Abstract
In this work, we consider a classic international trade model with two countries and one firm in each country. The game has two stages: in the first stage, the governments of each country use their welfare functions to choose their tariffs either: (a) competitively (Nash equilibrium) or (b) cooperatively (social optimum); in the second stage, firms competitively choose (Nash) their home and export quantities under Cournot-type competition conditions. In a previous publication we compared the competitive tariffs with the cooperative tariffs and we showed that the game is one of the two following types: (i) prisoner's dilemma (when the competitive welfare outcome is dominated by the cooperative welfare outcome); or (ii) a lose-win dilemma (an asymmetric situation where only one of the countries is damaged in the cooperative welfare outcome, whereas the other is benefited). In both scenarios, their aggregate cooperative welfare is larger than the aggregate competitive welfare. The lack of coincidence of competitive and cooperative tariffs is one of the main difficulties in international trade calling for the establishment of trade agreements. In this work, we propose a welfare-balanced trade agreement where: (i) the countries implement their cooperative tariffs and so increase their aggregate welfare from the competitive to the cooperative outcome; (ii) they redistribute the aggregate cooperative welfare according to their relative competitive welfare shares. We analyse the impact of such trade agreement in the relative shares of relevant economic quantities such as the firm's profits, consumer surplus, and custom revenue. This analysis allows the countries to add other conditions to the agreement to mitigate the effects of high changes in these relative shares. Finally, we introduce the trade agreement index measuring the gains in the aggregate welfare of the two countries. In general, we observe that when the gains are higher, the relative shares also exhibit higher changes. Hence, higher gains demand additional caution in the construction of the trade agreement to safeguard the interests of the countries.

Abstract
In a robotics scope, an excellent way to test and improve knowledge is through competitions. In other words, it is possible to follow the results in practice, compare them with the development of other teams and improve the current solutions. The Robot At Factory Lite proposal simulates an Industry 4.0 warehouse scenario, applying education through Science, Technology, Engineering, and Mathematics (STEM) methodology, where the participants have to work on a solution to overcome its challenges. Thus, this article presents an initial electromechanical proposal, which is the basis for developing robots for this competition. The presented main concepts aim to inform the possibilities of using the robot's parts and components. Thus, an idea can be sketched in the participants' minds, inspiring them to use their imagination and knowledge through the presentation of this model.

Abstract
Context. In the Milky Way the central massive black hole, Sgr A*, coexists with a compact nuclear star cluster that contains a sub-parsec concentration of fast-moving young stars called S-stars. Their location and age are not easily explained by current star formation models, and in several scenarios the presence of an intermediate-mass black hole (IMBH) has been invoked.Aims. We use GRAVITY astrometric and SINFONI, KECK, and GNIRS spectroscopic data of S2, the best known S-star, to investigate whether a second massive object could be present deep in the Galactic Centre (GC) in the form of an IMBH binary companion to Sgr A*.Methods. To solve the three-body problem, we used a post-Newtonian framework and consider two types of settings: (i) a hierarchical set-up where the star S2 orbits the Sgr A*-IMBH binary and (ii) a non-hierarchical set-up where the IMBH trajectory lies outside the S2 orbit. In both cases we explore the full 20-dimensional parameter space by employing a Bayesian dynamic nested sampling method.Results. For the hierarchical case we find the strongest constraints: IMBH masses > 2000 M-circle dot on orbits with smaller semi-major axes than S2 are largely excluded. For the non-hierarchical case, the chaotic nature of the problem becomes significant: the parameter space contains several pockets of valid IMBH solutions. However, a closer analysis of their impact on the resident stars reveals that IMBHs on semi-major axes larger than S2 tend to disrupt the S-star cluster in less than a million years. This makes the existence of an IMBH among the S-stars highly unlikely.Conclusions. The current S2 data do not formally require the presence of an IMBH. If an IMBH hides in the GC, it has to be either a low-mass IMBH inside the S2 orbit that moves on a short and significantly inclined trajectory or an IMBH with a semi-major axis > 1 ''. We provide the parameter maps of valid IMBH solutions in the GC and discuss the general structure of our results and how future observations can help to put even stronger constraints on the properties of IMBHs in the GC.

Abstract
We present MONET, a new multimodal dataset captured using a thermal camera mounted on a drone that flew over rural areas, and recorded human and vehicle activities. We captured MONET to study the problem of object localisation and behaviour understanding of targets undergoing large-scale variations and being recorded from different and moving viewpoints. Target activities occur in two different land sites, each with unique scene structures and cluttered backgrounds. MONET consists of approximately 53K images featuring 162K manually annotated bounding boxes. Each image is timestamp-aligned with drone metadata that includes information about attitudes, speed, altitude, and GPS coordinates. MONET is different from previous thermal drone datasets because it features multimodal data, including rural scenes captured with thermal cameras containing both person and vehicle targets, along with trajectory information and metadata. We assessed the difficulty of the dataset in terms of transfer learning between the two sites and evaluated nine object detection algorithms to identify the open challenges associated with this type of data. Project page: https://github.com/fabiopoiesi/monet-dataset.

Abstract
Assessing the societal contributions of research is not simple, especially for research projects that produce outputs with low technology readiness level. This paper analyses the potential societal impacts of research resulting in technologies with low maturity, but with the potential to be further developed in the long-term. It uses the case of the ESGRIDS (Enhancing Smart Grids for Sustainability) collaborative research project and its outputs aimed at enhancing smart grids for sustainability. Data was collected from the four participant research teams through two sequential questionnaires about technologies' state of development and expected long-term societal effects. Among the main results, we underscore the influence of individual perceptions and organisational contexts over the process of eliciting future developments. The analysis of technologies' status, barriers for market uptake, and potential future developments was translated into a technology roadmap, which outlined the time-dimension for technology maturity evolution and implementation impacts. The technologies developed within the ESGRIDS project can contribute to support consumers' energy decision-making and to encourage them to have a more active role in the electricity market. Those technologies can also create job opportunities associated with the development of new products and services, and contribute to mitigating climate change by promoting the use of renewable energies thus reducing carbon dioxide emissions, in addition to contributing to energy cost reduction by optimizing the use of supply and demand resources. Future research avenues point towards a methodology that can be used for assessing the potential impacts of research projects with low technology readiness outputs.

Abstract
Imaging the spatial distribution of chemical elements at a sample surface is a common application of laserinduced breakdown spectroscopy with vast scientific and technological applications. Yet, typical imaging solutions only explore the creation of two-dimensional maps, which can limit the interpretability of the results and further diagnostics in three-dimensional settings. Within this context, this work explores the combination of spectral imaging techniques and photogrammetry to deploy a versatile solution for the creation of threedimensional spectral imaging models. First, by making use of a numerical algorithm that is able to match features in the spectral image with those of the three-dimensional model, we show how to match the mesh from distinct sensor modalities. Then, we describe a possible visualization workflow, making use of dedicated photogrammetry and visualization software to easily deploy interactive models. Overall, the results demonstrate the versatility of our approach and pave for the development of novel spectral imaging diagnostic strategies that are able to deliver better qualitative analysis and insight in the three-dimensional space.