping of activities reveals that while different standards developed under SC 42 are broadly consistent with the high-level structure of AIMS, they vary in which areas of AIMS they focus on. While this may be arguably appropriate for the projects concerned, it does indicate that AIMS activities are not comprehensively mapping into other SC 42 standards as a matter of course. The ontology therefore provides a way of tracking these mapping and identifying gaps that may need to be addressed as the AIMS is specified or as changes to the other standards as they develop or are reviewed over time.
8. Modelling stakeholders
The mapping of role-oriented activity processes from the Big Data Reference Architecture to AIMS activities also indicates how groupings of activities expressed as a role, e.g. Big Data Application Provider or Big Data Consumer, can also be used to model a stakeholder. The draft foundational terms and concepts standard from SC 42 [20] has similarly identified stakeholder in relation to similar types of value-chain role, but SC 42 has not yet attempted a more detailed characterisation of the activities that such stakeholders would undertake, and which would therefore define such stakeholder roles.
However, many aspects of AI ethics and their impact on the trustworthiness of AI, relate to the impact on stakeholders who are not directly involved in the AI value chain, even as customers or consumers, e.g., pedestrians injured by an automated vehicle, local communities blighted by automated routing of commercial traffic through their neighbourhood, or people denied access to financial, health or social security services due to bias in algorithmic decision-making. In addition, such indirectly affected stakeholders may be difficult for organisations to identify and consult, so appropriate representation may be needed. Such representation could take the form of NGOs concerns with rights of certain groups, labour unions, professional bodies, local community groups, up to and including democratic forms of representation in the form of local and national governments. These issues are addressed however in the ISO Guidelines for Social Responsibility ISO26000 [56]. Its guidance is based on the fundamental practices of recognising social responsibility within an organisation and undertaking stakeholder identification and engagement. The guidance is based on the principles similar to those identified in the growing literature on AI ethics [6], including accountability, transparency and ethical behaviour as well as respect for the rule of law, international norms of behaviour and human rights. Social responsibility guidance is provided in terms of 37 issues, each with suggested actions to address them and associated behavioural exceptions. These issues are each grouped under one of the following social responsibility core subjects: Human Rights; Labour practices; the Environment; Fair operating practices; Consumer issues; and Community involvement and development. The fact that these core subjects map onto different areas of legislation in many jurisdictions internally also eases the mapping of guidelines using this structure onto regulatory or other legal obligations that must be complied with by an organisation’s management system. ISO26000 does not specifically address the use of AI, however these issues are sufficiently broad to provide a basis for mapping out specific ethical and societal issues associated with AI as shown through a comparison [57] with the normative language on trustworthy AI principles established in [2, 4, 5]. ISO already provides guidance in [58] on how to map principles and issues from ISO 26000 to the high level structure of MSS. SC 42 also recognises the importance of ISO 26000 guidance in handling stakeholder issues in the draft standards on AI governance [31] and AI risk management [24], with the draft technical report on ethical and societal concerns of AI [22] including a high level mapping of ISO26000 core issues onto AI risks and treatments.
9. Conclusions and further work
This chapter has highlighted the multiplicity of parallel activities being undertaken in developing international standards, regulations and individual organisational policies related to AI and its trustworthiness characteristics. The current lack of mappings between these activities presents the danger of a highly fragmented global landscape emerging in AI trustworthiness. This could present society, government and industry with competing standards, regulations and organisational practices that will then serve to undermine rather than build trust in AI. This chapter presents an overview of AI standardisation currently being undertaken in ISO/IEC JTC 1/SC 42 and identifies its work to define an AI management system standard as the starting point for establishing conceptual mapping between different initiatives. A minimal, high level ontology for the support of conceptual mapping between different standardisation, regulatory and organisational policy documents is presented. We show how this can help map out the overlaps and gaps between AI governance, management and technical operations activities present in some of the SC 42 standards currently under development.
Further work is required to develop and maintain a mapping between the ontological concepts and relationships identified from the emerging set of SC 42 AI standards and the emerging trustworthy AI regulations and policies from different organisations. The mapping of such standards to the ontology could be made publicly available in a findable, accessible, interoperable and reusable form, using linked open data principles [43], and updated as the referenced specifications evolve. This will assist in identifying gaps and inconsistencies between evolving drafts, especially in developing the AIMS standard [20]. The set of trustworthy AI characteristics could be captured in the ontology, based in the first instance on the AI engineering quality characteristic being developed in [26]. Similarly, the ontology can be extended to express sets of AI risks and treatments so concepts developed in AI risk [24] and bias [23] will also be captured.
