Sarcopenia is a process associated to aging. Persistent inflammation and oxidative stress in muscle favour muscle wasting and decreased ability to perform physical activity. Controlled exercise can optimize blood flux and moderate the production of reactive oxygen species. Therefore, supplements that can work as a vasodilators and control oxidative stress, might be beneficial for active elders. In this context, we have tested citrulline supplementation in a group of 44 participants aged from 60–73 years that followed a physical activity program adapted to their age and capacities. Volunteers were divided in two groups: placebo (n = 22) and citrullline supplemented (n = 22). Different physical tests and blood extractions were performed at the beginning and at the end of intervention (six weeks). Strength and endurance showed a tendency to increase in the citrulline supplemented group, with no significant differences respect to placebo. However, walking speed in the citrulline supplemented group improved significantly compared to placebo. Markers of muscle damage as well as circulating levels of testosterone, cortisol and vitamin D showed no significant changes, but a tendency to improve at the end of intervention in the supplemented group compared to placebo. Additional studies are necessary to confirm the effect of citrulline supplementation in sarcopenia delay.

Scientific evidence supports the role of L-glutamine in improving immune function. This could suggest a possible role of L-glutamine in recovery after intense exercise. To this end, the present report aimed to study if oral L-glutamine supplementation could attenuate muscle damage in a group of players of a mainly eccentric sport discipline such as basketball. Participants (n = 12) were supplemented with 6 g/day of glutamine (G group) or placebo (P group) for 40 days in a crossover study design (20 days with glutamine + 20 days with placebo and vice versa). Blood samples were obtained at the beginning and at the end of each period and markers from exercise-induced muscle damage were determined. The glutamine supplemented group displayed significantly low values of aspartate transaminase, creatine kinase and myoglobin in blood, suggesting less muscle damage compared to the placebo. In addition, adrenocorticotropic hormone levels were lower in the glutamine supplemented group than in the placebo. As a result, the circulating cortisol levels did not increase at the end of the study in the glutamine supplemented group. Altogether, the results indicate that glutamine could help attenuate exercise-induced muscle damage in sport disciplines with predominantly eccentric actions.

Affordable and clean energy is one of the Sustainable Development Goals (SDG). SDG compliance and economic crises have boosted investment in solar energy as an important source of renewable generation. Nevertheless, the complex maintenance of solar plants is behind the increasing trend to use advanced artificial intelligence techniques, which critically depend on big amounts of data. In this work, a model based on Deep Convolutional Generative Adversarial Neural Networks (DCGANs) was trained in order to generate a synthetic dataset made of 10,000 electroluminescence images of photovoltaic cells, which extends a smaller dataset of experimentally acquired images. The energy output of the virtual cells associated with the synthetic dataset is predicted using a Random Forest regression model trained from real IV curves measured on real cells during the image acquisition process. The assessment of the resulting synthetic dataset gives an Inception Score of 2.3 and a Fréchet Inception Distance of 15.8 to the real original images, which ensures the excellent quality of the generated images. The final dataset can thus be later used to improve machine learning algorithms or to analyze patterns of solar cell defects.

Buildings are currently among the largest consumers of electrical energy with considerable increases in CO₂ emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped improve electricity consumption, such as energy forecast models. However, because most forecasting models are not focused on updating based on the changing nature of buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned, the objective of this article is to analyze the integration of methods that can help forecasting models to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be used as tools to enhance savings in buildings. For this study, active and passive change detection methods were considered to be integrators in the decision tree and deep learning models. The results show that constant retraining for the decision tree models, integrating change detection methods, helped them to better adapt to changes in the whole building’s electrical consumption. However, for deep learning models, this was not the case, as constant retraining with small volumes of data only worsened their performance. These results may lead to the option of using tree decision models in buildings where electricity consumption is constantly changing.