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Il concetto di "modello di misura": qualche considerazione su un argomento molto importante
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Valore probatorio delle emoij: cosa accade nelle Corti USA?
No full textChat e istant messagging fanno sempre più parte del nostro modo di comunicare. Esse contengono immagini che non hanno un immediato significato letterale univoco, come le emoji. In questa rassegna di giurisprudenza comparata si verifica come le corti statunitensi hanno interpretato, ai fini di risolvere un contenzioso, il significato delle emoji utilizzate dalle parti
The elephant in the record: on the multiplicity of data recording work
No full textThis article focuses on the production side of clinical data work, or data recording work, and in particular, on its multiplicity in terms of data variability. We report the findings from two case studies aimed at assessing the multiplicity that can be observed when the same medical phenomenon is recorded by multiple competent experts, yet the recorded data enable the knowledgeable management of illness trajectories. Often framed in terms of the latent unreliability of medical data, and then treated as a problem to solve, we argue that practitioners in the health informatics field must gain a greater awareness of the natural variability of data inscribing work, assess it, and design solutions that allow actors on both sides of clinical data work, that is, the production and care, as well as the primary and secondary uses of data to aptly inform each other’s practices
Forecasting cycle time in semiconductor manufacturing systems: a literature review
No full textAn efficient and effective forecasting of production cycle times (CT) is a critical success factor in semiconductor manufacturing systems (SMS): inaccurate CT forecasts can have a negative impact on production scheduling, causing late deliveries, as well as on the amount of inventories and work-in-progress, which rapidly lose value over time because of the high risk of obsolescence. Therefore, since the 80s, several quantitative techniques have been developed to face this problem. Furthermore, Artificial Intelligence (AI) techniques are gaining importance, despite their potential is still not fully exploited even in the most advanced manufacturing systems. However, a synthetic overview of the techniques to forecast CT in SMS is still missing in the literature. As a result, it is difficult for decision makers to orient themselves and choose, among the many existing ones, the best model for their specific situation, comparing the different performance in terms of accuracy, data required, speed and easiness to use. This paper aims at presenting an overview of the quantitative techniques developed to forecast production CT in SMS. Firstly, a description of the methodology with which the literature review has been carried out is provided. Secondly, a taxonomy of forecasting techniques is proposed. Subsequently, a synthetic description of analytical, simulation, time-series and causal methods is presented. Within statistical techniques, a special focus is deserved to AI ones, since their popularity has dramatically increased in the last years. In particular, the most recent applications of artificial neural networks (ANN) in SMS – namely, hybrid methods and Long-Short-Term-Memory recursive neural networks – are described. Finally, a table with a qualitative comparison between the different methods is proposed.11-13 September 201