The use of this ontology-based approach for comparing the guidance between standards could also be applied between SC 42 and the largely orthogonal set of standards being developed under P7000. These include ethical design processes, transparency for autonomous systems, algorithmic bias, children, student and employee data governance, AI impact on human well-being, and trustworthiness rating for news sources.
Draft legislations for AI such as [59] will need to be analysed in terms of activities, actors, entities, characteristics and risks so that a mapping to the equivalent concepts from the SC 42 specifications family can be assembled and maintained. Similar analyses will be undertaken on publicly available policies from international bodies such as the EU High Level Expert Group on AI’s checklist for trustworthy AI [60] and the proposals emerging from the private sector for assigning trustworthiness declaration to products and services [47, 48, 49, 50, 51].
Acknowledgments
This work was conducted by the ADAPT Centre with support of SFI, by the European Union’s Horizon 2020 programme under the Marie Skłodowska-Curie Gran Grant Agreement No. 813497 and by the Irish Research Council Government of Ireland Postdoctoral Fellowship Grant GOIPD/2020/790. The ADAPT SFI Centre for Digital Content Technology is funded by Science Foundation Ireland through the SFI Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) through Grant # 13/RC/2106_P2.
Conflict of interest
David Filip is the convener of ISO/IEC JTC 1/SC 42/WG3 and Dave Lewis is a contributor to several of the SC 42 standardisation projects covered in this chapter.
Thanks
Thanks to Delaram Golpayegani, Arthit Suriyawongkul and PJ Wall of the ADAPT Centre for their pertinent discussion and comments on the subject of this chapter.
\n',keywords:"Trustworthy AI, AI Ethics, AI Governance, AI Standards, Ontology",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76436.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76436.xml",downloadPdfUrl:"/chapter/pdf-download/76436",previewPdfUrl:"/chapter/pdf-preview/76436",totalDownloads:328,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 18th 2020",dateReviewed:"March 29th 2021",datePrePublished:"May 7th 2021",datePublished:"October 27th 2021",dateFinished:"April 24th 2021",readingETA:"0",abstract:"Worldwide, there are a multiplicity of parallel activities being undertaken in developing international standards, regulations and individual organisational policies related to AI and its trustworthiness characteristics. The current lack of mappings between these activities presents the danger of a highly fragmented global landscape emerging in AI trustworthiness. This could present society, government and industry with competing standards, regulations and organisational practices that will then serve to undermine rather than build trust in AI. This chapter presents a simple ontology that can be used for checking the consistency and overlap of concepts from different standards, regulations and policies. The concepts in this ontology are grounded in an overview of AI standardisation currently being undertaken in ISO/IEC JTC 1/SC 42 and identifies its project to define an AI management system standard (AIMS or ISO/IEC WD 42001) as the starting point for establishing conceptual mapping between different initiatives. We propose a minimal, high level ontology for the support of conceptual mapping between different documents and show in the first instance how this can help map out the overlaps and gaps between and among SC 42 standards currently under development.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76436",risUrl:"/chapter/ris/76436",signatures:"Dave Lewis, David Filip and Harshvardhan J. Pandit",book:{id:"10576",type:"book",title:"Factoring Ethics in Technology, Policy Making, Regulation and AI",subtitle:null,fullTitle:"Factoring Ethics in Technology, Policy Making, Regulation and AI",slug:"factoring-ethics-in-technology-policy-making-regulation-and-ai",publishedDate:"October 27th 2021",bookSignature:"Ali G. Hessami and Patricia Shaw",coverURL:"https://cdn.intechopen.com/books/images_new/10576.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-161-4",printIsbn:"978-1-83969-160-7",pdfIsbn:"978-1-83969-162-1",isAvailableForWebshopOrdering:!0,editors:[{id:"108303",title:"Prof.",name:"Ali G.",middleName:null,surname:"Hessami",slug:"ali-g.