Abstract
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Abstract
Material heritage typically has a whole set of associated immaterial heritage, which is essential to pass on to the visitor as a cultural mission of the destinations and those who manage them. In this sense, the interpretation of material heritage is a complex process that is not a fully efficient process with the mere observation of physical artifacts. In this context, it emerges as fundamental to provide visitors with a set of tools that allow them to correctly interpret the artifacts that come to fully understand the cultural dimension of the destinations and their heritage. Accordingly, the role of virtual reality can leverage the creation of innovative and immersive solutions that allow the visitor to understand and feel part of their own heritage and its ancestral component that defines the sociocultural roots of destinations and their civilizational traditions. This article, after dissecting and substantiating the role of virtual reality in the interpretation of heritage, presents a conceptual model, based on the use of virtual reality, which was, in part, prototyped in the scenario of the Portuguese Museum in the city of Miranda do Douro. This proposal is an ongoing contribution to the creation of innovative and immersive tools for the interpretation of heritage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
To transform an organization, it isn't possible to ignore the people aspect and the significant impact of talent development in any transformation process. While in larger organizations, there is often access to more resources that ultimately could bring the needed competencies to successfully run such a complex program, in small or medium enterprises, the resources are more limited and the access to talent more difficult, forcing a potential more aggressive strategy in developing the digital skills needed to enable their transformation. This work aims to review some of the literature and better comprehend the role of learning analytics as a practice that could potentially be used to enhance the learning process within Small and Medium Enterprises and support the improvement of digital literacy within these companies.

Abstract
Multi-temporal InSAR (MT- InSAR) observations, which enable deformation monitoring at an unprecedented scale, are usually affected by decorrelation and other noise inducing factors. Such observations (PS - Persistent scatterers), are usually in the order of several thousand, making their respective evaluation frequently computationally expensive. In the present study, we propose an approach for the detection of MT-InSAR outlying observations through the implementation of Convolutional Neural Networks (CNN) classification models. For each PS, the corresponding MT-InSAR parameters and the respective parameters of the neighboring scatterers and its relative position are considered. Tests in two independent datasets, covering the regions of Bratislava city and the suburbs of Prievidza, Slovakia, were performed. The results showed that such models offer a robust and reduced computation time method for the evaluation of MT-InSAR outlying observations. However, the applicability of these models is limited by the deformation pattern in which such models were trained.

Abstract
Extreme learning machines (ELMs) are a versatile machine learning technique that can be seamlessly implemented with optical systems. In short, they can be described as a network of hidden neurons with random fixed weights and biases, that generate a complex behaviour in response to an input. Yet, despite the success of the physical implementations of ELMs, there is still a lack of fundamental understanding about their optical implementations. This work makes use of an optical complex media to implement an ELM and introduce an ab-initio theoretical framework to support the experimental implementation. We validate the proposed framework, in particular, by exploring the correlation between the rank of the outputs, H, and its generalization capability, thus shedding new light into the inner workings of optical ELMs and opening paths towards future technological implementations of similar principles.

Abstract
In this paper, we investigate a new variant of the multi-commodity network flow problem, taking into consideration multiple transport lines and time windows. This variant arises in a city logistics environment, more specifically in a long-haul passenger transport system that is also used to transport urban freight. We propose two mixed integer programming models for two objective functions: minimization of network operational costs and minimization of travel times. Since the problems under study are NP-hard, we propose three size reduction heuristics. In order to assess the performance of the proposed algorithms, we carried out computational experiments on a set of synthetic problem instances. We use the relative percentage deviation as performance criterion. For the cost objective function, a LP-and-Fix algorithm outperforms other methods in most tested instances, but for the travel time, a hybrid method (size reduction with LP-and-Fix algorithm) is, in general, better than other approaches.