Hydrology has used traditional methods for flood level forecasting. However, this type of forecast can lead to accuracy issues, caused by the nonlinear behavior of floods and limitations by not including all variables, such as water flow, level and precipitation. Consequently, some scientists began to use unconventional methods based on artificial intelligence models, to forecast floods more precisely and rigorously. This paper compares the HEC-RAS one-dimensional flow transit model with an artificial intelligence model based on Artificial Neural Networks, developed in MatLab to predict floods. The results were analyzed using six statistical indicators: mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), square root of the MSE, Pearson correlation coefficient (CC), and concordance correlation coefficient (ρc). In addition, the efficiency coefficient was calculated, and used in a virtual tool called Hydrotest. The analysis shows that forecast models that use neural networks have accurate results, given their closeness to the real data: MAPE between 11.95 and 12.51, CC between 0.90 and 0.92, ρc between 0.84 and 0.87, and a coefficient of efficiency larger than 0.8. The study was conducted on a section of the upper Bogotá River, in Colombia, between the Florence Bridge and Tocancipá hydrological stations. Flow data was taken from the Regional Autonomous Corporation of Cundinamarca (CAR), from September 2009 to October 2013.
Obstructive sleep apnoea-hypopnoea syndrome (OSA) is a respiratory disorder characterised by repetitive obstruction of the upper airway, leading to several interruptions during sleep. It is currently one of the main public health problems worldwide and one of the main cardiovascular risk factors in developed and intermediate developing countries, whose populations are increasing in rates of obesity and age. One of the common treatments for OSA is a continuous positive airway pressure (CPAP) device, which pumps air through a hose, reaches a mask that the patient has over his or her nose and travels the airway, keeping the upper airway open during sleep and avoiding episodes of airway collapse. The problem is that CPAP is not accepted by some patients due to a lack of adaptation, so alternative treatments may be needed. For some years, there have been explorations of treatments related to electrical stimulation of the muscles of the upper airway as therapy to reduce the number of episodes of apnoea (measured through the apnoea–hypopnoea index) during the night, strengthening these muscles through stimulation. This is the protocol of the first clinical study of a rehabilitation device for home use that not only provides functional stimulation of the upper-airway dilator muscles but also provides sensory stimulation. This device works by strengthening the dilating muscles of the upper respiratory tract and improving the sensory capacity of the laryngo-pharyngeal tract and is based on existing publications on the effectiveness of functional and somatosensory neurostimulation through neuroplasticity in the recovery of neurological deficits. Trial registration: Clinicaltrials.gov NCT04607343 (29/10/2020)
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