-hessami",fullName:"Ali G. Hessami"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"340280",title:"Associate Prof.",name:"Dave",middleName:null,surname:"Lewis",fullName:"Dave Lewis",slug:"dave-lewis",email:"dave.lewis@adaptcentre.ie",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"340285",title:"Dr.",name:"David",middleName:null,surname:"Filip",fullName:"David Filip",slug:"david-filip",email:"david.filip@adaptcentre.ie",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Trinity College Dublin",institutionURL:null,country:{name:"Ireland"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Challenges of building international consensus on governing trustworthy AI",level:"1"},{id:"sec_3",title:"3. JTC1 standards related to trustworthy AI",level:"1"},{id:"sec_4",title:"4. Conceptual modelling approach",level:"1"},{id:"sec_5",title:"5. Ontology for semantic interoperability between trustworthy AI documents",level:"1"},{id:"sec_6",title:"6. Modelling of AI management system activities",level:"1"},{id:"sec_7",title:"7. Modelling of AIMs technical operation activities",level:"1"},{id:"sec_8",title:"8. Modelling stakeholders",level:"1"},{id:"sec_9",title:"9. Conclusions and further work",level:"1"},{id:"sec_10",title:"Acknowledgments",level:"1"},{id:"sec_13",title:"Conflict of interest",level:"1"},{id:"sec_10",title:"Thanks",level:"1"}],chapterReferences:[{id:"B1",body:'Adamson, G., Havens, J. 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If the crisis is monitored promptly and appropriate measures are taken, not only may the enterprise continue to operate but it may also be able to seize opportunities for growth. The Italian legislator is introducing a procedure aimed at supporting companies to detect the very first warning signs of a crisis. The supervisory board of auditors, the audit firm, and certain qualified creditors will have the right and duty to start the early warning procedure (“allerta”). The board of statutory auditors (Collegio Sindacale) plays a fundamental role: its ex-ante supervisory and control activities over management allow it to effectively play an important role as main recipient of any crisis warning signs. The new regulatory framework lays down certain indicators and critical thresholds, which may trigger the alert process. Initially, the Delegated Legislation (Bill No.3671-bis) sets forth certain specific financial indicators. The new bill (Crisis and Insolvency Code) on the contrary refers to a more complex and sector-specific system of indicators. 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Other similarities include the accurate diagnosis of the real causes of the crisis, the forbiddance of the dissemination of false news and the reassurance of the public opinion that there is a solution to the crisis, a sound management decision, and a good plan for its implementation. We link the past time crises to the contemporary era, providing a comparison framework. The history of crisis tends to show us that the study of crisis management cannot be linked to a specific civilization or era, especially when humanity had witnessed multiple and complex environmental, political, economic, and military crisis. Moreover, some of the problems and complex issues in the modern era are rooted in history. Thus, many geopolitical crises nowadays are the result of old causes. The study of crisis management from an academic point of view should be a multifaceted analysis, including a historical, a cultural, and an anthropological one, which determines the course of evolution and consequences of the crisis.",book:{id:"6620",slug:"crisis-management-theory-and-practice",title:"Crisis Management",fullTitle:"Crisis Management - Theory and Practice"},signatures:"Khaled Zamoum and Tevhide Serra Gorpe",authors:[{id:"230918",title:"Prof.",name:"T. Serra",middleName:null,surname:"Gorpe",slug:"t.-serra-gorpe",fullName:"T. 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Other similarities include the accurate diagnosis of the real causes of the crisis, the forbiddance of the dissemination of false news and the reassurance of the public opinion that there is a solution to the crisis, a sound management decision, and a good plan for its implementation. We link the past time crises to the contemporary era, providing a comparison framework. The history of crisis tends to show us that the study of crisis management cannot be linked to a specific civilization or era, especially when humanity had witnessed multiple and complex environmental, political, economic, and military crisis. Moreover, some of the problems and complex issues in the modern era are rooted in history. Thus, many geopolitical crises nowadays are the result of old causes. The study of crisis management from an academic point of view should be a multifaceted analysis, including a historical, a cultural, and an anthropological one, which determines the course of evolution and consequences of the crisis.",book:{id:"6620",slug:"crisis-management-theory-and-practice",title:"Crisis Management",fullTitle:"Crisis Management - Theory and Practice"},signatures:"Khaled Zamoum and Tevhide Serra Gorpe",authors:[{id:"230918",title:"Prof.",name:"T. Serra",middleName:null,surname:"Gorpe",slug:"t.-serra-gorpe",fullName:"T. 