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Data and Visual Analytics for Cyber-physical Systems: Current Situation and Strategies for Action
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.ORCID iD: 0000-0002-8853-4159
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Today, cyber-physical systems (CPS) exist everywhere in different sizes, with different functionalities and capabilities. CPS often support critical missions that have significant economic and societal importance. They require software systems, communications technologies, sensors/actuators, embedded technologies, and physical systems to work together seamlessly, and they are seen as a driving force behind digital transformation. This dissertation describes the research work carried out to investigate applicability of data and visual analytics for CPS to overcome three main challenges: interoperability, complexity, and sustainability.

To this end, several case studies are used to effectively implement and test different data and visual analytics solutions to aid stakeholders when they make decisions on interoperability, complexity, and sustainability for CPS. These studies raised questions about issues found to be of importance for the success of data and visual analytics approaches, including accessibility, availability, quality, volume, and variety of data—issues. Moreover, additional studies are used to show the benefits of blending different approaches, such as systems thinking and design thinking, and the current data analytics readiness of the Swedish industry is assessed through a questionnaire completed by more than a hundred respondents. 

The data and visual analytics are positioned between digitalization and machine intelligence as a research focus. Data and visual analytics is the next step after digitalizing the information by adding analytical capabilities to the data. It is also an important phase before developing machine intelligence applications. Earlier studies clearly show that only a fraction of companies have machine intelligence applications across the enterprise. One important reason behind this is the lack of strong digital capabilities that big data and advanced data analytics technologies could bring. The findings of the work carried out as part of this thesis show the importance of this middle phase—data and visual analytics—for the success of not only the CPS but also these two concepts—digitalization and machine intelligence.

This thesis concludes by highlighting that currentdata and visual analytics approaches in CPS are closely dependent onthe availability, accessibility, quality, volume, and variety of the data. Notably, the huge amount of industrial data that exists in CPS manufacturers data repositories does not always mean that this data is useful, especially for analytical purposes. To this end, firstly, the CPS industry should concentrate its efforts to collect useful data that will benefit the industry by providing analytical insight intothe environments where CPS is produced and operated. Secondly, the industry should make necessary organizational changes such as considering to employ data scientists, analysts, and business intelligence developers and make data accessible tothese people for further usage. Thirdly, the data management procedures and data analytics roadmaps of companies should be created and shared with other employees, and necessary mechanisms needto be considered to improve and guarantee the quality of the data. Lastly, the variety of data needs to be addressed by the industry. 

Data and visual analytics provides an opportunity to extract patterns; to evaluate the interoperability, complexity, and sustainability; to create an overview of the current challenge by providing different viewpoints adapted to different stakeholders, focusing on key concerns for the respective stakeholder; to optimize performance, automation, and cooperation of distributed CPS, development environment, and teams; and overall, to improve any of the challenges that are mentioned above by, basically, providing a better understanding.

To this end, I suggest that the industry discuss the next step after digitalization and address the challenges related to the availability, accessibility, quality, volume, and variety of data by considering user-centric approaches and organizational needs of the future development and manufacturing environments. The CPS industry should plan and act on these challenges as part of its data analytics strategies to expedite the machine intelligence applications of the future.

Abstract [sv]

Idag finns cyber-fysiska system (CPS) överallt i olika storlekar, med olika funktioner och kapaciteter. CPS stöder ofta uppdrag av betydande ekonomisk och samhällelig vikt. De kräver mjukvarusystem, kommunikationsteknik, sensorer, inbyggd teknik och fysiska system för att fungera sömlöst och de ses som en drivkraft bakom digital transformation. Denna avhandling sammanfattar det arbete som gjorts för att förstå hur data och visuell analys kan tillämpas så att CPS kan övervinna de tre huvudutmaningarna: interoperabilitet, komplexitet och hållbarhet.

