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The demand for real-time Indoor Air Quality (IAQ) management has increased recently, since low-cost and modern sensors such as Particulate Matter (PM), Volatile Organic Compounds (VOCs), Carbon Monoxide (CO), Carbon Dioxide (CO2), Radon (Rn), among others, have been put forward with considerable accuracy. Although these low-cost sensors cannot be considered measurement instruments, they are very useful for a vast number of application domains, such as home automation, smart building management, IAQ management, risk exposure assessment, to name a few. This paper presents a literature review and a prospective analysis and discussion regarding Internet of Things (IoT) technologies adopted to deal with scenarios that present known indoor Radon gas problems. Specifically, the main requirements for developing IoT-enabled radon management solutions are reviewed. Thus, a traditional IoT architecture is described, its main components are analyzed and some of the most recent academic solutions are reviewed. Finally, novel approaches for deploying IoT radon management architectures are presented together with the most relevant open challenges. In this way, this article presents a holistic review of the past, present, and future of indoor radon management in order to provide guidelines for future designers and developers., Comment: Paper accepted in Edge-IoT 2022

The Industry 5.0 paradigm focuses on industrial operator well-being and sustainable manufacturing practices, where humans play a central role, not only during the repetitive and collaborative tasks of the manufacturing process, but also in the management of the factory floor assets. Human factors, such as ergonomics, safety, and well-being, push the human-centric smart factory to efficiently adopt novel technologies while minimizing environmental and social impact. As operations at the factory floor increasingly rely on collaborative robots (CoBots) and flexible manufacturing systems, there is a growing demand for redundant safety mechanisms (i.e., automatic human detection in the proximity of machinery that is under operation). Fostering enhanced process safety for human proximity detection allows for the protection against possible incidents or accidents with the deployed industrial devices and machinery. This paper introduces the design and implementation of a cost-effective thermal imaging Safety Sensor that can be used in the scope of Industry 5.0 to trigger distinct safe mode states in manufacturing processes that rely on collaborative robotics. The proposed Safety Sensor uses a hybrid detection approach and has been evaluated under controlled environmental conditions. The obtained results show a 97% accuracy at low computational cost when using the developed hybrid method to detect the presence of humans in thermal images., Comment: Paper accepted in Edge-IoT 2022

Decentralized Metaverses, built on Web 3.0 and Web 4.0 technologies, have attracted significant attention across various fields. This innovation leverages blockchain, Decentralized Autonomous Organizations (DAOs), Extended Reality (XR) and advanced technologies to create immersive and interconnected digital environments that mirror the real world. This article delves into the Metaverse of Everything (MoE), a platform that fuses the Metaverse concept with the Internet of Everything (IoE), an advanced version of the Internet of Things (IoT) that connects not only physical devices but also people, data and processes within a networked environment. Thus, the MoE integrates generated data and virtual entities, creating an extensive network of interconnected components. This article seeks to advance current MoE, examining decentralization and the application of Opportunistic Edge Computing (OEC) for interactions with surrounding IoT devices and IoE entities. Moreover, it outlines the main challenges to guide researchers and businesses towards building a future cyber-resilient opportunistic MoE.

The advent of Low Power Wide Area Networks (LPWAN) has enabled the feasibility of wireless sensor networks for environmental traffic sensing across urban areas. In this study, we explore the usage of LoRaWAN end nodes as traffic sensing sensors to offer a practical traffic management solution. The monitored Received Signal Strength Indicator (RSSI) factor is reported and used in the gateways to assess the traffic of the environment. Our technique utilizes LoRaWAN as a long-range communication technology to provide a largescale system. In this work, we present a method of using LoRaWAN devices to estimate traffic flows. LoRaWAN end devices then transmit their packets to different gateways. Their RSSI will be affected by the number of cars present on the roadway. We used SVM and clustering methods to classify the approximate number of cars present. This paper details our experiences with the design and real implementation of this system across an area that stretches for miles in urban scenarios. We continuously measured and reported RSSI at different gateways for weeks. Results have shown that if a LoRaWAN end node is placed in an optimal position, up to 96% of correct environment traffic level detection can be obtained. Additionally, we share the l, Comment: 7 pages, accepted to Emerging Topics in Wireless (EmergingWireless) in CoNEXT 2022

