Collaborating Authors


AI supports assembly specialists


Artificial intelligence (AI) enables machines to recognize objects. For this purpose, large amounts of high-quality image data are required to manually train the algorithms. Kimoknow, a startup established at Karlsruhe Institute of Technology (KIT), has now developed a technology to automate this training. It is tested in cooperation with Elabo GmbH at the Center for Artificial Intelligence Talents (CAIT). "Training of AI systems for the recognition of objects still is time-consuming, inflexible, expensive, highly environment-dependent, and associated with a high computation expenditure," says Lukas Kriete, one of the founders of Kimoknow.

Europe's data revolution


The growth potential of the data economy is mind-blowing. In Europe alone, the figures and forecasts are eye-catching, to say the least. The European Commission expects the value of the data economy to rise to €829 billion by 2025, up from €301 billion in 2018. Focusing on the headline economic figures alone overlooks the enormous potential to use data to create lasting social change and improve the personal and professional lives of millions of European citizens. The term digital economy is a catch-all for a wide range of digital transformation activities.

Global Big Data Conference


The world is in the midst of a historical turning point. The COVID-19 pandemic has effectively halted life as we once knew it, and left the open question, "what will our world look like when'normal' life resumes?" While we don't have a crystal ball that allows us to peer into the future, history has given us a template on what to expect. Past pandemics have shaped politics, crashed economies, purred revolutions and produced other profound societal transformations. In the 14th century, the bubonic plague killed more than 60 percent of Europe's population – a dramatic population decline that actually improved living standards for the survivors and marked the decline in serfdom.

Artificial skin heals wounds and makes robots sweat


Imagine a dressing that releases antibiotics on demand and absorbs excessive wound exudate at the same time. Researchers at Eindhoven University of Technology hope to achieve just that, by developing a smart coating that actively releases and absorbs multiple fluids, triggered by a radio signal. This material is not only beneficial for the health care industry, it is also very promising in the field of robotics or even virtual reality. TU/e-researcher Danqing Liu, the lead author of this paper, and her Ph.D. student Yuanyuan Zhan are inspired by the skins of living creatures. Human skin secretes oil to defend against bacteria and sweats to regulate the body temperature.

The UK has only just begun to see the transformative potential of AI


Over the last year, our attention has been focused on a series of issues that are of global importance. Last year, Extinction Rebellion pushed climate change to the top of the agenda. This year, COVID-19 has made effective public health monitoring a priority. In recent weeks, anger at systemic racial injustice has fuelled public protests. While these are very different issues, they share something in common: they will not be addressed if we do not use data-driven technologies to understand, monitor and improve complex systems - whether that is the healthcare system, the justice system or the energy grid.

Experimental AI regime to be introduced in Moscow


Moscow City Hall has been instructed to determine the conditions, requirements and procedure for the development, creation, introduction and implementation of artificial intelligence technologies, as well as the cases and procedures for using the results of the application of artificial intelligence. It is expected that large IT companies using artificial intelligence in the field of medicine, urban infrastructure, face recognition and other uses will take part in the experiment. The Law separately outlines certain provisions relating to the storage and processing of personal data that will be obtained during the experiment. As a result, the Law makes it possible to use the previously anonymised personal data of individuals participating in the experiment to increase the effectiveness of the state or municipal government. However, the Law specifically establishes that such personal data can only be transferred to participants in the experiment and must be stored in Moscow.

Artist uses AI to create stunning realistic portraits of historical figures


Is this artificial intelligence or a time machine? Bas Uterwijk, an Amsterdam-based artist, is using AI to create extremely lifelike photographs of historical figures and monuments such as the Statue of Liberty, artist Vincent van Gogh, George Washington and Queen Elizabeth I. Using a program called Artbreeder, which is described as "deep learning software," Uterwijk builds his photographs based on a compilation of portraits, reports the Daily Mail. The program pinpoints common facial features and photograph qualities to produce an image. "I try to guide the software to a credible outcome. I think of my work more as artistic interpretations than scientifically or historically accurate," the artist tells the outlet.

Ask the Founders: What is Artificial Intelligence exactly?