För detta ändamål används flera fallstudier som ett effektivt sätt att implementera och testa olika data- och visuella analyslösningar för att hjälpa intressenter när de fattar beslut om interoperabilitet, komplexitet och hållbarhet gällande CPS. Dessa studier har väckt frågor som visat sig vara viktiga för framgången för  data- och visuella analysmetoder vad gäller tillgång, åtkomst, kvalitet, volym och mängd avseende data. Vidare används ytterligare studier för att visa fördelarna med att blanda olika tillvägagångssätt som systemtänkande och designtänkande, och den svenska industrins nuvarande dataanalysberedskap utvärderas genom ett frågeformulär som har besvarats av mer än hundra respondenter.

Data- och visuella analyser är som forskningsfokus placerade mellan digitalisering och maskinintelligens. Data och visuell analys ses som nästa steg efter digitalisering av informationen genom att lägga till analytiska möjligheter till tillgänglig data. Det är också en viktig fas innan utveckling av maskinintelligens kan ske. Tidigare studier visar tydligt att endast en bråkdel av undersökta företag använder maskinintelligens i större skala. En viktig orsak bakom detta är bristen på kraftfulla digitala kapaciteter som avancerad teknik som stor data och dataanalysteknik skulle kunna ge. Resultaten av det arbete som skett inom ramen för denna avhandling visar vikten av denna mellanfas - data- och visuell analys - för inte bara framgången för CPS utan också framgången för dessa två begrepp-digitalisering och maskinintelligens.

Avhandlingen avslutades genom att betona att nuvarande data- och visuella analysmetoder i CPS är beroende av tillgång, åtkomst, kvalitet, volym och mängd av data. Det är viktigt att påminna om att den enorma mängd industriell data som finns i CPS-tillverkarnas datalager inte alltid betyder att dessa data är användbara, särskilt för analytiska ändamål. För detta ändamål bör CPS-industrin i första hand rikta in sig på att samla in användbar data som kommer att gynna dem genom att ge analytisk inblick i de miljöer där CPS produceras och drivs. För det andra bör industrin göra nödvändiga organisatoriska förändringar som att anställa datatekniker, analytiker och affärsintelligensutvecklare samt göra relevant data tillgänglig för vidare användning av dessa personer. För det tredje bör industrin skapa databehandlingsförfaranden och dataanalysfärdplaner som kan delas med andra anställda och nödvändiga mekanismer måste utformas för att förbättra och garantera datans kvalitet. Slutligen måste industrin uppmärksamma mångfalden av data.

Data och visuell analys ger möjligheter att upptäcka mönster; att utvärdera interoperabilitet, hållbarhet och komplexitet; att skapa en överblick över den aktuella utmaningen genom att ge olika intressenter olika anpassade synpunkter och fokusera på viktiga frågeställningar för respektive intressent; att optimera prestanda, automatisering och samarbete av distribuerade CPS, utvecklingsmiljöer och arbetsgrupper; och sammantaget, att förbättra de utmaningar som nämns ovan genom att i grund och botten ge en bättre förståelse.

För detta ändamål föreslår jag att industrin diskuterar nästa steg efter digitaliseringen och tar itu med utmaningarna som finns avseende tillgång, åtkomst, kvalitet, volym och mängd av data genom att undersöka användarcentrerade tillvägagångssätt och organisatoriska behov i framtida utvecklings- och tillverkningsmiljöer. CPS-industrin bör planera och agera på dessa utmaningar som en del av deras dataanalysstrategier för att påskynda framtida användningsområden för maskinintelligens.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. , p. 57
Series
TRITA-ITM-AVL ; 2018:50
National Category
Mechanical Engineering
Research subject
Machine Design
Identifiers
URN: urn:nbn:se:kth:diva-239575ISBN: 978-91-7729-974-5 (print)OAI: oai:DiVA.org:kth-239575DiVA, id: diva2:1266060
Public defence
2019-01-25, Kollegiesalen, Brinellvägen 8, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20190111

