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"ABSTRAKThe implementation of High Speed Railway (HSR) networks involves a large amount of financial support imposing, not only at the conception and design level, but also during the lineoperation, a demanding, a complete, and a rigorous estimation of the total cost involved in the life cycle of the system. By using appropriatetools for estimating HSR life cycle costs (LCC), itis possible to minimize the final cost and, at the same time, to identify the most important aspects and parameters influencing the cost evaluation. Research, therefore,is not only required on the LCC modeling, but also on the estimation of major degradation factors and in theassessment of its impact on the maintenance needs. This paper deals with this former aspect. The various methodologies for evaluating the geometrical track quality are presented andcompared to each other, namely the J Synthetic Coefficient, the Indian TGI and also the approach presented in the European StandardEN 13848-5. In order to compare these threemethodologies, they are applied to a railway stretch of the Portuguese Northern Railway Line. By doing so, the prediction of track degradation rate within the period of research can bedetermined, which possibly is used in the future for defining cost-effective maintenance strategies."

"The study sought to evaluate smartwatch-based sleep quality indicators of fitness to work. Eighteen males (aged 20–26 years) were assigned to three randomized daytime sleep conditions (bad/moderate/good), which varied in terms of lighting, noise, and temperature, for a six-hour period. After this daytime sleep, participants completed simulated computer tasks during a 12-hour nighttime waking period. Prior to those tasks, participants’ fitness to work was determined by subjective measures that included the Sleep Quality Index-Karolinska Sleep Diary (SQI-KSD)), the Psychomotor Vigilance Task (PVT)), and the Karolinska Sleepiness Scale (KSS) to measure drowsiness. Total sleep time (TST), light sleep quantity (LSQ), deep sleep quantity (DSQ), and REM sleep quantity (REMSQ) were recorded using a smartwatch. The results confirmed that TST, LSQ, and SQI-KSD can be used as measures of sleep quality and fitness to work (p < 0.05)."