Heart rate and blood pressure are the most important vital signs in diagnosing disease. Both heart rate and blood pressure are characterized by a high degree of short term variability from moment to moment, medium term over the normal day and night as well as in the very long term over months to years. The study of new mathematical algorithms to evaluate the variability of these cardiovascular parameters has a high potential in the development of new methods for early detection of cardiovascular disease, to establish differential diagnosis with possible therapeutic consequences. The autonomic nervous system is a major player in the general adaptive reaction to stress and disease. The quantitative prediction of the autonomic interactions in multiple control loops pathways of cardiovascular system is directly applicable to clinical situations. Exploration of new multimodal analytical techniques for the variability of cardiovascular system may detect new approaches for deterministic parameter identification. A multimodal analysis of cardiovascular signals can be studied by evaluating their amplitudes, phases, time domain patterns, and sensitivity to imposed stimuli, i.e., drugs blocking the autonomic system. The causal effects, gains, and dynamic relationships may be studied through dynamical fuzzy logic models, such as the discrete-time model and discrete-event model. We expect an increase in accuracy of modeling and a better estimation of the heart rate and blood pressure time series, which could be of benefit for intelligent patient monitoring. We foresee that identifying quantitative mathematical biomarkers for autonomic nervous system will allow individual therapy adjustments to aim at the most favorable sympathetic-parasympathetic balance.
Studies of the Sun and the Earth's atmosphere and climate consider solar variability and its constant monitoring an important driver in climate models. Solar irradiance is one of the main parameters that allow monitoring this variation, which can be studied in spectrum ranges or in its version that integrates all those ranges. Some physical and semi-empirical models were developed and made available and are very relevant for the reconstruction of irradiance in periods of data failure or absence in the collection. However, the solar irradiance prediction could benefit ionospheric and climate models through prior knowledge of irradiance values hours or days ahead, without the need to know or have available other parameters that would be necessary for their reconstruction. This paper presents a neural network based approach, which uses images of the solar photosphere to extract sunspot and active region information and thus generate inputs for recurrent neural networks to perform the irradiance prediction. Experiments were performed with two recurrent neural network architectures for short- and long-term predictions of total and spectral solar irradiance along three wavelengths. The results show good quality of prediction results for TSI and motivate individual effort in improving the prediction of each type of irradiance predicted in this work. The results obtained for SSI point out that photosphere images do not have the same influence on the prediction of all wavelengths tested, but encourage the bet on new spectral lines prediction. The quality closeness in neural networks and physical models results raise the possibility that prediction is an option to be considered in studies for which only reconstructed data are considered.
The prediction of solar irradiance at the top of the atmosphere is useful for research that analyzes the behavior and response of the different layers of the Earth’s atmosphere to variations in solar activity. It would also be useful for the reconstruction of the measurement history (time series) of different instruments that suffered from time failures and discrepancies in scales due to the calibration of equipment. In this work we compare three Keras recurrent neural network architectures to perform forecast of the total solar irradiance. The experiments are part of a larger proposal for modularization of the prediction workflow, which uses digital images of the Sun as input, and aims to make the process modular, accessible and reproducible.
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