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.

Abstract
Diabetes has significant effects on the human body, one of which is the increase in the blood pressure and when not diagnosed early, can cause severe vision complications and even lead to blindness. Early screening is the key to overcoming such issues which can have a significant impact on rural areas and overcrowded regions. Mobile systems can help bring the technology to those in need. Transfer learning based Deep Learning algorithms combined with mobile retinal imaging systems can significantly reduce the screening time and lower the burden on healthcare workers. In this paper, several efficiency factors of Diabetic Retinopathy detection systems based on Convolutional Neural Networks are tested and evaluated for mobile applications. Two main techniques are used to measure the efficiency of DL based DR detection systems. The first method evaluates the effect of dataset change, where the base architecture of the DL model remains the same. The second method measures the effect of base architecture variation, where the dataset remains unchanged. The results suggest that the inclusivity of the datasets, and the dataset size significantly impact the DR detection accuracy and sensitivity. Amongst the five chosen lightweight architectures, EfficientNet-based DR detection algorithms outperformed the other transfer learning models along with APTOS Blindness Detection dataset.

Abstract
Facility Location Problems (FLP) are complex combinatorial optimization problems whose general goal is to locate a set of facilities that serve a particular set of customers with minimum cost. Being NP-Hard problems, using exact methods to solve large instances of these problems can be seriously compromised by the high computational times required to obtain the optimal solution. To overcome this difficulty, a significant number of heuristic algorithms of various types have been proposed with the aim of finding good quality solutions in reasonable computational times. We propose a Scatter Search approach to solve effectively the Uncapacitated Facility Location Problem (UFLP). The algorithm was tested on the standard testbed for the UFLP obtained state-of-the-art results. Comparisons with current best-performing algorithms for the UFLP show that our algorithm exhibits excellent performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.