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Emdad Haque"},{id:"168399",title:"Mr.",name:"Mohammed S",middleName:null,surname:"Uddin",slug:"mohammed-s-uddin",fullName:"Mohammed S Uddin"}]},{id:"74444",title:"Flood Disaster Hazards; Causes, Impacts and Management: A State-of-the-Art Review",slug:"flood-disaster-hazards-causes-impacts-and-management-a-state-of-the-art-review",totalDownloads:731,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Floods are among disasters that cause widespread destruction to human lives, properties and the environment every year and occur at different places with varied scales across the globe. Flood disasters are caused by natural phenomena, but their occurrences and impacts have been intensified through human actions and inactions. The practice of flood disaster management have evolved over the years from traditional approaches of ad-hoc response measures to integrated approaches involving technologically advanced tools in flood disaster awareness, preparedness and response measures. This chapter proffers understanding into flood disaster awareness, preparedness and management, mitigation and adaptation strategies. Most importantly, the chapter presents a review on the relevance of modern technological tools namely Geographic Information System, Remote Sensing, Internet of Things and Big Data, that are available to flood managers, in the creation of efficient early warnings and Flood decision support systems that elevates the resilience of societies to flood disasters.",book:{id:"7712",slug:"natural-hazards-impacts-adjustments-and-resilience",title:"Natural Hazards",fullTitle:"Natural Hazards - Impacts, Adjustments and Resilience"},signatures:"Frank Jerome Glago",authors:[{id:"325046",title:"M.A.",name:"Frank Jerome",middleName:null,surname:"Glago",slug:"frank-jerome-glago",fullName:"Frank Jerome Glago"}]},{id:"64604",title:"Evidence-Based Contingency Planning to Enhance Local Resilience to Flood Disasters",slug:"evidence-based-contingency-planning-to-enhance-local-resilience-to-flood-disasters",totalDownloads:1480,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"The Sendai Framework for Disaster Risk Reduction 2015–2030 addresses the importance of “Enhancing disaster preparedness for effective response and to ‘Build Back Better’ in recovery, rehabilitation and reconstruction” as the fourth priority action. One of the practical tools to achieve effective preparedness for flood disaster response is evidence-based contingency planning, which is based on scientific approaches such as flood simulation and quantitative risk assessment. This method, however, is not always feasible to disaster-prone areas in Asia due to the lack of data on natural and social conditions. This chapter proposes a method with six steps for local communities to conduct contingency planning by assuming the dynamic change of inundation using flood simulation, assessing flood risk with key indicators, deciding response strategies against the identified flood risk and developing a contingency plan beforehand. This method was first applied to one of the Asian flood-prone areas, Calumpit Municipality in the Pampanga River basin of the Philippines, to verify its effectiveness in areas where the availability of natural and socio-economic data is limited.",book:{id:"8375",slug:"recent-advances-in-flood-risk-management",title:"Recent Advances in Flood Risk Management",fullTitle:"Recent Advances in Flood Risk Management"},signatures:"Miho Ohara, Naoko Nagumo, Badri Bhakta Shrestha and Hisaya Sawano",authors:[{id:"261112",title:"Dr.",name:"Miho",middleName:null,surname:"Ohara",slug:"miho-ohara",fullName:"Miho Ohara"},{id:"264405",title:"Dr.",name:"Badri",middleName:"Bhakta",surname:"Shrestha",slug:"badri-shrestha",fullName:"Badri Shrestha"},{id:"270525",title:"Mr.",name:"Hisaya",middleName:null,surname:"Sawano",slug:"hisaya-sawano",fullName:"Hisaya Sawano"},{id:"272127",title:"Dr.",name:"Naoko",middleName:null,surname:"Nagumo",slug:"naoko-nagumo",fullName:"Naoko Nagumo"}]},{id:"42656",title:"Conceptual Frameworks of Vulnerability Assessments for Natural Disasters Reduction",slug:"conceptual-frameworks-of-vulnerability-assessments-for-natural-disasters-reduction",totalDownloads:9979,totalCrossrefCites:18,totalDimensionsCites:75,abstract:null,book:{id:"3054",slug:"approaches-to-disaster-management-examining-the-implications-of-hazards-emergencies-and-disasters",title:"Approaches to Disaster Management",fullTitle:"Approaches to Disaster Management - Examining the Implications of Hazards, Emergencies and Disasters"},signatures:"Roxana L. Ciurean, Dagmar Schröter and Thomas Glade",authors:[{id:"163703",title:"Prof.",name:"Thomas",middleName:null,surname:"Glade",slug:"thomas-glade",fullName:"Thomas Glade"},{id:"164141",title:"Ph.D. Student",name:"Roxana",middleName:"Liliana",surname:"Ciurean",slug:"roxana-ciurean",fullName:"Roxana Ciurean"},{id:"164142",title:"Dr.",name:"Dagmar",middleName:null,surname:"Schroeter",slug:"dagmar-schroeter",fullName:"Dagmar Schroeter"}]}],onlineFirstChaptersFilter:{topicId:"1326",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:36,paginationItems:[{id:"82195",title:"Endoplasmic Reticulum: A Hub in Lipid Homeostasis",doi:"10.5772/intechopen.105450",signatures:"Raúl Ventura and María Isabel