Abstract
Purpose Portugal has been experiencing a continuous growth in tourism activity, with hospitality industry as one of the main tourism sectors. Therefore, the assessment of hotel companies' performance is very important to assist decision processes. The purpose of this paper is to assess the financial performance (FP) of 570 hotel companies operating hotel units in Portugal in 2017. To explore the question of brand affiliation, a comparison was made between hotel companies with similar stars rating and market orientation. In addition, this paper intends to fill a gap in literature studying the Portuguese reality on the subject of brand affiliation. Design/methodology/approach The present study uses a methodology based on data envelopment analysis (DEA) to assess the overall performance for each company, which further decomposed into the within-group performance and the technological gap. The performance of the hotel company is assessed through the aggregation of multiple financial indicators using the composite indicator (CI) derived from the DEA model. A bivariate analysis based on the Tobit regression to test the robustness of brand effect on FP of hotel companies (HC) was also included. Findings The empirical results show that branded companies, on average, have significantly better overall FP than non-branded companies. On the one hand, the brand effect tends to improve the within-group FP of HCs and the brand presents a statistically significant positive effect on the FP. On the other hand, the best practices are observed in both branded and non-branded companies. Practical implications The results of this study illustrate that, globally, the better FP of the branded companies is because of their individual relative companies' performance and a better model of operation given by the brand effect. Brand affiliation will generally allow for a better FP and essentially a better profitability for invested equity, a higher return on sales and a higher value added per employee. Originality/value The study provides important theoretical and practical contributions that can assist the strategic decision of the HCs in choosing to operate independently or to adopt brand affiliation. Also, it is innovative because the FP of branded and non-branded HCs is measured not using a set of individual financial ratios but through a single CI that aggregates those financial ratios, using a DEA model.

Abstract

Abstract
Psoriasis is a chronic inflammatory skin disease with a high worldwide incidence that in worst cases reaches 4.6%. This dermatosis can be associated with other comorbidities and has a significant negative impact on labor productivity and the quality of life of affected people. During day-to-day lives, psoriasis patients come across several practical clinical difficulties, e.g. to i) easily register a time evolution of affected skin areas (for later analysis by health carers); ii) daily evaluate the size of each affected skin area, to be able to iii) calculate the amount of medication to be applied on those affected body areas. In such a context, this paper proposes the Follow-App mobile system aiming to support people with psoriasis, by alleviating and managing their daily life with the disease. More precisely, the goals of the system are: to allow individual photographic registration of body parts affected by psoriasis; in addition, cataloging each image according to its body segment location and sampling date; then, on those photos, automatically detect and segment the affected skin surface, to posteriorly be able to calculate the area of the lesions; finally, based on the area and prescribed medicine, dynamically accounting the amount of topical medicine to use. These were the requirements addressed by the proposed system prototype. The evaluation tests on the ability to detect and quantify the area of the skin lesions were performed on a data-set with 22 images. The proposed segmentation algorithm for detecting the area of redness lesions reached an IoU rate over 81%. Therefore, the proposed Follow-App mobile system may become an important asset for people with psoriasis since the extent and redness of affected areas are major evaluation factors for the disease severity.

Abstract
Portugal has developed a national roadmap for hydrogen deployment as a key element of the Portuguese energy transition towards carbon neutrality, with a major contribution towards the electrification of society, generating synergies between the electric and gas systems. Considering the government goals for hydrogen injection within natural gas infrastructures for 2025 and 2030, as long as the indicative trajectories for 2040 and 2050, the authors used the natural gas forecast of the security of supply official report in order to obtain the hydrogen demand and power plant capacity, evaluating the system effort to meet public policy goals. Several alternative scenarios were developed for sensitive analysis, in order to assess the different strategies of hydrogen deployment, considering production from an electrolyzer. Regarding the current Portuguese situation and every scenario outcome, the authors stated that major efforts must be undertaken in order to develop full-scale hydrogen projects in order to meet the national goals.

Abstract
This article proposes a risk constrained decision-making problem for wind power producers (WPPs) in a competitive environment. In this problem, the WPP opts to maximize its likely profit whereas aggregators want to minimize their payments. So, this bilevel problem is converted to a single level one. Then, the WPP offers proper prices to the aggregators to attract them to supply their demand. Also, these aggregators can procure reserve for the WPP to compensate its uncertainties. Therefore, through a peer-to-peer (P2P) trading mechanism, the WPP requests the aggregators to allocate reserve to cover the uncertainties of the wind generation. Also, due to the presence of uncertain resources of the problem, a risk measurement tool is applied to the problem to control the uncertainties. The effectiveness of the model is assessed on realistic data from the Nordpool market and the results show that as the loads become responsive, more loads are allowed to choose their WPP to supply their load. Also, the reserve that is provided by these responsive loads to the WPP increases.

Abstract
Packet sampling plays an important role in keeping storage and processing requirements at a manageable level in network management. However, because it reduces the amount of available information, it can also reduce the performance of some related tasks, such as detecting security events. In this context, this work explores how packet sampling impacts machine learning-based tasks, in particular, flow-based C2 TLS malware traffic detection using a deep neural network. Based on a proposed lightweight sampling scheme, the ongoing results show a small reduction in classification accuracy compared with analysing all the traffic, while reducing in 10 fold the number of packets processed.