Available from: 2018-12-12 Created: 2018-11-27 Last updated: 2019-01-11Bibliographically approved
List of papers
1. A Systematic Review to Merge Discourses: Interoperability, Integration and Cyber-Physical Systems
Open this publication in new window or tab >>A Systematic Review to Merge Discourses: Interoperability, Integration and Cyber-Physical Systems
2017 (English)In: Journal of Industrial Information Integration, ISSN 2452-414XArticle in journal (Refereed) Published
Abstract [en]

Cyber-physical systems (CPS) are developed through the cooperation of several engineering disciplines. Powerful software tools are utilized by each individual discipline, but it remains challenging to connect these into tool chains for increased efficiency. To support this endeavour, the literature on interoperability assessment was surveyed to identify concepts valuable to transfer from the interoperability to the tool integration research field.

Implementation options, types of interoperability and domains described in interoperability assessment models were concepts identified as directly transferable. To avoid the problems with uptake that plague the models identified, visual analytics is suggested as a vehicle for the transfer. Furthermore, based on the use of non-functional properties as an underlying motivation for these models, cost, performance and sustainability are suggested as a common base for future research in both discourses.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Tool interoperability; Tool integration; Interoperability measurement; Interoperability assessment; Maturity models, and data visualization
National Category
Computer Systems
Research subject
Machine Design
Identifiers
urn:nbn:se:kth:diva-219951 (URN)10.1016/j.jii.2017.12.001 (DOI)000427762400002 ()2-s2.0-85044008633 (Scopus ID)
Note

QC 20171218

Available from: 2017-12-14 Created: 2017-12-14 Last updated: 2019-01-11Bibliographically approved
2. Making Interoperability Visible: Data Visualization of Cyber-Physical Systems Development Tool Chains
Open this publication in new window or tab >>Making Interoperability Visible: Data Visualization of Cyber-Physical Systems Development Tool Chains
2016 (English)In: Journal of Industrial Information Integration, ISSN 2452-414X, Vol. 4, p. 26-34Article in journal (Refereed) Published
Abstract [en]

Cyber-physical system (CPS) development cycles include various engineering disciplines, artefacts, and areas of expertise. There are powerful software tools used in these cycles, which can be put to good use by one individual discipline but are challenging to integrate with other tools. This paper discusses a node-link diagram (NLD) visualization technique that can be used to visualize interoperability in CPS development tool chains. The purpose is to help tool chain developers assess the current interoperability status and make decisions on integration scenarios accordingly. To this end, a case study is introduced and explained as an example. Data about the tool chain, which contains different structures relating to the software tools usage and the interactions between them, are provided by ABB Corporate Research Centre. These structures are used in creation of visualizations for making interoperability visible in CPS development tool chains and applicability of each visualization technique is discussed with the interoperability perspective. In this study, we also exercised a clustering algorithm for an extended case study and discussed the use of visualizations to improve interoperability in CPS development environments.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Cyber-physical system interoperability; Interoperability visualization; Development tool chains; Data visualization; Node-link diagram
National Category
Computer Systems
Research subject
Information and Communication Technology; Machine Design; Computer Science
Identifiers
urn:nbn:se:kth:diva-196530 (URN)10.1016/j.jii.2016.09.002 (DOI)
Note

QC 20161206

Available from: 2016-11-15 Created: 2016-11-15 Last updated: 2019-01-11Bibliographically approved
3. Data Visualization Support for Complex Logistics Operations and Cyber-physical Systems
Open this publication in new window or tab >>Data Visualization Support for Complex Logistics Operations and Cyber-physical Systems
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Today, complex logistics operations include different levels of communication and interactions. This paper explores the requirements of these operations and conceptualizes important key performance indicators, stakeholders, and different data visualizations to support the stakeholders in order to understand interactions between entities easier and faster. Three different levels were identified-supply chain, automated warehouse, and intelligent agent-to define the complex logistics operations. For each level, important stakeholders and performance indicators were determined. A case study was designed and described to exemplify the role of cyber-physical systems in complex logistics operations. Moreover, different data visualizations were developed as part of a dashboard to illustrate key performance indicators of different levels for the purpose of supporting stakeholders. This exploratory study concludes by identifying important data necessary for each performance indicator, suggesting ways to collect these data, and exemplifying how data visualization approach can be used through a dashboard design.

