NEW URBAN STADIA

This book provides with the most comprehensive collection of new urban stadia built or renovated in the last 30 years. It displays the success of the new urban pattern in designing and building stadia as part of an urban context. A unique collection of plans all presented at the same scale will help understanding the key role played by new urban stadia in shaping and re-generating existing urban neighborhoods. The book allows academics and students to have access to the largest collection available today of new urban stadia

Excellent Researcher or Good Public Servant? The Interplay between Research and Academic Citizenship

Academics have always been endowed with the privilege of autonomy, but the diffusion of evaluation systems based on publication outcomes potentially jeopardizes the benefits deriving from behaviors that address other pillars of higher education. Besides research and teaching, academic citizenship, i.e., the service behaviors carried out within and outside organizational boundaries, are in fact cornerstones of university functioning. We investigate the relationship between academic citizenship and research after the introduction of an evaluation system that moves research performance to center stage on a dataset collecting publication records and service activities of 353 Italian scholars in the accounting discipline in the 2004-2013 period. A cluster analysis reveals different academics' orientations towards research and academic citizenship. We contribute to the debate on academic choices by showing that a large number of university members tend to focus on a single type of academic citizenship or to adopt a research orientation, while a significant part remains stuck in the middle without achieving satisfying performance in any domains according to international standards, and discuss implications for the design of behavioral incentives

Can we be all in one?

The pursuit of a high research performance is nowadays shared by academics internationally since it is considered to sustain national development. Generating outstanding research is an effort that can jeopardize the enactment of other academic activities and the attainment of related satisfying goals, though. While the interplay between research and other knowledge transfer activities such as patenting, spin-off creation and consulting, has been widely debated, the influence of research on academic citizenship, i.e., on the service provided by faculty to their institution and to the wider collective, has remained surprisingly in the backward of the reflection on higher education systems. This study analyzes the effect of research performance on academic citizenship in a sample of 216 Italian academics in the field of management. With the exception of research awards and international scientific collaborations, research does not emerge to significantly impact upon academic citizenship, which may account for the scarce attention devoted to this latter. Since service is necessary for all organizations, universities included, to thrive, citizenship needs to be fostered and awarded through appropriate institutional and managerial policies that are here highlighted

A COMPARISON BETWEEN 3D RECONSTRUCTION USING NERF NEURAL NETWORKS AND MVS ALGORITHMS ON CULTURAL HERITAGE IMAGES

In this research, an innovative comparison between 3D reconstructions obtained by means of Artificial Intelligence, in particular NeRF Neural Networks, and by Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) open-source algorithms is proposed. The 3D reconstruction comparison is performed on two test cases, one of cultural interest, one useful only for technical discussion. It is known that the approaches are traditionally used with different objectives and in different contexts but they can however also be used with similar purpose, i.e., 3D reconstruction. In particular, we were interested in evaluating how NeRF reconstructions are accurate from a metric point of view and how the models obtained from the application of NeRF differ from the model obtained from the classical photogrammetry. By analyzing the results in the considered test cases, we show how NeRF networks, although computationally demanding, can be an interesting alternative or complementary methodology, especially in cases where classical photogrammetric techniques do not allow satisfactory results to be achieved. It is therefore suggested to expand efforts in this direction by exploiting, for example, the numerous improvement proposals of the original NeRF network

A MATCH-MOVING METHOD COMBINING AI AND SFM ALGORITHMS IN HISTORICAL FILM FOOTAGE

Searching for suitable material for photogrammetry is a key part in the documentation of Cultural Heritage. Photogrammetry can be used to produce a metrically certified 3D model. Material contained in historical film footage archives is especially useful for documentation when the heritage has been lost. In this research an innovative match-moving method is proposed that aims to exploit Artificial Intelligence and SfM algorithms to identify the frames extracted from a film footage in which the lost monument appears and that are suitable to be processed with photogrammetry for its 3D reconstruction. First of all the identification and tracking of the heritage in the videos was performed training an object detection Neural Network. Then the frames detected were automatically extracted with the coordinates of the bounding boxes that contain the monument. The camera motions were identified by selecting only the shots taken from multiple points of view of the same scene and analysing the evolution of the bounding boxes position over time. A further check of the material was necessary to select only sequences and to eliminate single frames and images from different historic periods. After this process, only the correct frames were automatically selected and processed with photogrammetry and the quality of the obtained 3D model was assessed. The method experimented in this research represents a powerful tool in the field of Cultural Heritage because it makes the selection of suitable material for photogrammetry automatic. Moreover it offers important insights that could be extended to other sectors

