Abstract
Tourists nowadays tend to avoid tourist traps and are looking for engaging ways to explore cities in the limited time they have. Standard options to explore cities seldom offer a combination between efficiency and fun. Furthermore, a search for an exploration city app returns an unlimited supply of lookalike websites and apps, all claiming to be the best. This paper reports the development of QRioCity, an efficient and exciting way to explore cities, by the “Dragonics” student team. QRioCity offers users the option to sign up for a playful tour through the city of Porto using a public kiosk with an interactive touchscreen. There is no limit to the number of teams playing simultaneously nor there is need to provide personal data. The teams are led through the city using clues and are proposed assignments, like scanning QR codes, to earn points. At the end of the game, every team receives discount coupons for local shops or stores depending on their score, even when they play alone. This way QRioCity helps tourists enjoying the local city life while offering municipalities a chance to strengthen their local economy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
To tackle wildfires and improve forest biomass management, cost effective and reliable mowing and pruning robots are required. However, the development of visual perception systems for forestry robotics needs to be researched and explored to achieve safe solutions. This paper presents two main contributions: an annotated dataset and a benchmark between edge-computing hardware and deep learning models. The dataset is composed by nearly 5,400 annotated images. This dataset enabled to train nine object detectors: four SSD MobileNets, one EfficientDet, three YOLO-based detectors and YOLOR. These detectors were deployed and tested on three edge-computing hardware (TPU, CPU and GPU), and evaluated in terms of detection precision and inference time. The results showed that YOLOR was the best trunk detector achieving nearly 90% F1 score and an inference average time of 13.7ms on GPU. This work will favour the development of advanced vision perception systems for robotics in forestry operations.

Abstract
This article presents the results regarding a systematic literature review procedure on digital twins applied to precision agriculture. In particular, research and development activities aimed at the use of digital twins, in the context of predictive control, with the purpose of improving soil quality. This study was carried out through an exhaustive search of scientific literature on five different databases. A total of 158 articles were extracted as a result of this search. After a first screening process, only 11 articles were considered to be aligned with the current topic. Subsequently, these articles were categorised to extract all relevant information, using the preferred reporting items for systematic reviews and meta-analyses methods. Based on the obtained results, there are two main conclusions to draw: First, when compared with industrial processes, there is only a very slight rising trend regarding the use of digital twins in agriculture. Second, within the time frame in which this work was carried out, it was not possible to find any published paper on the use of digital twins for soil quality improvement within a model predictive control context.

Abstract

Abstract
In recent years, the Twin-Transition reference model has gained notoriety as one of the key options for decarbonizing the economy while adopting more sustainable models leveraged by the Industry 4.0 paradigm. In this regard, one of the most relevant challenges is the integration of data-driven approaches with sustainability assessment approaches, since overcoming this challenge will foster more agile sustainable development. Without disregarding the effort of academics and practitioners in the development of sustainability assessment approaches, the authors consider the need for holistic frameworks that also encourage continuous improvement in sustainable development. The main objective of this research is to propose a holistic framework that supports companies to assess sustainability performance effectively and more easily, supported by digital capabilities and data-driven concepts, while integrating improvement procedures and methodologies. To achieve this objective, the research is based on the analysis of published approaches, with special emphasis on the data-driven concepts supporting sustainability assessment and Lean Thinking methods. From these results, we identified and extracted the metrics, scopes, boundaries, and kinds of output for decision-making. A new holistic framework is described, and we have included a guide with the steps necessary for its adoption in a given company, thus helping to enhance sustainability while using data availability and data-analytics tools.

Abstract
Mobile applications have experienced exponential growth in recent years, including mHealth apps related to stroke, one of the most prevalent diseases worldwide. This review aims to analyze the characteristics of available stroke apps designed to assist in assessing stroke severity. Initially, 809 apps were retrieved from both the App Store and Google Play Store. These apps were then filtered, primarily excluding those that did not implement a prehospital stroke scale with a resulting score. A total of 36 apps met the criteria for further analysis in this review. The majority of these apps displayed scale items using text only. Certain scales, such as RACE, VAN, and NIHSS, are supported by studies demonstrating their ability to accurately assess stroke severity. Consequently, apps featuring these scales are more likely to be useful in achieving the objective of this study. Improvements to these apps could be made by expanding the functionalities they offer and enhancing their user experience.