A first-order lumped parameters model of electrohydraulic actuators for low-inertia rotating systems with dry friction

In aerospace engineering, there are several control systems affected by dry friction, which are characterized by low inertia and high working frequencies. For these systems, it is possible to use a downgraded, first-order dynamic model to represent their behaviour properly without run into numerical problems that would be harmful for the solution itself and would require high computational power to be solved, which means more weight, costs, and complexity. Yet, the effect of dry friction is still possible to be accounted for accurately using a new algorithm based on the Coulomb friction model applied to the downgraded, first-order dynamic model. In this paper, the degraded first-order model is applied to an electrohydraulic servomechanism with its PID control unit, hydraulic motor, electrohydraulic servo-valve, and applied load. These components represent a classic airplane actuator system. The downgraded model will be compared to the second-order one focusing on the pros and cons of the reduction process with a focus on the effect of dry friction for reversible and irreversible actuators

Time variable cosmological constant of holographic origin with interaction in Brans-Dicke theory

Time variable cosmological constant (TVCC) of holographic origin with interaction in Brans-Dicke theory is discussed in this paper. We investigate some characters for this model, and show the evolutions of deceleration parameter and equation of state (EOS) for dark energy. It is shown that in this scenario an accelerating universe can be obtained and the evolution of EOS for dark energy can cross over the boundary of phantom divide. In addition, a geometrical diagnostic method, jerk parameter is applied to this model to distinguish it with cosmological constant.Comment: 10 pages, 9 figure

Observational signatures of Jordan-Brans-Dicke theories of gravity

We analyze the Jordan-Brans-Dicke model (JBD) of gravity, where deviations from General Relativity (GR) are described by a scalar field non-minimally coupled to gravity. The theory is characterized by a constant coupling parameter, $\omega_{\rm JBD}$; GR is recovered in the limit $\omega_{\rm JBD} \to \infty$. In such theories, gravity modifications manifest at early times, so that one cannot rely on the usual approach of looking for inconsistencies in the expansion history and perturbations growth in order to discriminate between JBD and GR. However, we show that a similar technique can be successfully applied to early and late times observables instead. Cosmological parameters inferred extrapolating early-time observations to the present will match those recovered from direct late-time observations only if the correct gravity theory is used. We use the primary CMB, as will be seen by the Planck satellite, as the early-time observable; and forthcoming and planned Supernov{\ae}, Baryonic Acoustic Oscillations and Weak Lensing experiments as late-time observables. We find that detection of values of $\omega_{\rm JBD}$ as large as 500 and 1000 is within reach of the upcoming (2010) and next-generation (2020) experiments, respectively.Comment: minor revision, references added, matching version published in JCA

miR-SEA: miRNA Seed Extension based Aligner Pipeline for NGS Expression Level Extraction

The advent of Next Generation Sequencing (NGS) technology has enabled a new major approach for micro RNAs (miRNAs) expression profiling through the so called RNA-Sequencing (RNA-Seq). Different tools have been developed in the last years in order to detect and quantify miRNAs, especially in pathological samples, starting from the big amount of data deriving from RNA sequencing. These tools, usually relying on general purpose alignment algorithms, are however characterized by different sensitivity and accuracy levels and in the most of the cases provide not overlapping predictions. To overcome these limitations we propose a novel pipeline for miRNAs detection and quantification in RNA-Seq sample, miRNA Seed Extension Aligner (miR-SEA), based on an experimental evidence concerning miRNAs structure. The proposed pipeline was tested on three Colorectal Cancer (CRC) RNA-Seq samples and the obtained results compared with those provided by two well-known miRNAs detection tools showing good ability in performing detection and quantification more adherent to miRNAs structur

miR-SEA: miRNA Seed Extension based Aligner Pipeline for NGS Expression Level Extraction

The advent of Next Generation Sequencing (NGS) technology has enabled a new major approach for micro RNAs (miRNAs) expression profiling through the so called RNA-Sequencing (RNA-Seq). Different tools have been developed in the last years in order to detect and quantify miRNAs, especially in pathological samples, starting from the big amount of data deriving from RNA sequencing. These tools, usually relying on general purpose alignment algorithms, are however characterized by different sensitivity and accuracy levels and in the most of the cases provide not overlapping predictions. To overcome these limitations we propose a novel pipeline for miRNAs detection and quantification in RNA-Seq sample, miRNA Seed Extension Aligner (miR-SEA), based on an experimental evidence concerning miRNAs structure. The proposed pipeline was tested on three Colorectal Cancer (CRC) RNA-Seq samples and the obtained results compared with those provided by two well-known miRNAs detection tools showing good ability in performing detection and quantification more adherent to miRNAs structure

Learning the relationship between galaxies spectra and their star formation histories using convolutional neural etworks and cosmological simulations

We present a new method for inferring galaxy star formation histories (SFH) using machine learning methods coupled with two cosmological hydrodynamic simulations. We train Convolutional Neural Networks to learn the relationship between synthetic galaxy spectra and high resolution SFHs from the EAGLE and Illustris models. To evaluate our SFH reconstruction we use Symmetric Mean Absolute Percentage Error (SMAPE), which acts as a true percentage error in the low-error regime. On dust-attenuated spectra we achieve high test accuracy (median SMAPE = 10.5%). Including the effects of simulated observational noise increases the error (12.5%), however this is alleviated by including multiple realisations of the noise, which increases the training set size and reduces overfitting (10.9%). We also make estimates for the observational and modelling errors. To further evaluate the generalisation properties we apply models trained on one simulation to spectra from the other, which leads to only a small increase in the error (median SMAPE ∼15%⁠). We apply each trained model to SDSS DR7 spectra, and find smoother histories than in the VESPA catalogue. This new approach complements the results of existing SED fitting techniques, providing star formation histories directly motivated by the results of the latest cosmological simulations

A new diagrammatic representation for correlation functions in the in-in formalism

In this paper we provide an alternative method to compute correlation functions in the in-in formalism, with a modified set of Feynman rules to compute loop corrections. The diagrammatic expansion is based on an iterative solution of the equation of motion for the quantum operators with only retarded propagators, which makes each diagram intrinsically local (whereas in the standard case locality is the result of several cancellations) and endowed with a straightforward physical interpretation. While the final result is strictly equivalent, as a bonus the formulation presented here also contains less graphs than other diagrammatic approaches to in-in correlation functions. Our method is particularly suitable for applications to cosmology.Comment: 14 pages, matches the published version. includes a modified version of axodraw.sty that works with the Revtex4 clas