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Artificial intelligence and machine learning are revolutionising fields of science and engineering. In recent years, process engineering has widely benefited from this novel modelling and optimisation approach. The open literature can offer several examples of their applications to chemical engineering problems. Increasing investments are devoted to these techniques from different industrial areas, but insufficient information on a structured course covering these topics in a chemical engineering curriculum could be found. The course in this paper intends to reduce this gap. We introduce one of the first courses on artificial intelligence applications in a chemical engineering curriculum. The course targets Master's students with a chemical engineering background and insufficient knowledge of statistical approaches. It covers the main aspects by utilising frontal lectures and hands-on exercises with active learning methods. This paper shows the methodology we adapted to introduce students to machine learning techniques and how they responded to each class. The student performances for each test are shown, as well as the survey results based on student feedback and suggestions. This work contains essential guidelines for educators who will provide an artificial intelligence course in a chemical engineering curriculum. • An AI course for chemical engineers is given as one of the first attempts in Europe. • The course is structured into frontal and hands-on lessons. • Modelling and optimisation techniques via AI were shown to the students. • Applications of developed models were explained to the students. • Students can apply basic AI in chemical engineering tasks after this course. [ABSTRACT FROM AUTHOR]

Advanced technologies are changing our working life in unpredictable ways. Consequently, a fear of technologically induced mass unemployment has re-emerged. The increased precarity associated with the technological substitution of work could lead to a regression towards materialist values that are more accepting of authoritarianism and xenophobia. Crucially, these values are less associated with the skills demanded in future work, which tends to be depicted as demanding higher levels of innovation, creative and social skills that are associated with post-materialist values. Current research has thus far overlooked the cultural aspects of large-scale technological substitution of work, which this study illuminates. We investigate how the relationship between occupational values and occupational automatability has developed between 2002 and 2018 in Europe. The results demonstrate that occupational values have been rather stable throughout the period. Occupational values are not becoming more or less fit for artificial intelligence society as would be expected if the context becomes increasingly precarious or innovation-driven. The paper demonstrates that a cultural adaptation to this type of society has not yet occurred. • This is the first paper to explore if occupational values indicate a cultural adaptation to artificial intelligence society. • Contrary to theoretical expectations, this empirical study does not find a significant adaptation to a new work context. • The paper demonstrates the relationship between values and occupational automatability across 2002–2018. [ABSTRACT FROM AUTHOR]

Background: Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998.Objectives: To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future.Methods: We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers.Results: We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication.Conclusions: There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited. [ABSTRACT FROM AUTHOR]

With increased digitalization and e-government services, Artificial Intelligence (AI) gained momentum. This paper focuses on AI-governance in Child Social Care field, exploring how aspects of individual, family/community factors are embedded in organizational level, especially when dealing with children resilience and wellbeing. A three-level based review has been conducted. In the first part we explored the interlink between individual factors associated to either resilience or wellbeing are connected to community and governance level where a new conceptual model is provided. In the second phase, we conducted an in-depth systematic literature review using PRISMA review protocol where new categorizations of identified literature with respect to individual, family and community levels in child social care field were suggested, while in the third phase, a review of relevant AI-initiatives in Europe and USA was performed. Finally, a comprehensive discussion of the literature review outcomes was carried out and a new updated conceptual model was provided. • Systematic literature using PRISMA protocol has been carried out. • Athree level literature steps is performed: Management, overall and EU case study reports. • There is a clear distinction between individual and family/community factors in governance. • The sparsity of applications of Artificial Intelligence to child social care is emphasized. • New conceptual models have been put forward. [ABSTRACT FROM AUTHOR]