A COMPARISON BETWEEN 3D RECONSTRUCTION USING NERF NEURAL NETWORKS AND MVS ALGORITHMS ON CULTURAL HERITAGE IMAGES

In this research, an innovative comparison between 3D reconstructions obtained by means of Artificial Intelligence, in particular NeRF Neural Networks, and by Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) open-source algorithms is proposed. The 3D reconstruction comparison is performed on two test cases, one of cultural interest, one useful only for technical discussion. It is known that the approaches are traditionally used with different objectives and in different contexts but they can however also be used with similar purpose, i.e., 3D reconstruction. In particular, we were interested in evaluating how NeRF reconstructions are accurate from a metric point of view and how the models obtained from the application of NeRF differ from the model obtained from the classical photogrammetry. By analyzing the results in the considered test cases, we show how NeRF networks, although computationally demanding, can be an interesting alternative or complementary methodology, especially in cases where classical photogrammetric techniques do not allow satisfactory results to be achieved. It is therefore suggested to expand efforts in this direction by exploiting, for example, the numerous improvement proposals of the original NeRF network

ARCHITECTURAL HERITAGE RECOGNITION IN HISTORICAL FILM FOOTAGE USING NEURAL NETWORKS

Researching historical archives for material suitable for photogrammetry is essential for the documentation and 3D reconstruction of Cultural Heritage, especially when this heritage has been lost or transformed over time. This research presents an innovative workflow which combines the photogrammetric procedure with Machine Learning for the processing of historical film footage. A Neural Network is trained to automatically detect frames in which architectural heritage appears. These frames are subsequently processed using photogrammetry and finally the resulting model is assessed for metric quality. This paper proposes best practises in training and validation on a Cultural Heritage asset. The algorithm was tested through a case study of the Tour Saint Jacques in Paris for which an entirely new dataset was created. The findings are encouraging both in terms of saving human effort and of improvement of the photogrammetric survey pipeline. This new tool can help researchers to better manage and organize historical information

The role of brokers and social identities in the development of capabilities in Global Virtual Teams

While organizations are increasingly relying on global virtual teams (GVTs) to carry out knowledge intensive activities, the understanding of how GVTs develop capabilities is still limited. We explore how GVTs adapt routines and build capabilities, and the role played by brokers and social identities in this process. We interviewed 49 professionals working in fifteen GVTs based in Europe, India, and US, and operating in IT and engineering consulting companies. Our multi-level grounded model highlights that, while brokers help in the creation of mutual knowledge, they reduce the accuracy of perceptions about distant co-workers. Mutual knowledge, combined with limited accuracy of perceptions, diminishes the need to adapt team routines over time. The negative effect of brokers on the creation of team capabilities is reduced when individual professional identities trigger the search for more accurate perceptions of distant colleagues and clients with the objective of adapting team routines and performing more stimulating work. On top of this, organizational identity further enables the process of adaptation of team routines. We conclude with a discussion of theoretical implications on the interplay between operational and social processes in GVTs and team capabilities, as well as practical implications for designing and managing GVTs

Learning in university technology transfer offices: transactions-focused and relations-focused approaches to commercialization of academic research

University Technology Transfer Offices (TTOs) need a wide range of abilities to facilitate commercial exploitation of research outputs; however, we know relatively little about how these important abilities are developed and refined over time. We draw on practice-based studies of learning to create a novel conceptualization of learning processes and their outcomes in TTOs and show that this conceptualization of learning provides new empirical insights into how learning in TTOs shapes their commercialization practice. We investigate learning-in-practice in case studies of six UK TTOs and find two approaches to commercialization, namely transactions-focused practice and relations-focused practice. We find that both practices co-exist and co-evolve in some TTOs while other TTOs are predominantly transactions-focused. For the latter the development of a relations-focused approach is difficult, but possible if there is strategic direction and if sources of inertia are removed by TTO directors. Given that evolving practice cannot be fully explained by informal learning processes, we suggest that so far separate streams of practice-based literature on learning and strategizing should be brought together. The implications for further investigations of TTO abilities and some recommendations for policy and practice are discussed