Industria 4.0 Senza Slogan

Senza grossa fatica l’Industria 4.0 potrebbe essere letta come un’azione di marketing (riuscitissima) ad opera di un gruppo di vari attori, tutti con sede in Germania. D’altronde l’idea di una fabbrica totalmente digitalizzata non ha potuto che trovare favorevoli i grandi e piccoli provider di software e di sistemi di nuova generazione (cloud, 4G, ecc..) che hanno iniziato a fare da cassa di risonanza al concetto di Industria 4.0. L’argomento purtroppo si presta a varie interpretazioni e a varie strumentalizzazioni, anche in attesa di fondi europei stanziati ad hoc. Ogni punto di vista adottabile per descrivere la filosofia 4.0 porta inevitabilmente una distorsione, data dalla specifica prospettiva scelta. Può un concetto nato nel mondo delle grandi imprese tedesche essere preso senza adattamenti alla realtà italiana? Certamente no, occorre uno sforzo di reinterpretazione e adattamento. Ma allora cosa può significare, in concreto, Industria 4.0 in Toscana, ed in particolare nella pratica e nella vita quotidiana delle PMI

Device for vitrectomy surgical operations

A vitrectomy probe for removing vitreous material from an eyeball, having a longitudinal axis and an outer tube with a closed end and a lateral surface, at least one inlet port located proximate to the closed end and configured to allow the vitreous material to enter into the outer tube; an inner tube arranged in the outer tube to relatively move with respect to the outer tube, the inner tube having an opening with a cutting profile through which the vitreous material can be cut and can enter the inner tube, a drive unit configured for causing the outer tube and the inner tube to move relatively with respect to each other, in such a way that the cutting profile cuts the vitreous material entering the inlet port

Data science for engineering design: State of the art and future directions

Abstract Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS. We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines. The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities

Foresight as a key enabler of innovation in the economy. Introduction to the topical collection

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The Light and Shade of Knowledge Recombination: A Systematic Look at the Bioinformatics Patent Scenario

This research focuses on a special case of General Purpose Technology: Bioinformatics. It explores whether – and to what extent – Bioinformatics inventions build upon inherently diverse knowledge sources. Precisely, the role of scientific and technological diversity (measured with Shannon-Wiener diversity index) as driver of impactful Bioinformatics inventions (measured at different standard deviations of the forward citations distribution) is investigated. To this purpose, we carried out an analysis of both Non-Patent and Patent references cited into Bioinformatics patented inventions in the period 1976-2014. Results from a series of logistic regression models indicate that different degrees of impact require different degrees of knowledge diversity; at the same time, and importantly for practitioners and scholars, recombining diverse scientific and technological knowledge bases not always lead to impactful inventions. In other terms: the interplay of science and technology is not always the best option to get impactful inventions

Functions and failures: how to manage technological promises for societal challenges

Technological promises are becoming part of the way in which scientific and technological communities try to attract the attention of stakeholders, aiming at legitimation, reputation, and funding. Not all promises, however, become reality. With the increase in the use of promises comes the risk of disillusion and cynicism, which may affect negatively policy-makers and the public opinion. The paper suggests that the introduction in the field of S&T and innovation policy of a tool commonly used in engineering fields aimed at identifying and measuring all possible failures of a proposed technology. Instead of focusing on the magnitude of promises, it suggests that a useful perspective can be gained by placing systematic attention to the negative side, i.e. all reasons why a given technology may fail to deliver the promises. The paper develops the methodology, presents a case study, and illustrates the benefits of using it in policy-making

Functional technology foresight. A novel methodology to identify emerging technologies

The speed and complexity of the technology evolution faced by modern societies need new approaches to the analysis and understanding of the world. Indeed, an exclusive focus on technological goals can miss to recognize all the stakeholders of a technology and address real user needs; moreover, on the one hand low signals are becoming more and more important in fast evolving markets, on the other hand the excess of hype, fashions, or vested interests sometimes deeply alter indicators. However, the so called Big Data promise to be a huge low cost set of valuable information, available and affordable to all (SMEs included). But, analyzing them is not trivial especially if we deal with academic papers and patents. To tackle these issues, the present paper proposes to apply a powerful methodological tool called Functional Analysis to the Technology Foresight process. Actually the rigorous study of the functions, that an artefact should perform to satisfy the user needs, provides a universal and thus unifying point of view, which is able to correlate the user perspective on the product with its technical features. Functional reasoning has been applied to (i) detect possible patterns of development, spotting missing elements and highlighting strengths as well as potential sources of failure; (ii) to enhance traditional bibliometric tools such as the analysis of S-curves and (iii), integrated with a natural language processing analysis toolchain, tailored for patent documents, to identify emerging technologies. The paper describes the functional approach to technology foresight activity, presents how to integrate it with text mining algorithms and experts’ domain knowledge, and finally discusses its benefits in the context of Technology Foresight also from an economic point of view, showing that oresight is affordable also for Small and Medium Enterprises

How to do research on the societal impact of research? Studies from a semantic perspective

We review some recent works of our research lab that have applied novel text mining techniques to the issue of research impact assessment. The techniques are Semantic Hypergraphs and Lexicon-based Named Entity Recognition. By using these techniques, we address two distinct and open issues in research impact assessment: the epistemological and logical status of impact assessment, and the construction of quantitative indicators. © 2021 18th International Conference on Scientometrics and Informetrics, ISSI 2021. All rights reserved