Keywords
Data Visualization, Information Visualization, Supply Chain, Automated Warehouse, Intelligent Agent, Cyber-Physical Systems, Dashboard Design, Soar
National Category
Engineering and Technology Embedded Systems
Research subject
Machine Design
Identifiers
urn:nbn:se:kth:diva-222144 (URN)
Conference
14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Projects
SCOTT - Secure Connected Trustable Things
Note

QC 20180327

Available from: 2018-02-01 Created: 2018-02-01 Last updated: 2019-01-11Bibliographically approved
4. Knowledge Representation of Cyber-physical Systems for Monitoring Purpose
Open this publication in new window or tab >>Knowledge Representation of Cyber-physical Systems for Monitoring Purpose
Show others...
2018 (English)In: 51st CIRP Conference on Manufacturing Systems, Elsevier, 2018, Vol. 72, p. 468-473Conference paper, Published paper (Refereed)
Abstract [en]

Automated warehouses, as a form of cyber-physical systems (CPSs), require several components to work collaboratively to address the common business objectives of complex logistics systems. During the collaborative operations, a number of key performance indicators (KPI) can be monitored to understand the proficiency of the warehouse and control the operations and decisions. It is possible to drive and monitor these KPIs by looking at both the state of the warehouse components and the operations carried out by them. Therefore, it is necessary to represent this knowledge in an explicit and formally-specified data model and provide automated methods to derive the KPIs from the representation. In this paper, we implement a minimalistic data model for a subset of warehouse resources using linked data in order to monitor a few KPIs, namely sustainability, safety and performance. The applicability of the approach and the data model is illustrated through a use case. We demonstrate that it is possible to develop minimalistic data models through Open Services for Lifecycle Collaboration (OSLC) resource shapes which enables compatibility with the declarative and procedural knowledge of automated warehouse agents specified in Planning Domain Definition Language (PDDL).

Place, publisher, year, edition, pages
Elsevier, 2018
Series
Procedia CIRP, ISSN 2212-8271 ; 72
National Category
Production Engineering, Human Work Science and Ergonomics Manufacturing, Surface and Joining Technology Computer and Information Sciences Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-228233 (URN)10.1016/j.procir.2018.03.018 (DOI)2-s2.0-85049594479 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, Stockholm Waterfront Congress Centre, Stockholm, Sweden, 16 May 2018 through 18 May 2018
Projects
SCOTT
Note

QC 20180530

Available from: 2018-05-21 Created: 2018-05-21 Last updated: 2019-01-11Bibliographically approved
5. Methodology for Linked Enterprise Data Quality Assessment Through Information Visualizations
Open this publication in new window or tab >>Methodology for Linked Enterprise Data Quality Assessment Through Information Visualizations
2018 (English)In: Journal of Industrial Information Integration, ISSN 2452-414XArticle in journal (Refereed) Published
Abstract [en]

Today’s development environments in the manufacturing industry require different development tools to work together. These complex environments are highly heterogeneous and constantly changing, and the development tools are producing a huge amount of data. As a result, these development environments must overcome a significant problem related to data integration. In this paper, we examine a case study from the automotive industry using the linked enterprise data approach to integrate data from different development tools. The study explains and applies a data quality assessment methodology as a post-integration phase for linked enterprise data. 

