Abstract
Using VLTI/GRAVITY and SINFONI data, we investigate the subparsec gas and dust structure around the nearby type 1 active galactic nucleus (AGN) hosted by NGC 3783. The K-band coverage of GRAVITY uniquely allows simultaneous analysis of the size and kinematics of the broad line region (BLR), the size and structure of the near-infrared(near-IR)-continuum-emitting hot dust, and the size of the coronal line region (CLR). We find the BLR, probed through broad Br gamma emission, to be well described by a rotating, thick disc with a radial distribution of clouds peaking in the inner region. In our BLR model, the physical mean radius of 16 light-days is nearly twice the ten-day time-lag that would be measured, which closely matches the ten-day time-lag that has been measured by reverberation mapping. We measure a hot dust full-width at half-maximum (FWHM) size of 0.74 mas (0.14 pc) and further reconstruct an image of the hot dust, which reveals a faint (5% of the total flux) offset cloud that we interpret as an accreting or outflowing cloud heated by the central AGN. Finally, we directly measure the FWHM size of the nuclear CLR as traced by the [Ca VIII] and narrow Br gamma line. We find a FWHM size of 2.2 mas (0.4 pc), fully in line with the expectation of the CLR located between the BLR and narrow line region. Combining all of these measurements together with larger scale near-IR integral field unit and mid-IR interferometry data, we are able to comprehensively map the structure and dynamics of gas and dust from 0.01 to 100 pc.

Abstract
VirtuosoNextTM is a distributed real-time operating system (RTOS) featuring a generic programming model dubbed Interacting Entities. This paper focuses on these interactions, implemented as so-called Hubs. Hubs act as synchronisation and communication mechanisms between the application tasks and implement the services provided by the kernel. While the kernel provides the most basic services, each carefully designed, tested and optimised, tasks are limited to this handful of basic hubs, leaving the development of more complex mechanisms up to application specific implementations. This work presents a toolset that supports the building of new services compositionally, using notions borrowed from the Reo coordination language, on which the developer can delegate coordination-related duties. This toolset uses a formal compositional semantics for hubs that captures dataflow and time, formalising the behaviour of existing hubs, and allowing the definition of new ones. Furthermore, it enables the analysis and verification of hubs under our automata interpretation, including time-sensitive behaviour via the UPPAAL model checker, usable on http://arcatools .org /hubs. We illustrate the proposed tools and methods by verifying key properties on different interaction scenarios between tasks and a composed hub.

Abstract
This paper is inspired by a manual picking retail company where shape and weight constraints affect the order-picking process. We proposed an alternative clustering similarity index that considers the similarity, the weight and the shape of products. This similarity index was further incorporated in a storage allocation heuristic procedure to set the location of the products. We test the procedure in a retail company that supplies over 191 stores, in Northern Portugal. When comparing the strategy currently used in the company with this procedure, we found out that our approach enabled a reduction of up to 40% on the picking distance; a percentage of improvement that is 32% higher than the one achieved by applying the Jaccard index, a similarity index commonly used in the literature. This allows warehouses to save time and work faster.

Abstract
In this study, we used machine learning techniques to reconstruct the wavelength dependence of the absorption coefficient of human normal and pathological colorectal mucosa tissues. Using only diffuse reflectance spectra from the ex vivo mucosa tissues as input to algorithms, several approaches were tried before obtaining good matching between the generated absorption coefficients and the ones previously calculated for the mucosa tissues from invasive experimental spectral measurements. Considering the optimized match for the results generated with the multilayer perceptron regression method, we were able to identify differentiated accumulation of lipofuscin in the absorption coefficient spectra of both mucosa tissues as we have done before with the corresponding results calculated directly from invasive measurements. Considering the random forest regressor algorithm, the estimated absorption coefficient spectra almost matched the ones previously calculated. By subtracting the absorption of lipofuscin from these spectra, we obtained similar hemoglobin ratios at 410/550 nm: 18.9-fold/9.3-fold for the healthy mucosa and 46.6-fold/24.2-fold for the pathological mucosa, while from direct calculations, those ratios were 19.7-fold/10.1-fold for the healthy mucosa and 33.1-fold/17.3-fold for the pathological mucosa. The higher values obtained in this study indicate a higher blood content in the pathological samples used to measure the diffuse reflectance spectra. In light of such accuracy and sensibility to the presence of hidden absorbers, with a different accumulation between healthy and pathological tissues, good perspectives become available to develop minimally invasive spectroscopy methods for in vivo early detection and monitoring of colorectal cancer.

Abstract
The 2020/2021 academic year started full of uncertainties for new students of higher education in Portugal. The restrictions imposed by the COVID-19 pandemic, the fears of a new lockdown, all coupled with the well-known challenges that a university student faces in his first year, made this year a particularly challenging year in terms of the students' integration. In this paper, we present how the mentoring programme of the Integrated Master in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto was adapted to help the integration of first-year students in the university environment under the pandemic.

Abstract
In this paper, an advanced control of a DC Micro Grid (MG)-connected Proton Exchange Membrane (PEM) Fuel Cell connected with a DC/DC boost converter is addressed to achieve an overall appropriate control scheme in the power management system. In this context, a nonlinear PEM Fuel Cell stack, which is the main source of the continuous power to the load, is modelled and controlled by an optimal Linear Parameter Varying (LPV) technique in the presence of uncertainties and variation in its operating parameters i.e. output current and temperature. To this end, a polytopic-LPV model is considered for nonlinear PEM Fuel Cell stack and sufficient conditions for designing a stabilizing continuous time LPV controller based on state feedback controlling law is derived in terms of Linear Matrix Inequality (LMI). On the other hand, a feedback linearization controller is developed simultaneously to control the duty cycle of the DC/DC boost converter, which is connected between the PEM Fuel Cell stack and the load, aiming to regulate the DC output voltage of the grid to an arbitrary and predefined reference value. The performance of the proposed approach and controllers are verified through simulation results.