SiPM and front-end electronics development for Cherenkov light detection

The Italian Institute of Nuclear Physics (INFN) is involved in the development of a demonstrator for a SiPM-based camera for the Cherenkov Telescope Array (CTA) experiment, with a pixel size of 6$\times$6 mm$^2$. The camera houses about two thousands electronics channels and is both light and compact. In this framework, a R&D program for the development of SiPMs suitable for Cherenkov light detection (so called NUV SiPMs) is ongoing. Different photosensors have been produced at Fondazione Bruno Kessler (FBK), with different micro-cell dimensions and fill factors, in different geometrical arrangements. At the same time, INFN is developing front-end electronics based on the waveform sampling technique optimized for the new NUV SiPM. Measurements on 1$\times$1 mm$^2$, 3$\times$3 mm$^2$, and 6$\times$6 mm$^2$ NUV SiPMs coupled to the front-end electronics are presentedComment: In Proceedings of the 34th International Cosmic Ray Conference (ICRC2015), The Hague, The Netherlands. All CTA contributions at arXiv:1508.0589

What influences the speed of prototyping? An empirical investigation of twenty software startups

It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds, firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startups progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototypingComment: This is the author's version of the work. Copyright owner's version can be accessed at doi.org/10.1007/978-3-319-57633-6_2, XP2017, Cologne, German

Amidst Uncertainty–or Not? : Decision-Making in Early-Stage Software Startups

Peer reviewe

Genetic variation at CHRNA5-CHRNA3-CHRNB4 interacts with smoking status to influence body mass index

Cigarette smoking is associated with lower body mass index (BMI), and a commonly cited reason for unwillingness to quit smoking is a concern about weight gain. Common variation in the CHRNA5-CHRNA3-CHRNB4 gene region (chromosome 15q25) is robustly associated with smoking quantity in smokers, but its association with BMI is unknown. We hypothesized that genotype would accurately reflect smoking exposure and that, if smoking were causally related to weight, it would be associated with BMI in smokers, but not in never smokers

Determinants for the success of software startups: Insights from a regional cluster

In recent years, we have seen a growing interest in technology-based companies and intensive knowledge. Several regional clusters have appeared supported in dynamic entrepreneurial ecosystems which, alongside intrinsic aspects of the business, are important determinants of the success of new companies. However, most startups created in these innovation-oriented spaces do not survive the first years of life, due to the high competitiveness of the technological market, due to deficiencies in the business model, due to the support conditions provided by the surrounding ecosystem, and finally due to a weak adjustment between all these dimensions. Among several models available, the Early-Life Decision Model (ELDM) presents itself as an interesting framework for studying the development and success conditions of software companies. This article discusses the application of the ELDM based on a series of interviews conducted to 15 Portuguese software startups installed in a technological cluster located in the northeast of Portugal. Based on the results obtained, it was appropriate to add a new dimension to the ELDM model (learning) and complementing it with the perspectives of the business type and internal versus external determinants.This work was supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT (Fundação para a Ciência e a Tecnologia) within the Project Scope: UID/CEC/00319/2013