Abstract
The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3.

Abstract
It is presented a study of the dependence between the free spectral range (FSR) and the cavity length in Fabry-Perot interferometers. Furthermore, the effect of frequency modulation on the FSR is studied when an optical spectrum analyser (OSA) is used as an interrogator. For low frequency range it is possible to observe this behaviour in the OSA and using an appropriate processing signal it is possible to use the white light interferometry technique.

Abstract
The Hub Location Problems (HLP) have gathered great interest due to the complexity and to the many applications in industry such as aviation, public transportation, telecommunications, among others. The HLP have many variants regarding allocation (single or multiple) and capacity (uncapacitated or capacitated). This paper presents a variant of the HLP, encompassing single allocation with capacity constraints. The Capacitated Single Allocation p-Hub Location Problem (CSApHLP) objective consists on determine the set of p hubs in a network that minimizes the total cost of allocating all the non-hub nodes to the p hubs. In this work, it is proposed a sophisticated RAMP approach (PD-RAMP) to improve the results obtained previously by the simple version (Dual-RAMP). Thus, a parallel implementation is conducted to assess the effectiveness of a parallel RAMP model applied to the CSApHLP. The first algorithm, the sequential PD-RAMP, incorporates Dual-RAMP with a Scatter Search procedure to create a Primal-Dual RAMP approach. The second algorithm, the parallel PD-RAMP, also take advantage of the dual and primal, parallelizing the primal side of the problem and interconnecting both sides as it is expected in the RAMP sequential algorithm. The quality of the results carried out on a standard testbed shows that the PD-RAMP approach managed to improve the state-of-the-art algorithms for the CSApHLP.

Abstract
A strategy for the detection of H2O2 as a milk adulterant using a single shot membrane sensor, is presented. Direct quantitative evaluation of H2O2 in raw, skimmed, semi-skimmed and whole milk was carried out based on a chemiluminescence reaction with luminol. For H2O2 water solutions a linear response was attained from 0.0001% to 0.007 %w/w, with a limit of detection of 3x10(-5) %w/w. A coefficient of determination, R-2, greater than 0.97 was achieved, with a relative standard deviation (RSD) not exceeding 10%. In the analyzed milk samples, the lowest H2O2 concentration detected was 0.001% w/w for raw and for skim milk and 0.002%w/w for, semi-skimmed and whole milk. The presented method is original, sensitive, rapid, and cost-effective. Due to the achieved sensitivity the method has great potential to be used for H2O2 detection in diverse areas, such as environmental monitoring and food quality.

Abstract
Lung diseases affect the lives of billions of people worldwide, and 4 million people, each year, die prematurely due to this condition. These pathologies are characterized by specific imagiological findings in CT scans. The traditional Computer-Aided Diagnosis (CAD) approaches have been showing promising results to help clinicians; however, CADs normally consider a small part of the medical image for analysis, excluding possible relevant information for clinical evaluation. Multiple Instance Learning (MIL) approach takes into consideration different small pieces that are relevant for the final classification and creates a comprehensive analysis of pathophysiological changes. This study uses MIL-based approaches to identify the presence of lung pathophysiological findings in CT scans for the characterization of lung disease development. This work was focus on the detection of the following: Fibrosis, Emphysema, Satellite Nodules in Primary Lesion Lobe, Nodules in Contralateral Lung and Ground Glass, being Fibrosis and Emphysema the ones with more outstanding results, reaching an Area Under the Curve (AUC) of 0.89 and 0.72, respectively. Additionally, the MIL-based approach was used for EGFR mutation status prediction - the most relevant oncogene on lung cancer, with an AUC of 0.69. The results showed that this comprehensive approach can be a useful tool for lung pathophysiological characterization.

Abstract
Objective: This study aimed to identify health behaviours that determine adolescent's adherence to the Mediterranean diet (MD) through a decision tree statistical approach. Design: Cross-sectional study, with data collected through a self-fulfilment questionnaire with five sections: (1) eating habits; (2) adherence to the MD (KIDMED index); (3) physical activity; (4) health habits and (5) socio-demographic characteristics. Anthropometric and blood pressure data were collected by a trained research team. The Automatic Chi-square Interaction Detection (CHAID) method was used to identify health behaviours that contribute to a better adherence to the MD. Setting: Eight public secondary schools, in Algarve, Portugal. Participants: Adolescents with ages between 15 and 19 years (n 325). Results: According to the KIDMED index, we found a low adherence to MD in 9 center dot 0 % of the participants, an intermediate adherence in 45 center dot 5 % and a high adherence in 45 center dot 5 %. Participants that regularly have breakfast, eat vegetable soup, have a second piece of fruit/d, eat fresh or cooked vegetables 1 or more times a day, eat oleaginous fruits at least 2 to 3 times a week, and practice sports and leisure physical activities outside school show higher adherence to the MD (P < 0 center dot 001). Conclusions: The daily intake of two pieces of fruit and vegetables proved to be a determinant health behaviour for high adherence to MD. Strategies to promote the intake of these foods among adolescents must be developed and implemented.