Abstract
The ability to locate a robot is one of the main features to be truly autonomous. Different methodologies can be used to determine robots location as accurately as possible, however these methodologies present several problems in some circumstances. One of these problems is the existence of uncertainty in the sensing of the robot. To solve this problem, it is necessary to combine the uncertain information correctly. In this way, it is possible to have a system that allows a more robust localization of the robot, more tolerant to failures and disturbances. This paper evaluates an Extended Kalman Filter (EKF) that fuses odometry information with Ultra-WideBand Time-of-Flight (UWB ToF) measurements and camera measurements from the detection of ArUco markers in the environment. The proposed system is validated in a real environment with a differential robot developed for this purpose, and the achieved results are promising.

Abstract
Research on incubation models indicates that incubators and accelerators are crucial catalysts for the development of start-ups. To facilitate start-ups in financial markets, several regulatory authorities have adopted a new incubation model called a ?regulatory sandbox?. Regulatory sandboxes enable eligible applicants to test their technology-enabled financial solutions for a certain period of time (subject to conditions the regulator imposes). As such, these instruments allow innovation while preventing severe instability in financial markets caused by systemic risk. Despite their importance, management research has devoted little attention to studying how sandboxes operate as a new incubation model. In our abductive study, we adopt the activity system framework and a qualitative analysis approach to investigate the activities of five leading sandboxes and compare them with the activities of other incubation models. The data analysis yielded an activity model with three design elements (achieving membership, participating and detaching) and one design theme (improving connectedness). Thus, sandboxes are characterized by providing regulatory guidance and facilitating access to testing across international jurisdictions, distinguishing them from both generic and specialized incubation models. Our primary contribution to the incubation literature is extending the knowledge of a unique incubation model through a set of theoretical propositions.

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Abstract
The increasing growing need for optimization of transportation in a sustainable and green environment is fundamental for the future of mobility. Companies, governments, and non-governmental institutions are trying to find new ways to contribute to this challenge. The main goal of this Special Issue was to integrate the most recent advances in transportation that connect the hybrid point of view. In particular, key contributions integrate several interesting topics related to earning algorithms for the integration of passengers and goods, gamification techniques, semantic data, blockchain, and edge computing. Other topics in urban mobility, railway, and highways were considered, and hot topics on shared autonomous vehicles (SAVs) brought huge value. It is revealed that connected and autonomous vehicle technology and services for the mobility of people and goods in mixed-mode environments can improve efficiency, safety, and cleanliness.

Abstract
Purpose: This study aims to explore the role open data assumes in social innovation initiatives that combat the effects of Covid-19, in which quantitative information about these projects is complemented with the analysis of open data innovation capabilities. Methodology: A mixed methods approach is adopted. Initially, quantitative data regarding the number of innovative projects is extracted from the OPSI framework. Then, in a second phase, qualitative data from each project is explored to explore the open data innovation capabilities offered by each initiative. Results: The results reveal that most social innovation initiatives focus on the area of data visualization dashboards and open datasets. However, some projects address other areas, e.g. hackathons, service availability, and tourism information. Implications: Covid-19 profoundly affects the lives of people and businesses, and open data can assume a determinant role in mitigating these effects and improve information transparency and collaboration between public and private entities. Originality/Value: This study is relevant for the establishment of supportive public policies that promote the emergence of open-data-driven initiatives to mitigate the effects of Covid-19 in several areas such as the economy, education, and leisure.