Hernández-Alvarez",slug:"endoplasmic-reticulum-a-hub-in-lipid-homeostasis",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}},{id:"82409",title:"Purinergic Signaling in Covid-19 Disease",doi:"10.5772/intechopen.105008",signatures:"Hailian Shen",slug:"purinergic-signaling-in-covid-19-disease",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82374",title:"The Potential of the Purinergic System as a Therapeutic Target of Natural Compounds in Cutaneous Melanoma",doi:"10.5772/intechopen.105457",signatures:"Gilnei Bruno da Silva, Daiane Manica, Marcelo Moreno and Margarete Dulce Bagatini",slug:"the-potential-of-the-purinergic-system-as-a-therapeutic-target-of-natural-compounds-in-cutaneous-mel",totalDownloads:9,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82103",title:"The Role of Endoplasmic Reticulum Stress and Its Regulation in the Progression of Neurological and Infectious Diseases",doi:"10.5772/intechopen.105543",signatures:"Mary Dover, Michael Kishek, Miranda Eddins, Naneeta Desar, Ketema Paul and Milan Fiala",slug:"the-role-of-endoplasmic-reticulum-stress-and-its-regulation-in-the-progression-of-neurological-and-i",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}}]},overviewPagePublishedBooks:{paginationCount:32,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science\nand Technology from the Department of Chemistry, National\nUniversity of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013.\nShe relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the\nNational Institute of Fundamental Studies from April 2013 to\nOctober 2016. She was a senior lecturer on a temporary basis at the Department of\nFood Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is\ncurrently Deputy Principal of the Australian College of Business and Technology –\nKandy Campus, Sri Lanka. She is also the Global Harmonization Initiative (GHI)",institutionString:"Australian College of Business & Technology",institution:null}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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In addition, he is also a Collaborating Professor in several Postgraduate programs at different universities all over the world.",institutionString:null,institution:{name:"Universidad Católica San Antonio de Murcia",country:{name:"Spain"}}},{id:"342152",title:"Dr.",name:"Santo",middleName:null,surname:"Grace Umesh",slug:"santo-grace-umesh",fullName:"Santo Grace Umesh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/342152/images/16311_n.jpg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"333647",title:"Dr.",name:"Shreya",middleName:null,surname:"Kishore",slug:"shreya-kishore",fullName:"Shreya Kishore",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333647/images/14701_n.jpg",biography:"Dr. Shreya Kishore completed her Bachelor in Dental Surgery in Chettinad Dental College and Research Institute, Chennai, and her Master of Dental Surgery (Orthodontics) in Saveetha Dental College, Chennai. She is also Invisalign certified. She’s working as a Senior Lecturer in the Department of Orthodontics, SRM Dental College since November 2019. She is actively involved in teaching orthodontics to the undergraduates and the postgraduates. Her clinical research topics include new orthodontic brackets, fixed appliances and TADs. She’s published 4 articles in well renowned indexed journals and has a published patency of her own. Her private practice is currently limited to orthodontics and works as a consultant in various clinics.",institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"323731",title:"Prof.",name:"Deepak M.",middleName:"Macchindra",surname:"Vikhe",slug:"deepak-m.-vikhe",fullName:"Deepak M. Vikhe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/323731/images/13613_n.jpg",biography:"Dr Deepak M.Vikhe .\n\n\t\n\tDr Deepak M.Vikhe , completed his Masters & PhD in Prosthodontics from Rural Dental College, Loni securing third rank in the Pravara Institute of Medical Sciences Deemed University. He was awarded Dr.G.C.DAS Memorial Award for Research on Implants at 39th IPS conference Dubai (U A E).He has two patents under his name. He has received Dr.Saraswati medal award for best research for implant study in 2017.He has received Fully funded scholarship to Spain ,university of Santiago de Compostela. He has completed fellowship in Implantlogy from Noble Biocare. \nHe has attended various conferences and CDE programmes and has national publications to his credit. His field of interest is in Implant supported prosthesis. Presently he is working as a associate professor in the Dept of Prosthodontics, Rural Dental College, Loni and maintains a successful private practice specialising in Implantology at Rahata.\n\nEmail: drdeepak_mvikhe@yahoo.com..................",institutionString:null,institution:{name:"Pravara Institute of Medical Sciences",country:{name:"India"}}},{id:"204110",title:"Dr.",name:"Ahmed A.",middleName:null,surname:"Madfa",slug:"ahmed-a.-madfa",fullName:"Ahmed A. Madfa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204110/images/system/204110.jpg",biography:"Dr. Madfa is currently Associate Professor of Endodontics at Thamar University and a visiting lecturer at Sana'a University and University of Sciences and Technology. He has more than 6 years of experience in teaching. His research interests include root canal morphology, functionally graded concept, dental biomaterials, epidemiology and dental education, biomimetic restoration, finite element analysis and endodontic regeneration. Dr. Madfa has numerous international publications, full articles, two patents, a book and a book chapter. Furthermore, he won 14 international scientific awards. Furthermore, he is involved in many academic activities ranging from editorial board member, reviewer for many international journals and postgraduate students' supervisor. Besides, I deliver many courses and training workshops at various scientific events. Dr. Madfa also regularly attends international conferences and holds administrative positions (Deputy Dean of the Faculty for Students’ & Academic Affairs and Deputy Head of Research Unit).",institutionString:"Thamar University",institution:null},{id:"210472",title:"Dr.",name:"Nermin",middleName:"Mohammed Ahmed",surname:"Yussif",slug:"nermin-yussif",fullName:"Nermin Yussif",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210472/images/system/210472.jpg",biography:"Dr. Nermin Mohammed Ahmed Yussif is working at the Faculty of dentistry, University for October university for modern sciences and arts (MSA). Her areas of expertise include: periodontology, dental laserology, oral implantology, periodontal plastic surgeries, oral mesotherapy, nutrition, dental pharmacology. She is an editor and reviewer in numerous international journals.",institutionString:"MSA University",institution:null},{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. He is now Head of the TMD Clinic at Prosthodontic Department of Faculty of Dentistry , Istanbul Aydın University , Turkey.",institutionString:"Istanbul Aydin University",institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"240870",title:"Ph.D.",name:"Alaa Eddin Omar",middleName:null,surname:"Al Ostwani",slug:"alaa-eddin-omar-al-ostwani",fullName:"Alaa Eddin Omar Al Ostwani",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/240870/images/system/240870.jpeg",biography:"Dr. Al Ostwani Alaa Eddin Omar received his Master in dentistry from Damascus University in 2010, and his Ph.D. in Pediatric Dentistry from Damascus University in 2014. Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",country:{name:"India"}}},{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null},{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178412/images/6954_n.jpg",biography:"Assoc. Prof. Dr. Gühan Dergin was born in 1973 in Izmit. He graduated from Marmara University Faculty of Dentistry in 1999. He completed his specialty of OMFS surgery in Marmara University Faculty of Dentistry and obtained his PhD degree in 2006. In 2005, he was invited as a visiting doctor in the Oral and Maxillofacial Surgery Department of the University of North Carolina, USA, where he went on a scholarship. Dr. Dergin still continues his academic career as an associate professor in Marmara University Faculty of Dentistry. He has many articles in international and national scientific journals and chapters in books.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178414/images/6953_n.jpg",biography:"Born in Istanbul in 1974, Dr. Emes graduated from Istanbul University Faculty of Dentistry in 1997 and completed his PhD degree in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery in 2005. He has papers published in international and national scientific journals, including research articles on implantology, oroantral fistulas, odontogenic cysts, and temporomandibular disorders. Dr. Emes is currently working as a full-time academic staff in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery.",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"192229",title:"Ph.D.",name:"Ana Luiza",middleName:null,surname:"De Carvalho Felippini",slug:"ana-luiza-de-carvalho-felippini",fullName:"Ana Luiza De Carvalho Felippini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192229/images/system/192229.jpg",biography:null,institutionString:"University of São Paulo",institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"256851",title:"Prof.",name:"Ayşe",middleName:null,surname:"Gülşen",slug:"ayse-gulsen",fullName:"Ayşe Gülşen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256851/images/9696_n.jpg",biography:"Dr. Ayşe Gülşen graduated in 1990 from Faculty of Dentistry, University of Ankara and did a postgraduate program at University of Gazi. \nShe worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. \nShe works as Craniofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Univeristy of Gazi",institution:null},{id:"255366",title:"Prof.",name:"Tosun",middleName:null,surname:"Tosun",slug:"tosun-tosun",fullName:"Tosun