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Background In February 2021 the “Cortina2021” Alpine World Ski Championships took place in the mountain town of Cortina d'Ampezzo (Italy), thus gaining the definition of “first sporting event of global significance in the time of covid-19 pandemic”. Though the event took place behind closed doors and in a context of national public health restrictive measures, it counted almost 6,000 participants. Methods A rigorous “Covid-19 Prevention and Hygiene Protocol” was drawn up by the local and regional health authorities together with the organizing committee, in order to contain the risk of viral circulation within the venue. Results Overall about 20,000 rapid antigen nasal swabs were performed to screen-detect sars-cov2 cases: only 22 of these were found positive, whereas 25 people were quarantined as close contacts. Conclusions The experience of “Cortina2021” proves that mass gatherings can take place even in times of epidemic threats, if adequately planned and by providing public health recommendations tailored on the viral circulation foreseen by the time the event is due. If new epidemics should arise in the future, hopefully the public health management of “Cortina2021” may be a template for next mass gatherings to be held, as far as possible, despite the viral circulation.

Different techniques exist to analyze multi-way data but PARAFAC is one of the most popular. The usual way of parameter estimation in PARAFAC is an alternating least squares (ALS) procedure which yields least-squares solutions and provides consistent outcomes. Together with these desirable features, the ALS procedure suffers several major flaws which might be particularly problematic for large-scale problems: slow convergence and sensitiveness to degeneracy conditions such as over-factoring, collinearity, bad initialization and local minima. Furthermore, it is well-known that algorithms which rely on least squares easily break down in the presence of outliers. The issue of nonrobustness of the ALS procedure was addressed by [1] and software for computing the robust PARAFAC model is available in the R package rrcov3way (see [4]). The other issues were addressed in a number of works proposing algorithms more efficient than ALS. However, often these do not provide stable results because the increased speed might come at the expense of accuracy. An integrated algorithm was proposed by [2] and [3] which seems to combine improved speed and stability. The purpose of this work is to elaborate this algorithm by adding capabilities for dealing with outliers which might be present in the data. The idea of a robust version of ALS is to identify enough ”good“ observations and to perform the classical ALS on these observations. This is repeated until no significant change is observed. Finally, a reweighting step is carried out to improve the efficiency of the estimators. In order to identify the ”good” observations a robust version of principal component analysis on the unfolded array is used. It is obvious that the robust procedure will be much more time consuming than the classical one, repeating many times the ALS optimization. Therefore, any improvement of the performance of the parameter estimation procedure will contribute to the improvement of the performance of the complete robust procedure. We combine the robust PARAFAC procedure proposed by [1] with the highly efficient estimation algorithm INT2 proposed by [2] in order to obtain a fast estimation and robust to outliers PARAFAC modeling technique which we call R-INT2. As before it starts by robust principal components to identify any outlying points and then iterates using the INT2 algorithm until no significant change is observed. After convergence a reweighting step with INT2 is conducted which produces the final solution. The performance of the newly proposed algorithm R-INT2 for robust estimation of trilinear PARAFAC models is demonstrated in a brief simulation study comparing classical ALS, the robust version based on ALS ([1]) and the new R-INT2. First of, all we want to verify that R-INT2 works well on data sets with and without contamination by identifying the outliers at least as good as the robust ALS algorithm retrieving solutions with good statistical quality. At the same time we want to verify that the convergence is improved significantly and thus the computational time is reduced. Future work should bring a thorough investigation of the properties of the algorithm, comparison to the many existing fast alternatives and studying the possibilities for combination with other computational algorithms.

The usual way of parameter estimation in CANDECOM/PARAFAC (CP) is an alternating least squares (ALS) procedure that yields least-squares solutions and provides consistent outcomes but at the same time has several deficiencies, like sensitivity to the presence of outliers in the data, slow convergence, and susceptibility to degeneracy conditions. A number of works have addressed these weaknesses, but to our knowledge, there is no outlier-robust procedure that is highly computationally efficient at the same time, especially for large data sets. We propose a robust procedure based on an integrated estimation algorithm, alternative to ALS, which guards against outliers and is computationally efficient at the same time.

Digital Maturity is a complex phenomenon, involving vertical as well as horizontal processes throughout firms. Therefore, the use of multidimensional tools represents the most appropriate approach in measuring companies’ digital transformation status (Chanias and Hess, 2016). In recent years, Digital Maturity Models (DMMs)were developed in order to get a current measure of an organization’s as-is digital capability (Deloitte, 2018). However, DMMs’ effectiveness relies purely on raw data without taking into account items’ different difficulties. Moreover, though the vast majority of digital maturity models are mainly focused on manufacturing based organizations, an understanding of the challenges and opportunities identified in each stage of the maturity model can help any organization to identify how digital can lead to success and income generation.The main focus of this work is try to overcome the lack of DMMs applications to the Italian GLAM sector, providing an alternative and more accurate indicator of digital ability , through a widely-used statistical approach. More specifically, we perform a Rasch Analysis using the last national survey on cultural organizations promoted by the Italian Institute of Statistics (ISTAT) in 2016, containing several questions about digital preservation and the use of digital tools in Italian museums.