Blending “hard” and “soft” TQM for academic excellence: the University of Siena experience in the field of Life Sciences

Purpose – Focusing on the adoption of Total Quality Management (TQM) principles in universities, this research paper explores how the “soft” dimensions of TQM trigger its “hard” dimensions considering them at the individual (micro-) and the university (meso-), and eventually at cluster (system-), levels. Design/methodology/approach – Adopting a qualitative approach, this study presents an in-depth, longitudinal case study of University of Siena, one of the oldest Italian universities, that has been at the core of the research-based cluster on vaccines, today converged in the Tuscan Life Science Cluster. In particular, data were collected between 2018 and February 2022 and consists of archival data (press articles, websites, books), nine interviews to key informants, multiyear experience of the Life Sciences sector by two of the authors and other material put at disposal by university offices, and emails. Data analysis relied on a timeline, a coding procedure that considered three levels of analysis (individual, organization and cluster). Finally, the authors looked at the “how” and “why” the emerged themes have contributed to academic excellence. Findings – This paper unveils how “soft” and “hard” sides of TQM are blended across multiple levels for reaching academic excellence. The grounded model emerged enlightens the importance of an individual “soft” dimension, academic passion (composed by its three subdimensions of individual research, teaching and entrepreneurial passion) and also sheds light on the organizational “soft” and “hard” sides that the university has been able to design for encouraging research, teaching and third mission quality. Academic excellence has been possible thanks to the capitalization of the individual and organizational“soft” sides into real outcomes as represented by the organizational and individual “hard” sides. Practical implications – The paper suggests the importance of TQM principles applied at universities’ level, providing an in-depth description of “soft” and “hard” sides dimensions of TQM and their impact on all the three pillars of academic excellence. The study findings suggest implications for managers and professionals in the higher education domain as well as for policymakers emphasizing the importance of supporting the individual and organizational soft sides of TQM. The authors provide practical implications recommending universities to consider not only the organizational dimensions but also individual ones when pursuing higher education excellence. In particular, individual passion plays a crucial role and universities need to identify ways of nurturing it. The authors also recommend policymakers to think about new ways to sustain universities as crucial actors in boosting a cluster development, as well as to consider higher education institutions, especially in more rural areas, as a privileged player not only capable of nurturing academic excellence but also able of creating an internationally renowned cluster. Originality/value – TQM principles have been intensively analysed from an industrial perspective focusing on manufacturing and services, while this paper focuses on TQM in universities, presenting a grounded model that blends the individual and organizational “soft” and “hard” sides

The role of acquisitions in the development of high-tech start-up: an introductory analysis of the importance of marketing

Complementing previous research that has focused on high tech acquisitions, this paper focuses on how an acquirer, that adopts a preservation approach, helps the acquired in growing. Due to the exploratory nature of this research, we performed a single case study. The paper points out that the competencies acquired by the start-up after the acquisition play a significant role in boosting the growth of the start-up. Amongst others, newcomer marketing competencies are crucial for the acquired start-up’s growth in terms of revenues, reputation, and long-term view. The research might be helpful for both academic and practitioners, describing how investments in marketing following acquisitions may help start-ups in boosting their growth