In this study, important data quality dimensions from the literature are merged with empirical rules that have been defined by Scania CV AB employees. As a result, a comprehensive methodology is developed and introduced to assess these data quality dimensions. This paper presents the methodology, which aims to develop a data quality assessment tool—a dashboard—in addition to policies and protocols to manage data quality. Moreover, the proposed methodology includes systematic guidelines for planning the data quality assessment activity, extracting requirements for the data quality management, setting priorities to expedite the adaptation, identifying dimensions and metrics to ease the understanding, and visualizing these dimensions and metrics to assess the overall data quality.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
data quality, linked data, quality assessment, linked enterprise data, information visualization, methodology
National Category
Computer and Information Sciences Embedded Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-232357 (URN)10.1016/j.jii.2018.11.002 (DOI)
Note

QC 20180822

Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2019-01-11Bibliographically approved
6. Visual Analytics for Cyber-physical Systems Development: Blending Design Thinking and Systems Thinking
Open this publication in new window or tab >>Visual Analytics for Cyber-physical Systems Development: Blending Design Thinking and Systems Thinking
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Cyber-physical systems (CPS) are integrations of computational and physical processes. They represent a new generation of systems that interact with humans and expand the capabilities of the physical world through computation, communication, and control. At the same time, actions and interventions associated with this complex systems can have highly unpredictable and unintended consequences. Furthermore, today’s practices of CPS design and implementation are not able to support the level of complexity required to detect these consequences. 

One methodology to approach this complex problem space is systems thinking (ST). Systems thinking emerges as both a worldview and a process in the sense that it informs one's understanding regarding a system and can be used as a problem-solving approach. Systems thinking is an abstraction-oriented analysis approach, specifically designed for heterogeneous complex systems.

At the same time, another methodology, design thinking (DT), has enjoyed significantly increased visibility and importance over the last decade. Design thinking is a creative problem-solving approach, which puts human to the center and focuses first on the needs and experiences of the user.

This paper aims to illustrate the possibility to use design thinking and systems thinking methodologies together to better deal with the complexity related problems during CPS design and implementation. The study proposes visual analytics as an integrative tool between these two methodologies, by (1) analyzing and understanding CPS development process through systems thinking, and (2) innovating and transforming the process through design thinking. To this end, an example use case is described and the application of the blended methodology explained step by step in relation to the use case. Visual analytics and data visualization are discussed in several steps and the possible benefits highlighted.

Keywords
design thinking, systems thinking, data analytics, cyber-physical systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-232358 (URN)
Conference
15th Annual NordDesign Conference (NordDesign 2018)
Note

QC 20180822

Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2019-01-11Bibliographically approved
7. Interoperable Toolchains in Cyber-physical Systems with a Sustainability Perspective
Open this publication in new window or tab >>Interoperable Toolchains in Cyber-physical Systems with a Sustainability Perspective
2018 (English)In: 2017 IEEE Conference on Technologies for Sustainability, SusTech 2017, Phoenix, AZ, USA: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper, Published paper (Refereed)
Abstract [en]

The development of cyber-physical systems (CPS) requires various engineering disciplines, artifacts, and areas of expertise to collaborate. Powerful software tools are used during this development process, but while successful in one individual discipline, it is often challenging to integrate with other tools. Several studies have been done on integration solutions for these toolchains. However, the possibility of including the sustainability concept to the interoperability strategies is rarely studied. This paper discusses an approach to include sustainability aspects while improving the interoperability of toolchains in CPS manufacturing. To this end, an automobile manufacturing process has been studied as a use case, and relevant sustainability metrics for each stage of the process are identified. Life cycle sustainability assessment methodology is used to identify the sustainability metrics, and the use case is employed to exemplify how some of these metrics can be integrated with interoperable toolchains to illustrate the applicability of the approach.

Place, publisher, year, edition, pages
Phoenix, AZ, USA: Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
toolchain interoperability, tool integration, interoperability, sustainability, life cycle sustainability assessment
National Category
Embedded Systems
Research subject
Machine Design
Identifiers
urn:nbn:se:kth:diva-217836 (URN)10.1109/SusTech.2017.8333471 (DOI)2-s2.0-85050475302 (Scopus ID)9781538604519 (ISBN)
Conference
5th Annual IEEE Conference on Technologies for Sustainability, SusTech 2017, Hilton Airport Hotel, Phoenix, United States, 12 November 2017 through 14 November 2017
Note