In the past few years, Artificial Intelligence has become more and more ubiquitous in our everyday tech, and it shows no signs of going away, with investment into UK AI reaching --2.42bn in 2019. According to the Tech Nation Report 2020, looking at data from 2015-2019, the UK is third in the world for levels of AI investment, second by deal count, and the only country of the top 5 AI nations to have demonstrated consistent positive year-on-year growth for the last 5 years. A regular feature of sci-fi and sometimes horror, AI has often been forewarned as something to be afraid of, and while the ethical implications are something that need to be kept central to the development of AI, it--s not something that necessarily spells the end of days. So what exactly is Artificial Intelligence, what does it mean for the consumer and where do we expect it to go in the next few years? We asked some members of the public for their main concerns surrounding the technology, and asked some founders from our Applied AI 1.0 programme if they could clear up some of myths, misunderstandings and concerns. Mohammad Rashid Khan, Co-founder & CEO of Calipsa and Jamie Potter, Co-founder & CEO at Flexciton gave us their insights.--

Artist uses AI to reveal what historical figures really looked like


A Dutch artist is using modern technology to create realistic photo-style portraits of famous figures only depicted in paint and sculpture. Bas Uterwijk, from Amsterdam, explained that he wanted to see if he could create realistic digital renderings of key faces in history, including Vincent Van Gogh and Napoleon. He also turned his talents to statues like Michelangelo's David and the Statue of Liberty. Bas uses Artbreeder, a'deep-learning' software which can create life-like images from scratch or based on a composite of different portraits. Bas Uterwijk, from Amsterdam, can create likenesses of famous historical figures using'deep-learning' technology.