Despite the significant emphasis placed on incorporating 21st century skills into the educational framework, particularly at the primary level, recent scholarly works indicate considerable variation in the implementation of these skills across different countries and regions, suggesting a demand for further research specifically focusing on primary education. The indications of the Digicomp framework and 21st-century skills in Europe have outlined the key competences for lifelong learning needed for all citizens, including teachers and students. In this perspective, Education plays a fundamental role in ensuring that citizens acquire the required skills. The objective in the common European framework is clear: to initiate a transition from the culture of knowledge to the culture of competence. Nowadays, technological advancement allows the researchers to create and combine different frameworks with the perspective of an even more tailored, and engaged education, some examples derived from the implementation of Virtual Reality (VR) and Augmented Reality (AR), in the combination of Gamification and AI, or the development of Intelligent Tutoring Systems (ITS) to foster and create an even more personalized learning and teaching. Following these premises, in this paper, we want to point out new research reflections and perspectives that could help researchers, teachers, educators (and consequently students) to reflect on the introduction of new technologies (e.g., artificial intelligence, robot tutors) and on how these can affect on human behavioral development and on the acquisition of new skills and competences (Specifically: Creativity, Critical Thinking, Problem Solving, and Computational Thinking) for the educational context. The analysis carried on, suggests a perspective on how creativity, critical thinking, and problem-solving can be effective in promoting computational thinking, and how Artificial Intelligence (AI) could be an aid instrument to teachers in the fostering of creativity, critical thinking, and problem-solving in schools and educational contexts. • Point out new research reflections and perspectives on the introduction of new; technologies (e.g., artificial intelligence, robot tutors) in educational context; • Outline activities and lines of research on Creativity, Critical Thinking, Problem Solving, and Computational Thinking; • Propose solutions to be able to implement AI and new technologies for the development of skills in human behavior; • Suggestions for implementing technologies in the school context; • The fundamental role of the network of schools in the European context. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k -Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users. [ABSTRACT FROM AUTHOR]

Abstract: The Advanced Mission Concepts and Technologies Office (Mission Technologies Office, MTO for short) at the European Space Operations Centre (ESOC) of ESA is entrusted with research and development of innovative mission operations concepts systems and provides operations support to special projects. Visions of future missions and requests for improvements from currently flying missions are the two major sources of inspiration to conceptualize innovative or improved mission operations processes. They include monitoring and diagnostics, planning and scheduling, resource management and optimization. The newly identified operations concepts are then proved by means of prototypes, built with embedded, enabling technology and deployed as shadow applications in mission operations for an extended validation phase. The technology so far exploited includes informatics, artificial intelligence and operational research branches. Recent outstanding results include artificial intelligence planning and scheduling applications for Mars Express, advanced integrated space weather monitoring system for the Integral space telescope and a suite of growing client applications for MUST (Mission Utilities Support Tools). The research, development and validation activities at the Mission technologies office are performed together with a network of research institutes across Europe. The objective is narrowing the gap between enabling and innovative technology and space mission operations. The paper first addresses samples of technology infusion cases with their lessons learnt. The second part is focused on the process and the methodology used at the Mission technologies office to fulfill its objectives. [ABSTRACT FROM AUTHOR]

This paper presents our work in the ADVICE (IST-1999-11305, Information Societies Technology Programme, V Framework Programme of the European Commission) project, an on-going European Commission research project of the IST Programme. The overall objective of this project is to design and implement an advice-giving system for E-commerce, supporting a move from the current catalogue-based customer services to a customer adapted intelligent assistance, emulating in some way the performance of a human seller. With this aim, the main elements of the ADVICE approach include an agent-based architecture with an Interface Agent to manage the multimedia presentation, the Interaction Agent to support an advanced user–system interaction and the Intelligent Agent incorporating a knowledge-based model of the e-business for the pilot domain, that supports the reasoning for advise-giving according with the user needs and the dialogue evolution. [Copyright &y& Elsevier]

On-Line monitoring evaluates instrument channel performance by assessing its consistency with other plant indications. Industry and EPRI experience at several plants has shown this overall approach to be very effective in identifying instrument channels that are exhibiting degrading or inconsistent performance characteristics “On-Line Monitoring of Instrument Channel Performance by EPRI (2000)”.On-Line monitoring of instrument channels provides information about the condition of the monitored channels through accurate, more frequent monitoring of each channel''s performance over time. This type of performance monitoring is a methodology that offers an alternate approach to traditional time-directed calibration. On-line monitoring of these channels can provide an assessment of instrument performance and provide a basis for determining when adjustments are necessary. Elimination or reduction of unnecessary field calibrations can reduce associated labor costs, reduce personnel radiation exposure and reduce the potential for miss-calibration.PEANO “A Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Nuclear Power Plant Data by Fantoni (2000)” is a system for on-line calibration monitoring developed in the years 1995–2000 at the Institutt for energiteknikk (IFE), Norway, which makes use of Artificial Intelligence techniques for its purpose. The system has been tested successfully in Europe in off-line tests with EDF (France), Tecnatom (Spain) and ENEA (Italy). PEANO is currently installed and used for on-line monitoring at the HBWR reactor in Halden. This paper describes the results of performance tests on PEANO with real data from a US PWR plant, in the framework of a co-operation among IFE, EPRI and Edan Engineering, to evaluate the potentials of PEANO for future installations in US nuclear plants. [Copyright &y& Elsevier]