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In this paper the removal of a wide range of heavy metal ions from different chemical environments has been explored with the use of phosphate-functionalised superparamagnetic iron oxide nanoparticles (SPIONs), specifically magnetite (Fe3O4).

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.

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

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.