Improve alignment of research policy and societal values


Historically, scientific and engineering expertise has been key in shaping research and innovation (R&I) policies, with benefits presumed to accrue to society more broadly over time ([ 1 ][1]). But there is persistent and growing concern about whether and how ethical and societal values are integrated into R&I policies and governance, as we confront public disbelief in science and political suspicion toward evidence-based policy-making ([ 2 ][2]). Erosion of such a social contract with science limits the ability of democratic societies to deal with challenges presented by new, disruptive technologies, such as synthetic biology, nanotechnology, genetic engineering, automation and robotics, and artificial intelligence. Many policy efforts have emerged in response to such concerns, one prominent example being Europe's Eighth Framework Programme, Horizon 2020 (H2020), whose focus on “Responsible Research and Innovation” (RRI) provides a case study for the translation of such normative perspectives into concrete policy action and implementation. Our analysis of this H2020 RRI approach suggests a lack of consistent integration of elements such as ethics, open access, open innovation, and public engagement. On the basis of our evaluation, we suggest possible pathways for strengthening efforts to deliver R&I policies that deepen mutually beneficial science and society relationships. Alignment of R&I objectives with societal benefits, which transcend exclusive economic value, is a globally relevant concern ([ 3 ][3]). Aspiration of stronger science and society interrelationships have been visible in U.S. research management efforts, as well as in Canada and Europe. In H2020, to which the European Commission (EC) allocated nearly €80 billion for the 2014–2020 funding period, the EC enumerated RRI as a priority across all of H2020 activities (a “cross-cutting issue”) to deepen science and society relationships and be responsive to societal challenges. To date, €1.88 billion have been invested across 200 different R&I areas (e.g., quantum computing, graphene nanotechnology, human brain research, artificial intelligence) in more than 1100 projects related to various dimensions of RRI (see the figure). Inclusion of RRI in H2020 reflected the commitment of the European Union (EU) to the precautionary principle with regard to R&I policy, and the deepening commitment of the EC to mainstream concerns related to science and society integration ([ 4 ][4], [ 5 ][5]). RRI principles and practices have been designed to enhance inclusive and democratic modes of conducting R&I to reflect current forms and aspirations of society ([ 4 ][4]). Formal adoption and exploitation of RRI in H2020 coalesced around six thematic domains of responsibility (“keys”): public engagement, gender equality, science education and science literacy, open access, ethics, and governance ([ 6 ][6]). As a relatively young concept, these six keys cover only a part of RRI as it is discussed in the academic literature. Their integration in the European R&I ecosystem was advanced by various political- and policy-level ambitions ([ 3 ][3]–[ 5 ][5]). The forthcoming Ninth Framework Programme, Horizon Europe (2021–2027), includes further mention of RRI, as well as additional efforts to increase responsiveness of science to society through elements of the so-called “three O's agenda” (i.e., open innovation, open science, openness to the world) ([ 7 ][7]). Despite this fairly extensive history of EC investment in mainstreaming activities, a recent survey of more than 3100 European researcher recipients of H2020 funding showed that a vast majority of respondents were not familiar with the concept of RRI ([ 8 ][8]). Although these findings by no means suggest that researchers are irresponsible, they raise questions about the success of the EC approach to embedding normative targets for responsibility into R&I. The need for systematic evaluation is clear ([ 9 ][9]). Our study contributes to a legacy of research on the efficacy of framework programmes in light of various EC ambitions ([ 10 ][10]). To answer our question about policy integration and implementation of RRI in H2020, we conducted a mixed method investigation in three stages: (i) desktop research, (ii) interviews, and (iii) case research [see supplementary materials (SM) S10 for details]. First, we collected and reviewed relevant documentation of the four H2020 Programme Sections (Excellent Science, Industrial Leadership, Societal Challenges, Diversity of Approaches) and 19 respective subthemes available on the websites of the EC. This included reviews of documents at the following levels: policy, scoping, work package, calls, projects, proposal templates, and evaluations. Review of documents extended to all three periods of H2020 (2014–2015, 2016–2017, and 2018–2020) and employed the six EC RRI keys as indicators. Second, we conducted interviews with representatives ( n = 257) of seven stakeholder groups within the 19 subthemes of H2020. Third, using natural language processing algorithms, we obtained and analyzed texts describing project objectives of all the H2020 projects (ongoing and finished, n = 13,644) available on the CORDIS Portal, which provides information on EU-funded R&I activities. We examined how proposal language and RRI policies translate into project activities across H2020 using text-mining approaches. We carried out keyword frequency analysis by applying a selection of 10 to 12 keywords (SM S8) associated with each of the six RRI keys. This resulted in an “RRI score” for each of six keys for each H2020 project (SM S13). This subsequent case research covered all three H2020 periods (i.e., 2014–2015, 2016–2017, and 2018–2020). At each of these stages we produced reports for each corresponding subtheme (SM S11). The resulting body of 19 reports was then systematically reviewed for levels of policy integration. The policy-integration levels were qualitatively assessed with the EC's own indicator assessment ([ 6 ][6]). ![Figure][11] How well is Responsible Research and Innovation represented in Horizon 2020? Limited high-quality reference to Responsible Research and Innovation (RRI) suggests that it has largely been referred to without proper understanding, or as an empty signifier. Data combine all four Horizon 2020 (H2020) program sections and reflect the amount and quality of representation of six RRI keys and three “O's,” across three levels: samples of internal H2020 program documents, H2020 stakeholder interviews, and H2020 project objectives. Comparison across keys within a given level is straightforward; all values are drawn from the same underlying materials. Comparison across levels within a given key should focus on relative proportions of the four colors within a given level, not on absolute values; analyses drew upon different types and amounts of underlying materials in each level. See supplementary materials for details. GRAPHIC: X. LIU/ SCIENCE This assessment demonstrates which elements of the RRI framework were initially defined by the policy-makers (desktop level), which RRI attributes the stakeholders were most aware of (interview level), and which RRI elements were manifested in project proposals (case level) (SM S12; see the figure). RRI as a concept has been present in most of the four Programme Sections of H2020, and particular RRI policy elements emerge as prominent in certain subthemes, especially those addressing societal challenges or explicitly promoting the uptake of RRI. But RRI overall has largely been referred to either without proper understanding of its definition, or as empty signifier, suggesting lack of compliance with the EC's interpretation of the RRI concept (see the figure; SM S9). Integration of the three O's agenda, contemplated as a successor to the RRI framework, lagged behind that of the six RRI keys; a finding consistent with introduction of the agenda in the later stages of H2020. Our results suggest that the integration of the RRI framework into H2020 has fallen short of stated EC ambitions. Our data show substantial discrepancies between the inclusion of RRI concepts within official subtheme documents (e.g., on policy and work programme levels), and awareness of RRI by interviewees working on projects funded by such subthemes (see the figure). Absence of RRI keys across the majority of programme subtheme evaluation criteria is a telling example. Such evidence suggests that (i) the RRI framework is still an evolving concept, the development of which hinders its proper understanding by those who are supposed to use it; (ii) such individuals have only superficial understanding of the notion for its effective exploitation; and (iii) although the RRI framework is present on the declarative, strategic policy level (scoping and subtheme general description), it wanes in funding calls (policy operationalization) and is largely absent in evaluation criteria used in proposal assessment. Collectively, these points further suggest that applicants have little in the way of consistently aligned incentives to regard RRI as relevant in proposal design and submission. Although (i) and (ii) are primarily a matter of a lack of adequate information, awareness and training, (iii) points to limitations of European science policy efforts related to the pursuit of RRI. Such translation failures are typically caused by interplay of different logics of negotiation at the different levels ([ 11 ][12]), a linear model of innovation appealing to scientific excellence in R&I ([ 12 ][13]), actors' resistance to change, path dependencies, cognitive boundaries, and competing policy agendas ([ 13 ][14]). As the issues covered by RRI are normatively claimed to be of high relevance by political decision-makers, as evidenced in several EC documents, we conclude that the problem is one of policy integration strategy and implementation ([ 14 ][15]). The lack of clarity in conceptualizing RRI for research policy and governance, the limited understanding among key stakeholders, and the concept's conflation with other—often conflicting—policy goals (e.g., scientific excellence, economic value, technological readiness) hinder the emergence of a specific RRI-oriented policy frame ([ 15 ][16]). Such conflicting policy goals are palpable at the core of European research funding (e.g., supporting either mission-oriented innovation or curiosity-driven basic research in key funding instruments) and highlight the structural tensions between the normative ideals and potential instrumentalization ([ 3 ][3]). There are some limitations of this study that must be taken into account when interpreting results. First, the measurements were cross-sectional and though representative, are not exhaustive. Generalizability of findings could be increased if the study were to extend in a longitudinal fashion and possibly to better elaborate causal relationships among factors. Second, although we employed mixed methods in our investigation, the number of interviews and case studies could be further increased to provide additional qualitative information about the dynamics of RRI at the project level. Third, as the framework programme remains ongoing, our analysis was not able to evaluate the entire H2020 corpus. Although the results indicate evidence of patchy RRI implementation, highlighting the need for more consistent support to help align EC science policy and societal values, the progress made is nontrivial, given the history of science ([ 1 ][1]). A clear discrepancy exists between the expressed strong normative position on RRI and its integration in concrete policies and practices. Fully integrating RRI as a strong normative position into research funding and governance is a necessary but not sufficient first step to creating a working policy system that drives RRI integration. Longer-lived investments are needed for building a shared understanding and awareness of the relevance of responsibility in R&I among key stakeholders. Integrating responsibility into research funding further requires RRI to shift from a “cross-cutting issue” to a “strategic concern” that receives consistent and sustained embedding in call texts and project selection criteria. This will require “policy entrepreneurs” who can stimulate interactions across subthemes to foster alignment of RRI integration and translation. In addition, a range of