Tosun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255366/images/7347_n.jpg",biography:"Graduated at the Faculty of Dentistry, University of Istanbul, Turkey in 1989;\nVisitor Assistant at the University of Padua, Italy and Branemark Osseointegration Center of Treviso, Italy between 1993-94;\nPhD thesis on oral implantology in University of Istanbul and was awarded the academic title “Dr.med.dent.”, 1997;\nHe was awarded the academic title “Doç.Dr.” (Associated Professor) in 2003;\nProficiency in Botulinum Toxin Applications, Reading-UK in 2009;\nMastership, RWTH Certificate in Laser Therapy in Dentistry, AALZ-Aachen University, Germany 2009-11;\nMaster of Science (MSc) in Laser Dentistry, University of Genoa, Italy 2013-14.\n\nDr.Tosun worked as Research Assistant in the Department of Oral Implantology, Faculty of Dentistry, University of Istanbul between 1990-2002. \nHe worked part-time as Consultant surgeon in Harvard Medical International Hospitals and John Hopkins Medicine, Istanbul between years 2007-09.\u2028He was contract Professor in the Department of Surgical and Diagnostic Sciences (DI.S.C.), Medical School, University of Genova, Italy between years 2011-16. \nSince 2015 he is visiting Professor at Medical School, University of Plovdiv, Bulgaria. \nCurrently he is Associated Prof.Dr. at the Dental School, Oral Surgery Dept., Istanbul Aydin University and since 2003 he works in his own private clinic in Istanbul, Turkey.\u2028\nDr.Tosun is reviewer in journal ‘Laser in Medical Sciences’, reviewer in journal ‘Folia Medica\\', a Fellow of the International Team for Implantology, Clinical Lecturer of DGZI German Association of Oral Implantology, Expert Lecturer of Laser&Health Academy, Country Representative of World Federation for Laser Dentistry, member of European Federation of Periodontology, member of Academy of Laser Dentistry. Dr.Tosun presents papers in international and national congresses and has scientific publications in international and national journals. He speaks english, spanish, italian and french.",institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"256417",title:"Associate Prof.",name:"Sanaz",middleName:null,surname:"Sadry",slug:"sanaz-sadry",fullName:"Sanaz Sadry",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256417/images/8106_n.jpg",biography:null,institutionString:null,institution:null},{id:"272237",title:"Dr.",name:"Pinar",middleName:"Kiymet",surname:"Karataban",slug:"pinar-karataban",fullName:"Pinar Karataban",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272237/images/8911_n.png",biography:"Assist.Prof.Dr.Pınar Kıymet Karataban, DDS PhD \n\nDr.Pınar Kıymet Karataban was born in Istanbul in 1975. After her graduation from Marmara University Faculty of Dentistry in 1998 she started her PhD in Paediatric Dentistry focused on children with special needs; mainly children with Cerebral Palsy. She finished her pHD thesis entitled \\'Investigation of occlusion via cast analysis and evaluation of dental caries prevalance, periodontal status and muscle dysfunctions in children with cerebral palsy” in 2008. She got her Assist. Proffessor degree in Istanbul Aydın University Paediatric Dentistry Department in 2015-2018. ın 2019 she started her new career in Bahcesehir University, Istanbul as Head of Department of Pediatric Dentistry. In 2020 she was accepted to BAU International University, Batumi as Professor of Pediatric Dentistry. She’s a lecturer in the same university meanwhile working part-time in private practice in Ege Dental Studio (https://www.egedisklinigi.com/) a multidisciplinary dental clinic in Istanbul. Her main interests are paleodontology, ancient and contemporary dentistry, oral microbiology, cerebral palsy and special care dentistry. She has national and international publications, scientific reports and is a member of IAPO (International Association for Paleodontology), IADH (International Association of Disability and Oral Health) and EAPD (European Association of Pediatric Dentistry).",institutionString:null,institution:null},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202198/images/6955_n.jpg",biography:"Buket Aybar, DDS, PhD, was born in 1971. She graduated from Istanbul University, Faculty of Dentistry, in 1992 and completed her PhD degree on Oral and Maxillofacial Surgery in Istanbul University in 1997.\nDr. Aybar is currently a full-time professor in Istanbul University, Faculty of Dentistry Department of Oral and Maxillofacial Surgery. She has teaching responsibilities in graduate and postgraduate programs. Her clinical practice includes mainly dentoalveolar surgery.