Efeitos dos exercícios aeróbio contínuo e intervalado na variabilidade da frequência cardíaca em adultos jovens saudáveis. Ensaio clínico aleatório

The high-intensity interval exercise has been described as an option for increasing physical activity and its use also being suggested in the therapeutic management of many conditions such as diabetes mellitus and heart failure. However, the knowledge of its physiological effects and parameters that can assure greater safety for interval exercise prescription; especially its effect on short- and medium-term (24 hours after exercise) exercise recovery, need to be clarified. This study objective was to evaluate the effect of continuous and interval aerobic exercise on the cardiac autonomic control immediate and medium term (24 hours), by assessing heart rate variability (HRV). The present study is a randomized crossover clinical trial in which healthy young individuals with low level of physical activity had the VFC 24 hours measured by a heart rate sensor and portable accelerometer (3D eMotion HRV, Kuopio, Finland) before and after continuous aerobic exercise (60-70% HR max, 21 min.) and interval exercise (cycle 1 min. 80-90% HR max, 2 min. at 50-60% HR max, duration 21 min.). HRV was measured in the time and frequency domain and the sympathovagal balance determined by the ratio LF / HF. Nonlinear evaluation was calculated by Shannon entropy. The data demonstrated delayed heart rate recovery immediate after exercise and lower HR after 24 hours compared to pre intervention values, especially in the interval exercise group. There was a tendency to higher predominance and representatives index values of sympathetic stimulation during the day in interval exercise group; however, without statistical significance. The study results help to clarify the effects of interval exercise on the 24 hours following interval exercise, setting parameters for prescription and for further evaluation of groups with metabolic and cardiovascular diseases.

Estudo da transição de fase da percolação através da entropia da informação

Various physical systems have dynamics that can be modeled by percolation processes. Percolation is used to study issues ranging from fluid diffusion through disordered media to fragmentation of a computer network caused by hacker attacks. A common feature of all of these systems is the presence of two non-coexistent regimes associated to certain properties of the system. For example: the disordered media can allow or not allow the flow of the fluid depending on its porosity. The change from one regime to another characterizes the percolation phase transition. The standard way of analyzing this transition uses the order parameter, a variable related to some characteristic of the system that exhibits zero value in one of the regimes and a nonzero value in the other. The proposal introduced in this thesis is that this phase transition can be investigated without the explicit use of the order parameter, but rather through the Shannon entropy. This entropy is a measure of the uncertainty degree in the information content of a probability distribution. The proposal is evaluated in the context of cluster formation in random graphs, and we apply the method to both classical percolation (Erd¨os- R´enyi) and explosive percolation. It is based in the computation of the entropy contained in the cluster size probability distribution and the results show that the transition critical point relates to the derivatives of the entropy. Furthermore, the difference between the smooth and abrupt aspects of the classical and explosive percolation transitions, respectively, is reinforced by the observation that the entropy has a maximum value in the classical transition critical point, while that correspondence does not occurs during the explosive percolation.

Estudo da transição de fase da percolação através da entropia da informação

Various physical systems have dynamics that can be modeled by percolation processes. Percolation is used to study issues ranging from fluid diffusion through disordered media to fragmentation of a computer network caused by hacker attacks. A common feature of all of these systems is the presence of two non-coexistent regimes associated to certain properties of the system. For example: the disordered media can allow or not allow the flow of the fluid depending on its porosity. The change from one regime to another characterizes the percolation phase transition. The standard way of analyzing this transition uses the order parameter, a variable related to some characteristic of the system that exhibits zero value in one of the regimes and a nonzero value in the other. The proposal introduced in this thesis is that this phase transition can be investigated without the explicit use of the order parameter, but rather through the Shannon entropy. This entropy is a measure of the uncertainty degree in the information content of a probability distribution. The proposal is evaluated in the context of cluster formation in random graphs, and we apply the method to both classical percolation (Erd¨os- R´enyi) and explosive percolation. It is based in the computation of the entropy contained in the cluster size probability distribution and the results show that the transition critical point relates to the derivatives of the entropy. Furthermore, the difference between the smooth and abrupt aspects of the classical and explosive percolation transitions, respectively, is reinforced by the observation that the entropy has a maximum value in the classical transition critical point, while that correspondence does not occurs during the explosive percolation.