We show that the same maximum entropy principle applied to recurrence microstates configures a new way to properly compute the time delay necessary to correctly sample a data set. The new method retrieves results obtained using traditional methods with the advantage of being independent of any free parameter. Since all parameters are automatically set, the method is suitable for use in artificial (computational) intelligence algorithms, recovering correct information embedded in time series, and rationalizing the process of data acquisition since only relevant data must be collected., Comment: 5 pages, 4 figures

An important idea in neural information processing is the communication-through-coherence hypothesis, according to which communication between two brain regions is effective only if they are phase-locked. Also of importance is neuronal variability, a phenomenon in which a single neuron's inter-firing times may be highly variable. In this work, we aim to connect these two ideas by studying the effects of that variability on the capability of neurons to reach phase synchronization. We simulate a network of modified-Hodgkin-Huxley-bursting neurons possessing a small-world topology. First, variability is shown to be correlated with the average degree of phase synchronization of the network. Next, restricting to spatial variability - which measures the deviation of firing times between all neurons in the network - we show that it is positively correlated to a behavior we call promiscuity, which is the tendency of neurons to to have their relative phases change with time. This relation is observed in all cases we tested, regardless of the degree of synchronization or the strength of the inter-neuronal coupling: high variability implies high promiscuity (low duration of phase-locking), even if the network as a whole is synchronized and the coupling is strong. We argue that spatial variability actually generates promiscuity. Therefore, we conclude that variability has a strong influence on both the degree and the manner in which neurons phase synchronize, which is another reason for its relevance in neural communication., Comment: 11 pages, 7 figures, to be submitted to Neural Networks journal

Recurrence entropy $(\cal S)$ is a novel time series complexity quantifier based on recurrence microstates. Here we show that $\mathsf{max}(\cal S)$ is a \textit{parameter-free} quantifier of time correlation of stochastic and chaotic signals, at the same time that it evaluates property changes of the probability distribution function (PDF) of the entire data set. $\mathsf{max}(\cal S)$ can distinguish distinct temporal correlations of stochastic signals following a power-law spectrum, $\displaystyle P(f) \propto 1/f^\alpha$ even when shuffled versions of the signals are used. Such behavior is related to its ability to quantify distinct subsets embedded in a time series. Applied to a deterministic system, the method brings new evidence about attractor properties and the degree of chaoticity. The development of a new parameter-free quantifier of stochastic and chaotic time series opens new perspectives to stochastic data and deterministic time series analyses and may find applications in many areas of science.

An algorithm is devised for solving minimization problems with equality constraints. The algorithm uses first-order derivatives of both the objective function and the constraints. The step is computed as a sum between a steepest-descent step (which minimizes the objective functional) and a correction step related to the Newton method(which aims to solve the equality constraints). The linear combination between these two steps involves coefficients similar to Lagrange multipliers which are computed in a natural way based on the Newton method. The algorithm uses no projection and thus the iterates are not feasible; the constraints are satisfied only in the limit (after convergence). This algorithm was proposed by one of the authors in a previous paper. In the present paper, a local convergence result is proven for a general non-linear setting, where both the objective functional and the constraints are not necessarily convex functions. The algorithm is extended, by means of an active set strategy, to account also for inequality constraints and to address minimax problems. The method is then applied to the optimization of periodic microstructures for obtaining homogenized elastic tensors having negative Poisson ratio (so-called auxetic materials) using shape and/or topology variations in the model hole. In previous works of the same authors, anisotropic homogenized tensors have been obtained which exhibit negative Poisson ratio in a prescribed direction of the plane. In the present work, a new approach is proposed, that employs multi-objective optimization in order to minimize the Poisson ratio of the (possibly anisotropic) homogenized elastic tensor in several prescribed directions of the plane. Numerical examples are presented., Comment: 32 pages, 7 figures

In this letter we explore how recurrence quantifier, the determinism ($\Delta$), can reveal stationarity breaking and coupling between physical systems. We demonstrate that it is possible to detect small variations in a dynamical system based only on temporal signal displayed by another system coupled to it. To introduce basic ideas, we consider a well known dynamical system composed of two master-slave coupled Lorenz oscillators. We start evidencing that due to the sensitivity of $\Delta$ computed from temporal time series of slave oscillator, its is possible to detect the stationary breaking imposed in the master oscillator. As a second example, the method is carried out in a real physiological data acquired from accelerometer sensors ($\mathrm{A_{cc}}$) and used to detect micro arousal phenomenology (described by a sharp burst in $\mathrm{A_{cc}}$ signal) during sleep periods in mice. Moreover, we show for the first time that making use of recurrence quantifier it is possible to infer a coupling between electric signals from hippocampus and "locomotor brain areas" of mice, based only on non invasive data from $\mathrm{A_{cc}}$. Our results suggest new possibilities of analysis of coupled systems making use of accessible time series. Our second example supports an interpretation of an internal coupling detectable as a stationarity breaking in $\mathrm{A_{cc}}$ that occurs some seconds before micro arousal processes during sleep periods in rodents, contributing to the idea that micro arousals are elements of sleep taking part in the regulation of sleep process. Such a characterization of micro arousals can improve our knowledge about sleep fostering tools of sleep diagnose and pharmacology research for mammals in general., Comment: After the review process, the authors do not agree that the paper should be submitted to the arXv. So no future version of the text will be uploaded

Bursting neurons fire rapid sequences of action potential spikes followed by a quiescent period. The basic dynamical mechanism of bursting is the slow currents that modulate a fast spiking activity caused by rapid ionic currents. Minimal models of bursting neurons must include both effects. We considered one of these models and its relation with a generalized Kuramoto model, thanks to the definition of a geometrical phase for bursting and a corresponding frequency. We considered neuronal networks with different connection topologies and investigated the transition from a non-synchronized to a partially phase-synchronized state as the coupling strength is varied. The numerically determined critical coupling strength value for this transition to occur is compared with theoretical results valid for the generalized Kuramoto model., Comment: 31 pages, 5 figures

In this letter we study how hyperbolic and non hyperbolic regions in the neighborhood of a resonant island perform a important role allowing or forbidding stickiness phenomenon around islands in conservative systems. The vicinity of the island is composed by non hyperbolic areas that almost prevent the trajectory to visit the island edge. For some specific parameters there are tiny channels embedded in the non hyperbolic area that are associated to hyperbolic fixed points present in the neighborhood of the islands. Such channels allow the trajectory to be injected in the inner portion of the vicinity. When the trajectory crosses the barrier imposed by the non hyperbolic regions, it spends a long time to abandon the surrounding of the island, since the barrier also prevents the trajectory to scape from the neighborhood of the island. In this scenario the non hyperbolic structures are the responsible for the stickiness phenomena, and more than that, the strength of the sticky effect. We reveal that those properties of the phase space allow us to manipulate the existence of extreme events (and the transport associated to it) responsible for the non equilibrium fluctuation of the system. In fact we demonstrate that monitoring very small portions of the phase space (namely $\approx 4\times 10^{-4}$ \% of it) it is possible to generate a completely diffusive system eliminating long time recurrences that result from the stickiness phenomenon., Comment: 5 pages, 6 figures

In this letter we study how deterministic features presented by a system can be used to perform direct transport in a {\it quasi}-symmetric potential and weak dissipative system. We show that the presence of nonhyperbolic regions around acceleration areas of the phase space plays an important role in the acceleration of particles giving rise to direct transport in the system. Such effect can be observed for a large interval of the weak asymmetric potential parameter allowing the possibility to obtain useful work from unbiased nonequilibrium fluctuation in real systems even in a presence of a {\it quasi}-symmetric potential., Comment: 4 pages, 3 figures

In presence of unstable dimension variability numerical solutions of chaotic systems are valid only for short periods of observation. For this reason, analytical results for systems that exhibit this phenomenon are needed. Aiming to go one step further in obtaining such results, we study the parametric evolution of unstable dimension variability in two coupled bungalow maps. Each of these maps presents intervals of linearity that define Markov partitions, which are recovered for the coupled system in the case of synchronization. Using such partitions we find exact results for the onset of unstable dimension variability and for contrast measure, which quantifies the intensity of the phenomenon in terms of the stability of the periodic orbits embedded in the synchronization subspace., Comment: 5 pages, 2 figures - to be submitted to Physical Review E - Brief Report

We show that the threshold of complete synchronization in a lattice of coupled non-smooth chaotic maps is determined by linear stability along the directions transversal to the synchronization subspace. We examine carefully the sychronization time and show that a inadequate observation of the system evolution leads to wrong results. We present both careful numerical experiments and a rigorous mathematical explanation confirming this fact, allowing for a generalization involving hyperbolic coupled map lattices., Comment: 22 pages (preprint format), 4 figures - accepted for publication in Physica A (June 28, 2010)

Many chaotic dynamical systems of physical interest present a strong form of nonhyperbolicity called unstable dimension variability (UDV), for which the chaotic invariant set contains periodic orbits possessing different numbers of unstable eigendirections. The onset of UDV is usually related to the loss of transversal stability of an unstable fixed point embedded in the chaotic set. In this paper, we present a new mechanism for the onset of UDV, whereby the period of the unstable orbits losing transversal stability tends to infinity as we approach the onset of UDV. This mechanism is unveiled by means of a periodic orbit analysis of the invariant chaotic attractor for two model dynamical systems with phase spaces of low dimensionality, and seems to depend heavily on the chaotic dynamics in the invariant set. We also described, for these systems, the blowout bifurcation (for which the chaotic set as a whole loses transversal stability) and its relation with the situation where the effects of UDV are the most intense. For the latter point, we found that chaotic trajectories off, but very close to, the invariant set exhibit the same scaling characteristic of the so-called on-off intermittency., Comment: 32 pages (preprint format), 9 figures

Chaotic dynamical systems with two or more attractors lying on invariant subspaces may, provided certain mathematical conditions are fulfilled, exhibit intermingled basins of attraction: Each basin is riddled with holes belonging to basins of the other attractors. In order to investigate the occurrence of such phenomenon in dynamical systems of ecological interest (two-species competition with extinction) we have characterized quantitatively the intermingled basins using periodic-orbit theory and scaling laws. The latter results agree with a theoretical prediction from a stochastic model, and also with an exact result for the scaling exponent we derived for the specific class of models investigated. We discuss the consequences of the scaling laws in terms of the predictability of a final state (extinction of either species) in an ecological experiment., Comment: 24 pages (preprint format), 6 figures

We investigate the parametric evolution of riddled basins related to synchronization of chaos in two coupled piecewise-linear Lorenz maps. Riddling means that the basin of the synchronized attractor is shown to be riddled with holes belonging to another basin in an arbitrarily fine scale, which has serious consequences on the predictability of the final state for such a coupled system. We found that there are wide parameter intervals for which two piecewise-linear Lorenz maps exhibit riddled basins (globally or locally), which indicates that there are riddled basins in coupled Lorenz equations, as previously suggested by numerical experiments. The use of piecewise-linear maps makes it possible to prove rigorously the mathematical requirements for the existence of riddled basins., Comment: 22 pages (preprint format), 8 figures

We show that the threshold of complete synchronization in a lattice of coupled non-smooth chaotic maps is determined by linear stability along the directions transversal to the synchronization subspace. As a result, the numerically determined synchronization threshold agree with the analytical results previously obtained [C. Anteneodo et al., Phys. Rev. E 68, 045202(R) (2003)] for this class of systems. We present both careful numerical experiments and a rigorous mathematical explanation confirming this fact, allowing for a generalization involving hyperbolic coupled map lattices., Comment: 4 pages, 1 figure, submitted to Physical Review E (rapid communication)

In this paper we re-examine the one-dimensional interaction of electromagnetic and ion acoustic waves in a plasma. Our model is similar to one solved by Rao et al. (Phys. Fluids, vol. 26, 2488 (1983)) under a number of analytical approximations. Here we perform a numerical investigation to examine the stability of the model. We find that for slightly over dense plasmas, the propagation of stable solitary modes can occur in an adiabatic regime where the ion acoustic electric field potential is enslaved to the electromagnetic field of a laser. But if the laser intensity or plasma density increases or the laser frequency decreases, the adiabatic regime loses stability via a transition to chaos. New asymptotic states are attained when the adiabatic regime no longer exists. In these new states, the plasma becomes rarefied, and the laser field tends to behave like a vacuum field., Comment: 19 pages, REVTeX, 6 ps figures, accepted for publication in Phys. Rev. E