\nHer topics of interest are biomaterials science and cell culture studies. She has many articles in international and national scientific journals and chapters in books; she also has participated in several scientific projects supported by Istanbul University Research fund.",institutionString:null,institution:null},{id:"260116",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yaltirik",slug:"mehmet-yaltirik",fullName:"Mehmet Yaltirik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/260116/images/7413_n.jpg",biography:"Birth Date 25.09.1965\r\nBirth Place Adana- Turkey\r\nSex Male\r\nMarrial Status Bachelor\r\nDriving License Acquired\r\nMother Tongue Turkish\r\n\r\nAddress:\r\nWork:University of Istanbul,Faculty of Dentistry, Department of Oral Surgery and Oral Medicine 34093 Capa,Istanbul- TURKIYE",institutionString:null,institution:null},{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/172009/images/7122_n.jpg",biography:"Dr. Deniz Uzuner was born in 1969 in Kocaeli-TURKEY. After graduating from TED Ankara College in 1986, she attended the Hacettepe University, Faculty of Dentistry in Ankara. \nIn 1993 she attended the Gazi University, Faculty of Dentistry, Department of Orthodontics for her PhD education. After finishing the PhD education, she worked as orthodontist in Ankara Dental Hospital under the Turkish Government, Ministry of Health and in a special Orthodontic Clinic till 2011. Between 2011 and 2016, Dr. Deniz Uzuner worked as a specialist in the Department of Orthodontics, Faculty of Dentistry, Gazi University in Ankara/Turkey. In 2016, she was appointed associate professor. Dr. Deniz Uzuner has authored 23 Journal Papers, 3 Book Chapters and has had 39 oral/poster presentations. She is a member of the Turkish Orthodontic Society. Her knowledge of English is at an advanced level.",institutionString:null,institution:null},{id:"332914",title:"Dr.",name:"Muhammad Saad",middleName:null,surname:"Shaikh",slug:"muhammad-saad-shaikh",fullName:"Muhammad Saad Shaikh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jinnah Sindh Medical University",country:{name:"Pakistan"}}},{id:"315775",title:"Dr.",name:"Feng",middleName:null,surname:"Luo",slug:"feng-luo",fullName:"Feng Luo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sichuan University",country:{name:"China"}}},{id:"423519",title:"Dr.",name:"Sizakele",middleName:null,surname:"Ngwenya",slug:"sizakele-ngwenya",fullName:"Sizakele Ngwenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419270",title:"Dr.",name:"Ann",middleName:null,surname:"Chianchitlert",slug:"ann-chianchitlert",fullName:"Ann Chianchitlert",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419271",title:"Dr.",name:"Diane",middleName:null,surname:"Selvido",slug:"diane-selvido",fullName:"Diane Selvido",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419272",title:"Dr.",name:"Irin",middleName:null,surname:"Sirisoontorn",slug:"irin-sirisoontorn",fullName:"Irin Sirisoontorn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"355660",title:"Dr.",name:"Anitha",middleName:null,surname:"Mani",slug:"anitha-mani",fullName:"Anitha Mani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"355612",title:"Dr.",name:"Janani",middleName:null,surname:"Karthikeyan",slug:"janani-karthikeyan",fullName:"Janani Karthikeyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"334400",title:"Dr.",name:"Suvetha",middleName:null,surname:"Siva",slug:"suvetha-siva",fullName:"Suvetha Siva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}}]}},subseries:{item:{id:"10",type:"subseries",title:"Animal Physiology",keywords:"Physiology, Comparative, Evolution, Biomolecules, Organ, Homeostasis, Anatomy, Pathology, Medical, Cell Division, Cell Signaling, Cell Growth, Cell Metabolism, Endocrine, Neuroscience, Cardiovascular, Development, Aging, Development",scope:"Physiology, the scientific study of functions and mechanisms of living systems, is an essential area of research in its own right, but also in relation to medicine and health sciences. The scope of this topic will range from molecular, biochemical, cellular, and physiological processes in all animal species. Work pertaining to the whole organism, organ systems, individual organs and tissues, cells, and biomolecules will be included. Medical, animal, cell, and comparative physiology and allied fields such as anatomy, histology, and pathology with physiology links will be covered in this topic. Physiology research may be linked to development, aging, environment, regular and pathological processes, adaptation and evolution, exercise, or several other factors affecting, or involved with, animal physiology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/10.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11406,editor:{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,series:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261"},editorialBoard:[{id:"306970",title:"Mr.",name:"Amin",middleName:null,surname:"Tamadon",slug:"amin-tamadon",fullName:"Amin Tamadon",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002oHR5wQAG/Profile_Picture_1623910304139",institutionString:null,institution:{name:"Bushehr University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón",slug:"juan-carlos-gardon",fullName:"Juan Carlos 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