Abstract
This paper compares a large set of programming languages regarding their efficiency, including from an energetic point-of-view. Indeed, we seek to establish and analyze different rankings for programming languages based on their energy efficiency. The goal of being able to rank programming languages based on their energy efficiency is both recent, and certainly deserves further studies. We have taken rigorous and strict solutions to 10 well defined programming problems, expressed in (up to) 27 programming languages, from the well known Computer Language Benchmark Game repository. This repository aims to compare programming languages based on a strict set of implementation rules and configurations for each benchmarking problem. We have also built a framework to automatically, and systematically, run, measure and compare the energy, time, and memory efficiency of such solutions. Ultimately, it is based on such comparisons that we propose a series of efficiency rankings, based on single and multiple criteria. Our results show interesting findings, such as how slower/faster languages can consume less/more energy, and how memory usage influences energy consumption. We also present a simple way to use our results to provide software engineers and practitioners support in deciding which language to use when energy efficiency is a concern. In addition, we further validate our results and rankings against implementations from a chrestomathy program repository, Rosetta Code., by reproducing our methodology and benchmarking system. This allows us to understand how the results and conclusions from our rigorously and well defined benchmarked programs compare to those based on more representative and real-world implementations. Indeed our results show that the rankings do not change apart from one programming language.

Abstract
The pose control (position and orientation) of a robot is important to control how and when the robot gets to the desired pose at the desired time in order to perform some task. Controlling omnidirectional robots is of great interest due to their complete maneuverability. So, we use Proportional-Integrative (PI), Proportional-Derivative (PD), and Feed-Forward (FF) controllers to control the pose of an omnidirectional robot in space and in time. The proposed controller approximates the future trajectory (a subset of points) on parametric polynomials for computing the derivatives needed in the FF. In the simulations performed, it was analyzed the size of the future trajectory horizon for the controller depending on the robot's velocity, and the proposed controller was compared to PD-only and a generic GoToXY controller. The results demonstrated that the proposed controller achieves better results than the other two both in space and in time.

Abstract
The Adaptive Object Model (AOM) is an architectural style in which domain entity types are represented as instances that can be changed at runtime. It can be used to achieve higher flexibility in domain classes. Due to AOM entities having a distinct structure, they are not compatible with most popular frameworks, especially those that use reflection and code annotations. To solve such limitations, this study aims to propose a model that enables the reuse of frameworks designed for classic object-oriented domain models in an AOM application. The proposed model uses dynamically-generated adapters for AOM entities that encapsulate them in a class with the format expected by the frameworks. A reference implementation was developed in the Esfinge AOM RoleMapper framework to evaluate the viability of the proposed model. Initially, to evaluate the solution feasibility, a case study was carried out using the Hibernate framework. Further, an experiment was conducted to assess how the participants perceived the framework functionality reuse through the proposed model. The feasibility study revealed that the solution could be applied in a complex setting for the chosen object-relational mapping frame. It raised some difficulties that can be addressed in future studies. In the experiment, the development time did not present a significant difference compared to the competing approach. Despite the considerable learning curve, most participants considered that the proposed approach has more advantages than the alternative. Based on the evaluations, we can conclude that the proposed model can be successfully employed to use AOM entities with frameworks that were not designed for AOM applications. The possibility of reusing existing frameworks can reduce the effort required to adopt an AOM architecture and, consequently, be a facilitator in implementing more flexible and adaptive approaches.

Abstract
The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

Abstract
Topic modeling or inference has been one of the well-known problems in the area of text mining. It deals with the automatic categorisation of words or documents into similarity groups also known as topics. In most of the social media platforms such as Twitter, Instagram, and Facebook, hashtags are used to define the content of posts. Therefore, modelling of hashtags helps in categorising posts as well as analysing user preferences. In this work, we tried to address this problem involving hashtags that stream in real-time. Our approach encompasses graph of hashtags, dynamic sampling and modularity based community detection over the data from a popular social media engagement application. Further, we analysed the topic clusters' structure and quality using empirical experiments. The results unveil latent semantic relations between hashtags and also show frequent hashtags in a cluster. Moreover, in this approach, the words in different languages are treated synonymously. Besides, we also observed top trending topics and correlated clusters.

Abstract
The COVID-19 pandemic has had a significant impact around the world on health, economies, businesses, equality and the movement of people in the form of tourism. In this context, this paper looks at the strategy chosen by Turismo de Portugal to adapt to the crisis in a country where tourism plays an important role in supporting the local economy, having grown significantly since 2010. The chosen strategy encouraged tourists not to visit Portugal during the pandemic, a turnaround from their previous digital marketing strategy, which invited tourists to discover the country. We undertook a survey that had 170 answers, predominantly from Ecuador, Mexico, the United Kingdom and Portugal but also from several other countries in Europe and Latin America. We aimed to understand whether their strategy was successful in encouraging people to consider Portugal as a holiday destination post-COVID-19. The Can't Skip Hope campaign was created in a work-from-home environment, with the voiceover recorded on a smartphone. Previously recorded footage was re-edited. Our survey found that respondents said the video matched their views of Portugal and that 79.5% would consider Portugal as a holiday destination when they next booked a holiday. In terms of inferential statistics, we performed chi-square tests of significance on the survey data. Thus, this paper contributes to the body of work because it offers insight into marketing strategy adaptation by a local tourist board during a period of crisis.