Many companies look for novel ways to trace their operational sustainability. The application of AI to analyze and make sense of the big data the company holds represents one promising approach for this aim. The authors study how one can set and design an AI-based solution for improving the sustainability of complex business processes and decision-making in companies of different types. First, they provide a general analysis of current frameworks for measuring adherence to sustainability goals for companies, then they present a conceptual framework and architecture design for an AI-enabled sustainability service for companies. The implications of our research suggest that AI can provide distinct functions: (a) automation: taking big data from different departments and analyzing them with the aim of tracing the sustainability of the company; (b) support: to help decision-making and create relevant insights for stakeholders that are coherent with defined sustainability decision criteria. To the authors' knowledge, no previous research has provided analysis and design of such AI solution for companies.

Teaching innovation and entrepreneurship is a challenging task. It requires students to gain an understanding of key terminology and theoretical knowledge as well as practical experience to build transversal skills. This is what is needed to develop a problem-solving and enterprising attitude for the jobs of tomorrow. At doctoral level, educating STEM graduates to become entrepreneurs and digital leaders in the digital transformation era calls for new learning formats and contents. In this paper we present a new design for the EIT Digital Doctoral School Innovation and Entrepreneurship (I&E) education and we specifically focus on the online educational components developed to cover key I&E content and topics. We present the first results of an evaluation conducted with the students participating in all the courses of the EIT Digital Doctoral School in 2019 and the first impressions and learnings from the new design. We found that the initial feedback was satisfying and that our students appreciated the approach and said that they recommend that instructors use this approach in future teaching. This study contributes to the ongoing debate on how to design and implement I&E education leveraging European universities networks.

In this paper we provide the first results obtained from an ongoing project on collaborations between Universities on blended learning courses in Innovation and Entrepreneurship. Using a design based research approach, all Universities participating in the pilot co-designed settings on how they will collaborate on a course, relying entirely on online tools and online materials. In total there were seven such cross-University collaboration settings, all of them using different key features, ranging from webinars (virtual classes), collaboration on a specific project, peer-review assignments, exchange of personnel, to an exchange of pedagogical methods. In this paper we report on the outcomes from a workshop in which the impressions from teachers and the students were presented for each collaboration. We analyze what worked well, and what did not, as well as recount he perceived value of the approaches taken by the collaborating Universities.

This paper explores whether older adults can remotely participate in museum visits with the help of virtual environments. We design and build a system that supports shared museum co-visits between onsite visitors inside the museum and older adults from a care home. We make the experience more engaging by providing a meaningful story, connecting the objects in the museum. The aim of the study is to understand whether older adults are able to use such technology and to study the mediated sense of spatial presence, the experienced social closeness, and the level of participants' engagement and enjoyment in the visit. We discuss the relationship between these aspects and factors leading to a better remote experience for older adults. The results show that older adults enjoy and engage in remote visits, and that there is a positive correlation between enjoyment, engagement and social closeness.

In this paper we present an approach to stimulate the development of soft skills in tertiary education students, which are seen as critical skills for active citizenship, personal fulfillment and employability in a knowledge-driven society. We developed an online learning setting where technical graduate students practice and gain knowledge on Innovation and Entrepreneurship themes. We present the early results of a peer assessment exercise where students submit assignments and evaluate other assignments made by their colleagues. We employ textual similarity measures and other effort related metrics to understand different learner types emerging in the exercise. Our results foster future directions of research on how soft skills can be studied in online environments. This study contributes to the ongoing debate on how to understand and visualize data coming from online courses and how to further the students' assessment of soft skills development.

UNLABELLED: High-throughput technologies have led to an explosion of genomic data available for automated analysis. The consequent possibility to simultaneously sample multiple layers of variation along the gene expression flow requires computational methods integrating raw information from different '-omics'. It has been recently demonstrated that translational control is a widespread phenomenon, with profound and still underestimated regulation capabilities. Although detecting changes in the levels of total messenger RNAs (mRNAs; the transcriptome), of polysomally loaded mRNAs (the translatome) and of proteins (the proteome) is experimentally feasible in a high-throughput way, the integration of these levels is still far from being robustly approached. Here we introduce tRanslatome, a new R/Bioconductor package, which is a complete platform for the simultaneous pairwise analysis of transcriptome, translatome and proteome data. The package includes most of the available statistical methods developed for the analysis of high-throughput data, allowing the parallel comparison of differentially expressed genes and the corresponding differentially enriched biological themes. Notably, it also enables the prediction of translational regulatory elements on mRNA sequences. The utility of this tool is demonstrated with two case studies. AVAILABILITY AND IMPLEMENTATION: tRanslatome is available in Bioconductor.