The CUORE cryostat and its bolometric detector

none126noneSantone, D.; Alduino, C.; Alfonso, K.; Artusa, D.R.; Iii, F.T. Avignone; Azzolini, O.; Banks, T.I.; Bari, G.; Beeman, J.W.; Bellini, F.; Bersani, A.; Biassoni, M.; Branca, A.; Brofferio, C.; Bucci, C.; Camacho, A.; Caminata, A.; Canonica, L.; Cao, X.G.; Capelli, S.; Cappelli, L.; Carbone, L.; Cardani, L.; Carniti, P.; Casali, N.; Cassina, L.; Chiesa, D.; Chott, N.; Clemenza, M.; Copello, S.; Cosmelli, C.; Cremonesi, O.; Creswick, R.J.; Cushman, J.S.; D'Addabbo, A.; Dafinei, I.; Davis, C.J.; Dell'Oro, S.; Deninno, M.M.; Di Domizio, S.; Vacri, M.L. Di; Drobizhev, A.; Fang, D.Q.; Faverzani, M.; Fernandes, G.; Ferri, E.; Ferroni, F.; Fiorini, E.; Franceschi, M.A.; Freedman, S.J.; Fujikawa, B.K.; Giachero, A.; Gironi, L.; Giuliani, A.; Gladstone, L.; Gorla, P.; Gotti, C.; Gutierrez, T.D.; Haller, E.E.; Han, K.; Hansen, E.; Heeger, K.M.; Hennings-Yeomans, R.; Hickerson, K.P.; Huang, H.Z.; Kadel, R.; Keppel, G.; Kolomensky, Yu.G.; Leder, A.; Ligi, C.; Lim, K.E.; Liu, X.; Ma, Y.G.; Maino, M.; Marini, L.; Martinez, M.; Maruyama, R.H.; Mei, Y.; Moggi, N.; Morganti, S.; Mosteiro, P.J.; Napolitano, T.; Nones, C.; Norman, E.B.; Novati, V.; Nucciotti, A.; O'Donnell, T.; Orio, F.; Ouellet, J.L.; Pagliarone, C.E.; Pallavicini, M.; Palmieri, V.; Pattavina, L.; Pavan, M.; Pessina, G.; Pettinacci, V.; Piperno, G.; Pira, C.; Pirro, S.; Pozzi, S.; Previtali, E.; Rosenfeld, C.; Rusconi, C.; Sangiorgio, S.; Scielzo, N.D.; Singh, V.; Sisti, M.; Smith, A.R.; Taffarello, L.; Tenconi, M.; Terranova, F.; Tomei, C.; Trentalange, S.; Vignati, M.; Wagaarachchi, S.L.; Wang, B.S.; Wang, H.W.; Wilson, J.; Winslow, L.A.; Wise, T.; Woodcraft, A.; Zanotti, L.; Zhang, G.Q.; Zhu, B.X.; Zimmermann, S.; Zucchelli, S.Santone, D.; Alduino, C.; Alfonso, K.; Artusa, D. R.; Iii, F. T. Avignone; Azzolini, O.; Banks, T. I.; Bari, G.; Beeman, J. W.; Bellini, F.; Bersani, A.; Biassoni, M.; Branca, A.; Brofferio, C.; Bucci, C.; Camacho, A.; Caminata, A.; Canonica, L.; Cao, X. G.; Capelli, S.; Cappelli, L.; Carbone, L.; Cardani, L.; Carniti, P.; Casali, N.; Cassina, L.; Chiesa, D.; Chott, N.; Clemenza, M.; Copello, Simone; Cosmelli, C.; Cremonesi, O.; Creswick, R. J.; Cushman, J. S.; D'Addabbo, A.; Dafinei, I.; Davis, C. J.; Dell'Oro, S.; Deninno, M. M.; DI DOMIZIO, Sergio; Vacri, M. L. Di; Drobizhev, A.; Fang, D. Q.; Faverzani, M.; Fernandes, Guido; Ferri, E.; Ferroni, F.; Fiorini, E.; Franceschi, M. A.; Freedman, S. J.; Fujikawa, B. K.; Giachero, A.; Gironi, L.; Giuliani, A.; Gladstone, L.; Gorla, P.; Gotti, C.; Gutierrez, T. D.; Haller, E. E.; Han, K.; Hansen, E.; Heeger, K. M.; Hennings Yeomans, R.; Hickerson, K. P.; Huang, H. Z.; Kadel, R.; Keppel, G.; Kolomensky, Y. u. G.; Leder, A.; Ligi, C.; Lim, K. E.; Liu, X.; Ma, Y. G.; Maino, M.; Marini, Laura; Martinez, M.; Maruyama, R. H.; Mei, Y.; Moggi, N.; Morganti, S.; Mosteiro, P. J.; Napolitano, T.; Nones, C.; Norman, E. B.; Novati, V.; Nucciotti, A.; O'Donnell, T.; Orio, F.; Ouellet, J. L.; Pagliarone, C. E.; Pallavicini, Marco; Palmieri, V.; Pattavina, L.; Pavan, M.; Pessina, G.; Pettinacci, V.; Piperno, G.; Pira, C.; Pirro, S.; Pozzi, S.; Previtali, E.; Rosenfeld, C.; Rusconi, C.; Sangiorgio, S.; Scielzo, N. D.; Singh, V.; Sisti, M.; Smith, A. R.; Taffarello, L.; Tenconi, M.; Terranova, F.; Tomei, C.; Trentalange, S.; Vignati, M.; Wagaarachchi, S. L.; Wang, B. S.; Wang, H. W.; Wilson, J.; Winslow, L. A.; Wise, T.; Woodcraft, A.; Zanotti, L.; Zhang, G. Q.; Zhu, B. X.; Zimmermann, S.; Zucchelli, S

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation