Abstract
With the increasing use of smartphones in people's daily lives, mobile accessibility has become a key factor for them. Tourism is one of the sectors that has benefited the most from this growth but has not yet reached its full potential as accessibility has not yet been fully exploited. The main goal of this study is to assess accessibility in mobile applications for the tourism sector. Thus, 14 mobile applications were analyzed, using a manual and automatic methodology through the proposal of an evaluation model divided by quantitative and qualitative requirements, as well as the use of features such as VoiceOver and TalkBack. The results show a high overall number of errors in most quantitative requirements as well as non-compliance with most qualitative requirements. On iPhone 4, "Viseu - Guia da Cidade" was the application with the highest rating, while on Wiko GOA, it was the "JiTT.Travel Funchal" application. In turn, on iPhone 6 Plus, iPhone XR, Nokia 5.1 and OnePlus 6 devices, the best results were achieved by the "Viseu - Guia da Cidade," "JiTT.Travel Funchal" and "TUR4all" applications. Regarding the accessibility of mobile applications on different versions of the same mobile operating system, it was concluded that there are no differences in their accessibility on both operating systems (iOS and Android). Finally, regarding the accessibility of applications on smartphones with different screen sizes, there are also no differences in their accessibility.

Abstract
Cooperation between different data owners may lead to an improvement in forecast quality-for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.

Abstract
Hexapods are very common in astronomy as a mechanism to provide a stiff mount or a precision alignment tool. Here, we present a lumped model for a general symmetric hexapod that allows us to compute the load distribution under external forces, the hexapod's resolution, and the identification of singularity loci within the workspace. We also developed a script to analyze this parametric model, which is publicly available. We use this model to develop and design a hexapod for mid-infrared ELT imager and spectrograph, one of the extremely large telescope's first light instruments. The designed hexapod solution can survive strict earthquake conditions that can go up to 5g, and position and align the 11 ton instrument with submillimetric and arcsecond precisions. Although the model presented is not as precise or as realistic as a finite element WO analysis, it provides, in a fraction of a second, a very good first approximation. Therefore, unlike Eh methods, the model is able to study many geometries in a short time. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Abstract
Green growth has resulted from resource management, setting the speed for sustainable development. Eco-innovations are essential for the improvement of a firm's performance with societal gains, demanding special attention from policy makers. This paper deals with the effect of policy actions on the enhancement of eco-innovation adoption. The Community Innovation Survey (CIS) 2012-2014 is used to estimate the impact of 'carrots' and 'sticks' on innovations with ecological benefits. In addition, the impact of a firm's structural characteristics in ecological strategies is investigated. Regulations and taxes enhance eco-innovation, but grants are only relevant in the case of eco-innovations with external benefits. The firm dimension and non-technological innovation also increase the eco-innovation propensity. Embedding policy actions with environmental concerns will enhance social responsibility and promote resource preservation, providing waste as an economic value. The purpose of this paper is twofold. First, it aims to appraise the effectiveness of the different policy instruments applied in the adoption of innovation with ecological benefits with both internal and external benefits. Secondly, it aims to identify which firm characteristics determine these managerial strategies. Hopefully, light will be cast on the topic so that public and private decision-makers will be given recommendations for policy package design working towards smart and green growth.

Abstract

Abstract
Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.