Abstract
The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincare plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

Abstract
The ever-increasing number of interconnected devices in smart environments, i.e., homes and cities, is bolstering the amount of data generated and exchanged. These devices can range from small embedded platforms, such as those included in home appliances, to critical operational systems, such as traffic lights. However, this increasing adoption is raising significant security and privacy concerns. Although some researchers have already solved some of these issues, data privacy still lacks a viable solution, especially when considering a flexible, decentralized approach to avoid a central overseer. One of the biggest challenges regarding privacy is the lack of transparency about how data flows are mediated and regulated as, often, these resources share data with external entities without the users' knowledge. We argue that a novel data-sharing control mechanism is required to properly control users' privacy and their respective Internet of Things (IoT) devices. This work focuses on a middleware layer solution for the IoT devices, which allows the control of the data generated by the device by its owner. The platform places the user as an active participant in the data market, behaving as its own data intermediary for potential consumers by monitoring, controlling, and negotiating the usage of their data.

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Abstract
About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents The Entropy Universe, which aims to review the many variants of entropies applied to time-series. The purpose is to answer research questions such as: How did each entropy emerge? What is the mathematical definition of each variant of entropy? How are entropies related to each other? What are the most applied scientific fields for each entropy? We describe in-depth the relationship between the most applied entropies in time-series for different scientific fields, establishing bases for researchers to properly choose the variant of entropy most suitable for their data. The number of citations over the past sixteen years of each paper proposing a new entropy was also accessed. The Shannon/differential, the Tsallis, the sample, the permutation, and the approximate entropies were the most cited ones. Based on the ten research areas with the most significant number of records obtained in the Web of Science and Scopus, the areas in which the entropies are more applied are computer science, physics, mathematics, and engineering. The universe of entropies is growing each day, either due to the introducing new variants either due to novel applications. Knowing each entropy's strengths and of limitations is essential to ensure the proper improvement of this research field.

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Abstract