integration policies are required at the system level and within subthemes, in which the issue of RRI is adopted as a goal. This is pertinent as, in case of such integration failures, it is often the normative position that is called into question instead of the implementation strategy, or actual integration pathway. The EC would benefit from enhancing previous efforts to integrate RRI and so affirm its role as a leader of ethically acceptable and societally responsible R&I on the world stage. Otherwise Europe needlessly undercuts its ability to direct research toward tackling societal challenges in ways compatible with its values. [][17] 1. [↵][18]1. M. Polanyi, 2. J. Ziman, 3. S. Fuller , Minerva 38, 1 (2000). [OpenUrl][19][CrossRef][20][Web of Science][21] 2. [↵][22]1. N. Mejlgaard et al ., Science 361, 761 (2018). [OpenUrl][23][FREE Full Text][24] 3. [↵][25]1. R. von Schomberg, 2. J. Hankins 1. R. von Schomberg , in International Handbook on Responsible Innovation: A Global Resource, R. von Schomberg, J. Hankins, Eds. (Edward Elgar, 2019), pp. 12–32. 4. [↵][26]1. R. Owen, 2. P. Macnaghten, 3. J. Stilgoe , Sci. Public Policy 39, 751 (2012). [OpenUrl][27][CrossRef][28][Web of Science][29] 5. [↵][30]1. D. Simon, 2. S. Kuhlmann, 3. J. Stamm, 4. W. Canzler 1. R. Owen, 2. M. Pansera , in Handbook on Science and Public Policy, D. Simon, S. Kuhlmann, J. Stamm, W. Canzler, Eds. (Edward Elgar, 2019), pp. 26–48. 6. [↵][31]DGRI, “Indicators for promoting and monitoring responsible research and innovation: Report from the expert group on policy indicators for responsible research and innovation” (Report, European Commission, 2015); [\_rri/rri\_indicators\_final\_version.pdf][32]. 7. [↵][33]DGRI, Open innovation, open science, open to the world: A vision for Europe” (Directorate-General for Research and Innovation, European Union, 2016); . 8. [↵][34]1. S. Bührer et al ., “Monitoring the evolution and benefits of responsible research and innovation: Report on the researchers' survey – Study” [Report KI-1-18-886-EN-N, Directorate-General for Research; Innovation (European Commission), 2018]. 9. [↵][35]1. A. Rip , J. Responsib. Innov. 3, 290 (2016). [OpenUrl][36] 10. [↵][37]1. H. Rodríguez, 2. E. Fisher, 3. D. Schuurbiers , Res. Policy 42, 1126 (2013). [OpenUrl][38] 11. [↵][39]1. M. Howlett, 2. J. Vince, 3. P. Del Río , Politics Gov. 5, 69 (2017). [OpenUrl][40] 12. [↵][41]1. K. Rommetveit, 2. R. Strand, 3. R. Fjelland, 4. S. Funtowicz , “What can history teach us about the prospects of a European research area? Joint Research Centre scientific and policy reports” (Report JRC84065, European Commission, 2013). 13. [↵][42]1. H. Colebatch , Public Policy Admin 33, 365 (2017). [OpenUrl][43] 14. [↵][44]1. B. G. Peters et al ., Designing for Policy Effectiveness: Defining and Understanding a Concept (Cambridge Univ. Press, 2018). 15. [↵][45]1. R. Owen, 2. E.-M. Forsberg, 3. C. Shelley-Egan , “RRI-practice policy recommendations and roadmaps: Responsible research and innovation in practice” (Report, RRI-Practice Project, 2019); [\_Policy\_recommendations.pdf][46]. Acknowledgments: This project received funding from the EU's Horizon 2020 research and innovation programme under grant agreement no. 741402. We acknowledge all the consortium members who contributed to the data collection and writing of the reports (SM S11), which this study is based on. We express our gratitude to H. Tobi and N. Mejlgaard, as well as to the reviewers, for their helpful and constructive comments. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: pending:yes [12]: #ref-11 [13]: #ref-12 [14]: #ref-13 [15]: #ref-14 [16]: #ref-15 [17]: [18]: #xref-ref-1-1 "View reference 1 in text" [19]: {openurl}?query=rft.jtitle%253DMinerva%26rft.volume%253D38%26rft.spage%253D1%26rft_id%253Dinfo%253Adoi%252F10.1023%252FA%253A1026591624255%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: /lookup/external-ref?access_num=10.1023/A:1026591624255&link_type=DOI [21]: /lookup/external-ref?access_num=000165793800001&link_type=ISI [22]: #xref-ref-2-1 "View reference 2 in text" [23]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DMejlgaard%26rft.auinit1%253DN.%26rft.volume%253D361%26rft.issue%253D6404%26rft.spage%253D761%26rft.epage%253D762%26rft.atitle%253DEurope%2527s%2Bplans%2Bfor%2Bresponsible%2Bscience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aav0400%26rft_id%253Dinfo%253Apmid%252F30139865%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [24]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiRlVMTCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjE0OiIzNjEvNjQwNC83NjEtYiI7czo0OiJhdG9tIjtzOjIxOiIvc2NpLzM2OS82NDk5LzM5LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [25]: #xref-ref-3-1 "View reference 3 in text" [26]: #xref-ref-4-1 "View reference 4 in text" [27]: {openurl}?query=rft.jtitle%253DSci.%2BPublic%2BPolicy%26rft_id%253Dinfo%253Adoi%252F10.1093%252Fscipol%252Fscs093%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [28]: /lookup/external-ref?access_num=10.1093/scipol/scs093&link_type=DOI [29]: /lookup/external-ref?access_num=000312510500007&link_type=ISI [30]: #xref-ref-5-1 "View reference 5 in text" [31]: #xref-ref-6-1 "View reference 6 in text" [32]: [33]: #xref-ref-7-1 "View reference 7 in text" [34]: #xref-ref-8-1 "View reference 8 in text" [35]: #xref-ref-9-1 "View reference 9 in text" [36]: {openurl}?query=rft.jtitle%253DJ.%2BResponsib.%2BInnov.%26rft.volume%253D3%26rft.spage%253D290%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [37]: #xref-ref-10-1 "View reference 10 in text" [38]: {openurl}?query=rft.jtitle%253DRes.%2BPolicy%26rft.volume%253D42%26rft.spage%253D1126%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [39]: #xref-ref-11-1 "View reference 11 in text" [40]: {openurl}?query=rft.jtitle%253DPolitics%2BGov.%26rft.volume%253D5%26rft.spage%253D69%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [41]: #xref-ref-12-1 "View reference 12 in text" [42]: #xref-ref-13-1 "View reference 13 in text" [43]: {openurl}?query=rft.jtitle%253DPublic%2BPolicy%2BAdmin%26rft.volume%253D33%26rft.spage%253D365%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: #xref-ref-14-1 "View reference 14 in text" [45]: #xref-ref-15-1 "View reference 15 in text" [46]: