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This work presents an innovative control architecture, which takes its ideas from the theory of adaptive control techniques and the theory of statistical learning at the same time. Taking inspiration from the architecture of a classical neural network with several hidden levels, the principle is to divide the architecture of the adaptive controller into three different levels. Each level implements an algorithm based on learning from data and therefore we can talk about learning concepts. Each level has a different task: the first to learn the required reference to the control loop; the second to learn the coefficients of the state representation of a model of the system to be controlled; and finally, the third to learn the coefficients of the state representation of the actual controller. The design of the control system is reported from both a rigorous and an operational point of view. As an application example, the proposed control technique is applied on a second-order non-linear system. We consider a servo-drive based on a brushless DC (BLDC) motor, whose dynamic model considers all the non-linear effects related to the electromechanical nature of the electric machine itself, and also an accurate model of the switching power converter. The reported example shows the capability of the control algorithm to ensure trajectory tracking while allowing for disturbance rejection with different disturbance signal amplitude. The implementation complexity analysis of the new controller is also proposed, showing its low overhead vs. basic control solutions.

Recurrent cycles of climatic change during the Quaternary period have dramatically affected the population genetic structure of many species. We reconstruct the recent demographic history of the coyote ( Canis latrans ) through the use of Bayesian techniques to examine the effects of Late Quaternary climatic perturbations on the genetic structure of a highly mobile generalist species. Our analysis reveals a lack of phylogeographic structure throughout the range but past population size changes correlated with climatic changes. We conclude that even generalist carnivorous species are very susceptible to environmental changes associated with climatic perturbations. This effect may be enhanced in coyotes by interspecific competition with larger carnivores.

This paper studies the vibrational motion of a dynamical system connected to an electromagnetic device, which is one of the energy harvesting (EH) devices that transform the vibrational motion into electric energy. This system has three degrees-of-freedom (DOF) and consists of two linked parts attached together; one is a nonlinear Duffing oscillator, and the other is a nonlinear damping spring pendulum. The regulating equations of motion (EOM) are achieved utilizing Lagrange’s equations and solved analytically applying the approach of multiple scales (AMS) till the third order of approximation. The accuracy of the attained solutions has been examined by comparing them with the numerical ones of the EOM. The time histories of the solutions and the nonlinear stability analysis of the modulation equations are represented graphically in various plots. The Poincaré maps and phase portraits diagrams displayed the stable behavior of the studied dynamical system. In addition, the different ranges of the stabilities are examined and discussed. In the electromagnetic device, the output power and current time series are depicted as a function of different values of the damping coefficients, excitation amplitudes, and load resistance. It is noted that the output current and power are dropped when the damping coefficient is raised. On the other hand, the increment of the excitation has a positive effect on the electrical generation and produces increment of the output power and current. Furthermore, the output power grows when the total resistance increases to accommodate the applied load. The EH device generates high output current and power at low-frequency values. The significance of this work is limited to the numerous uses of its outcomes in everyday life, such as powering medical devices, serving as a power supply for sensors, and serving as a backup energy source for some electronic devices.

The charged-particle production ratios $\bar{p}/p$, $K^-/K^+$, $��^-/��^+$, $(p + \bar{p})/(��^+ + ��^-)$, $(K^+ + K^-)/(��^+ + ��^-)$ and $(p + \bar{p})/(K^+ + K^-)$ are measured with the LHCb detector using $0.3 {\rm nb^{-1}}$ of $pp$ collisions delivered by the LHC at $\sqrt{s} = 0.9$ TeV and $1.8 {\rm nb^{-1}}$ at $\sqrt{s} = 7$ TeV. The measurements are performed as a function of transverse momentum $p_{\rm T}$ and pseudorapidity $��$. The production ratios are compared to the predictions of several Monte Carlo generator settings, none of which are able to describe adequately all observables. The ratio $\bar{p}/p$ is also considered as a function of rapidity loss, $��y \equiv y_{\rm beam} - y$, and is used to constrain models of baryon transport. Incorrect entries in Table 2 corrected. No consequences for rest of paper