Abstract
The transport sector of the European Union is the only sector of the economy that has been increasing its emissions since 2014. To reduce the use of fossil fuels and achieve the greenhouse gas emissions mitigation target, many countries are focusing on the deployment of electric vehicles. This paper aims at analysing recent literature on the deployment of electric vehicles (EV) and typifying objectives, methods and indicators generally exploited, to better understand the state of the art on this topic. The Web of Science database was used and the results showed that the interest in the topic of electric vehicles has been increasing exponentially since 2010. The main significant indicators and the assessment methodologies were analysed. The indicators identified were aggregated in four main clusters: environmental, economic, social and technical indicators. Although the factors that contribute to EV deployment can vary depending on the regions specific characteristics, most of the research studies pointed out that the main contributors are the high density of recharging points, the existence of government monetary incentives and the lower operational cost of EV.

Abstract
The Natural Language Processing task of determining Who did what to whom is called Semantic Role Labeling. For English, recent methods based on Transformer models have allowed for major improvements in this task over the previous state of the art. However, for low resource languages, like Portuguese, currently available semantic role labeling models are hindered by scarce training data. In this paper, we explore a model architecture with only a pre-trained Transformer-based model, a linear layer, softmax and Viterbi decoding. We substantially improve the state-of-the-art performance in Portuguese by over 15 F1. Additionally, we improve semantic role labeling results in Portuguese corpora by exploiting cross-lingual transfer learning using multilingual pre-trained models, and transfer learning from dependency parsing in Portuguese, evaluating the various proposed approaches empirically.

Abstract
Although advances have been made in reducing the time needed for manhole inspection, the procedure is still mostly done manually, with workers having to enter and visually assess the areas being inspected. There is also a growing need to have these structures inspected regularly, in order to prevent casualties and services interruption, as well as the higher cost of rebuilding instead of repairing these structures, which is possible only if pathologies are identified at early stages. This situation renders the task a good target for automation. This paper reviews a set of existing manhole, tunnel and duct inspection systems to ascertain the main features required for the task, as well as the technologies currently used. Most of the present-day solutions are rather expensive and cumbersome, requiring the deployment of relatively heavy equipment and specialized personnel to operate them. With the recent development of laser range sensors and depth (RGBD) cameras with small form factors and weights, the development of solutions with higher portability and lower cost become feasible. Such a solution could improve considerably the rate at which manholes are inspected, and the technology could be used to generate textured models to be analyzed and reported by a remotely located specialist, both online and offline. The work presented here lays the ground for the development of such a system in our research group who has been working on low-cost systems for the generation of 3D textured models for automated inspection. © 2021, Springer Nature Switzerland AG.

Abstract
Eco-design is focused on incorporating environmental criteria early in the design process to reduce the environmental impacts of new products. However, while services now represent the largest share of the world & rsquo;s economy, the incorporation of environmental sustainability in the design of new services is very limited. This research proposes the ECO-Service Design (ECO-SD) method that integrates eco-design and service design to conceptualize new environmentally sustainable services. The ECO-SD method bridges environmental criteria from eco-design with the human-centred approach of service design, to foster the environmental sustainability of new services, while offering a desirable user experience. To this end, this method encompasses four stages: service exploration, to understand the service context and how users interact with it; service visualization, to visually identify the barriers to environmental performance and user experience during service provision; service ideation, to conceptualize a new service that overcomes the identified barriers; and service assessment, to understand the changes in environmental sustainability and user experience of the newly designed service. The application of the ECO-SD method to two in-dividual shared transport services shows how it enables integrated identification of opportunities to overcome environmental and user experience barriers in the existing services.

Abstract
The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies.

Abstract
The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.