Abstract
The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases.

Abstract
Occupational stress is a complex process affecting health and performance. Air Traffic Control is a complex and demanding profession. The current study demonstrates the concept of using a biomonitoring wearable platform (BWP), that combines self-report measures with biomarkers, to track stress among Air Traffic Controllers. A wearable ECG device was used to gather continuously medical-grade ECG data along with a mobile app for daily stress perception, symptoms and events annotation. A total of 256 hours of data from 32 routine work shifts and 5 days-off, from 5 ATCs was recorded with 35 tagged events using Heart Rate Variability metrics- AVNN, RMSSD, pNN50 and LF/HF were computed from ECG data and analyzed during a) shifts vs days off; b) events vs non-events and c) before and after working pauses. ATCs showed low levels of chronic stress using self-reports. Results showed that stress symptomatology slightly increase from the beginning to the end of the shift (Md=1 to Md=2; p<0.05). Statistical significant physiological changes were found between shifts and days off for AVNN and LF/HF (p<0.05), showing higher physiological activation during shifts. A significant reduction of physiological arousal was verified after working pauses, particularly for AVNN and LF/IIF (p<0.001). Self-reported data also suggests the same trend (p<0.005). Findings reinforced the discriminatory power of AVNN and LF/HF for short-term stress classification using HRV measurements. Results suggest that the rotating working system, with pause/resting periods included, effective acted as a recovery period.

Abstract
A non-parametric identification algorithm is proposed to identify Linear Time Periodic (LTP) systems. The period is unknown and can be any real positive number. The system is modelled as an ARX Linear Parameter Varying (LPV) system with a virtual scheduling signal consisting of two orthogonal sinusoids (a sine and a cosine) with a period equal to the system period. Hence, the system parameters are polynomial functions of the scheduling vector. As these polynomials may have infinite degree, a non-parametric model is adopted to describe the LPV system. This model is identified by a Gaussian Process Regression (GPR) algorithm where the system period is a hyperparameter. The performance of the proposed identification algorithm is illustrated through the identification of a simulated LTP continuous system described by a state-space model. The ARX-LTP discrete-time model estimated in the noiseless case was taken as the true model. Copyright (C) 2021 The Authors.

Abstract
Lung cancer is the type of cancer with highest mortality worldwide. Low-dose computerized tomography is the main tool used for lung cancer screening in clinical practice, allowing the visualization of lung nodules and the assessment of their malignancy. However, this evaluation is a complex task and subject to inter-observer variability, which has fueled the need for computer-aided diagnosis systems for lung nodule malignancy classification. While promising results have been obtained with automatic methods, it is often not straightforward to determine which features a given model is basing its decisions on and this lack of explainability can be a significant stumbling block in guaranteeing the adoption of automatic systems in clinical scenarios. Though visual malignancy assessment has a subjective component, radiologists strongly base their decision on nodule features such as nodule spiculation and texture, and a malignancy classification model should thus follow the same rationale. As such, this study focuses on the characterization of lung nodules as a means for the classification of nodules in terms of malignancy. For this purpose, different model architectures for nodule characterization are proposed and compared, with the final goal of malignancy classification. It is shown that models that combine direct malignancy prediction with specific branches for nodule characterization have a better performance than the remaining models, achieving an Area Under the Curve of 0.783. The most relevant features for malignancy classification according to the model were lobulation, spiculation and texture, which is found to be in line with current clinical practice.

Abstract
Two distinct optical fibers for endoscope-based configurations are demonstrated and studied in this work. The fibers used for the experiment consist of: a conventional singlemode fiber (SMF 28e) and a hollow core photonic crystal fiber (HC-PCF) based on silica. Two studies that allowed the characterization of these fibers, according to their optical output power and when subjected to curvature, were carried out. The intensity power profile was also analysed in relation to the propagation distance, transversal displacement and incidence angle. After this study it can be concluded that the most suitable solution for the endoscope is the HC-PCF fiber working as a transmission probe. For the proof of concept of the fiber-based endoscope, a cleaved multimode fiber (MMF) tip was used as a reception probe and its reflection efficiency was also analysed.

Abstract
The knowledge of the optical properties of biological tissues in a wide spectral range is highly important for the development of noninvasive diagnostic or treatment procedures. The absorption coefficient is one of those properties, from which various information about tissue components can be retrieved. Using transmittance and reflectance spectral measurements acquired from ex vivo rabbit brain cortex samples allowed to calculate its optical properties in the ultraviolet to the near infrared spectral range. Melanin and lipofuscin, the two pigments that are related to the aging of tissues and cells were identified in the cortex absorption. By subtracting the absorption of these pigments from the absorption of the brain cortex, it was possible to evaluate the true ratios for the DNA/RNA and hemoglobin bands in the cortex—12.33-fold (at 260 nm), 12.02-fold (at 411 nm) and 4.47-fold (at 555 nm). Since melanin and lipofuscin accumulation increases with the aging of the brain tissues and are related to the degeneration of neurons and their death, further studies should be performed to evaluate the evolution of pigment accumulation in the brain, so that new optical methods can be developed to aid in the diagnosis and monitoring of brain diseases.