QC 20180111

Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2019-01-11Bibliographically approved
8. Visual Analytics to Support the Service Design for Sustainable Mobility
Open this publication in new window or tab >>Visual Analytics to Support the Service Design for Sustainable Mobility
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Intelligent transport system is a general term for the combined application of communication technologies, control and information processing for transport systems. Intelligent transport system covers all modes of transportation—including public transport—and all elements of the transportation system, such as the vehicle, infrastructure, and the driver. Integrated transport system allows a series of new unconventional solutions to improve the safety of the traffic and to satisfy transport requirements using new technologies. The service design of these systems, however, brings along different challenges. 

The process of service design requires the designers to engage with user behavior and understand the usage patterns related to the intelligent transport systems. Today, there are no well-developed methods to support this engagement. This paper suggests a data-oriented visual analytics approach to support designers in their decision-making processes, the implementation of successful services for sustainable, shared mobility service systems, and data-oriented approaches. Moreover, this paper discusses visual analytics as a tool to aid service designers by enabling real-time data analytics support.

To this end, this paper summarizes the current literature on system innovation, challenges related to the design of these systems for sustainability and presents a shared and connected mobility service case study to illustrate the benefits of having visual analytics platforms for sustainable and intelligent transport systems. The study concludes that intuitive, data-oriented, interactive visual analytics approach has the potential to support service designers to create a coherent picture of the user in the service design process.

Keywords
visual analytics, intelligent transport systems, service design, mobility as a service, data analytics, sustainability
National Category
Computer and Information Sciences Embedded Systems
Identifiers
urn:nbn:se:kth:diva-232355 (URN)
Conference
6th Annual IEEE Conference on Technologies for Sustainability (SusTech 2018)
Note

QCR 20180822

Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2019-01-11Bibliographically approved
9. Digitalizing Swedish Industry: What is Next?: Data Analytics Readiness Assessment of Swedish Industry According to Survey Results
Open this publication in new window or tab >>Digitalizing Swedish Industry: What is Next?: Data Analytics Readiness Assessment of Swedish Industry According to Survey Results
(English)In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194Article in journal (Refereed) Submitted
Abstract [en]

Digitalizationrefers to enabling, improving, and transforming operations, functions, models, processes, or activities by leveraging digital technologies. Furthermore, digitalization is considered one of the most powerful drivers of innovation with the potential to trigger the next wave of innovation. 

Today, the importance of digitalization is well-understood in Swedish government agencies and industry. Although there are several initiatives working to actively drive change, one question is key: What is the next step? Data analytics is a promising way to turn information into outcomes, enhance decision-making, make data-driven discoveries, minimize risk, and unearth valuable insights that would otherwise remain hidden. This paper presents survey results on data analytics adoption and usage within Swedish industry, to highlight post-digitalization industry needs. To this end, a questionnaire was designed and distributed. Answers from more than 100 respondents from the manufacturing, technology, engineering, telecommunications, and automotive industries in Sweden were collected and analyzed.

The assessment results show that Swedish industry has a high technological readiness score. This suggests that the necessary data, tools, and skills are in place. Moreover, its cultural readiness level, which focuses on the acceptance of data-driven decision-making, scores between high and very high. At the same time, the strategic readiness level is high, except in the telecommunication domain. However, the operational readiness level is between medium and low, which shows that the business impacts of data analytics are not in place. These findings suggest that the industry should use the advantages of the current cultural readiness and concentrate efforts on exploring the business impacts of data analytics, ensuring the support from executive managers, and implementing data analytics protocols to improve strategic and technological readiness. Moreover, proper planning, timing, budgeting, and setting of clear key performance indicators/metrics need to be considered systematically in order to ameliorate the operational readiness of data analytics.

Keywords
data analytics, data analytics readiness, Swedish industry, digitalization, survey
National Category
Computer Sciences Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-232354 (URN)
Note

QCR 20180822

Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2019-01-11Bibliographically approved

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