Doppler radar having phase modulation of both transmitted and reflected return signals

A microwave radar signal is generated for transmission through an antenna. Before transmission, the signal is phase modulated by 0 deg or 90 deg amounts during each alternate half-cycles of an intermediate frequency (IF) clock signal. After transmission and return, the signal is again phase modulated the same amounts during each alternate half-cycles. The return phase modulated signal is mixed with a leakage signal component of the microwave signal, leaving an IF Doppler. The IF Doppler signal may then be amplified, removing any requirement that direct current level signals be amplified and also removing the effect of detector noise from the Doppler signal

Application of machine learning techniques to weather forecasting

Weather forecasting is, still today, a human based activity. Although computer simulations play a major role in modelling the state and evolution of the atmosphere, there is a lack of methodologies to automate the interpretation of the information generated by these models. This doctoral thesis explores the use of machine learning methodologies to solve specific problems in meteorology and particularly focuses on the exploration of methodologies to improve the accuracy of numerical weather prediction models using machine learning. The work presented in this manuscript contains two different approaches using machine learning. In the first part, classical methodologies, such as multivariate non-parametric regression and binary trees are explored to perform regression on meteorological data. In this first part, we particularly focus on forecasting wind, where the circular nature of this variable opens interesting challenges for classic machine learning algorithms and techniques. The second part of this thesis, explores the analysis of weather data as a generic structured prediction problem using deep neural networks. Neural networks, such as convolutional and recurrent networks provide a method for capturing the spatial and temporal structure inherent in weather prediction models. This part explores the potential of deep convolutional neural networks in solving difficult problems in meteorology, such as modelling precipitation from basic numerical model fields. The research performed during the completion of this thesis demonstrates that collaboration between the machine learning and meteorology research communities is mutually beneficial and leads to advances in both disciplines. Weather forecasting models and observational data represent unique examples of large (petabytes), structured and high-quality data sets, that the machine learning community demands for developing the next generation of scalable algorithms

Review Symposium: Claudia Rozas Gómez, Paul Gibbs and Petra Mikulan on Peter Roberts and Herner Saeverot’s Education and the limits of reason: Reading Dostoevsky, Tolstoy and Nabokov, with a response from the authors, Roberts, P., & Saeverot, H. (2018). Education and the limits of reason: Reading Dostoevsky, Tolstoy and Nabokov. Routledge.

In Education and the Limits of Reason, Roberts and Saeverot (2018) point out that Vladimir Nabokov urged us to read with our spine. They explain this directive as a commitment to reading that engages both the heart and the brain so that neither objective nor subjective interpretations come to dominate our experience of texts. As they beautifully put it, ‘it’s about being touched between the shoulder blades’ (p. 89). In its meticulous entirety, this book demonstrates a reading-with-the-spine through which the reader is introduced to new ways of thinking about a body of philosophical literature and what it may teach us about education

Star Forming Objects in the Tidal Tails of Compact Groups

A search for star forming objects belonging to tidal tails has been carried out in a sample of deep Halpha images of 16 compact groups of galaxies. A total of 36 objects with Halpha luminosity larger than 10^38 erg s-1 have been detected in five groups. The fraction of the total Halpha luminosity of their respective parent galaxies shown by the tidal objects is always below 5% except for the tidal features of HCG95, whose Halpha luminosity amounts to 65% of the total luminosity. Out of this 36 objects, 9 star forming tidal dwarf galaxy candidates have been finally identified on the basis of their projected distances to the nuclei of the parent galaxies and their total Halpha luminosities. Overall, the observed properties of the candidates resemble those previously reported for the so-called tidal dwarf galaxies.Comment: 5 gif figures. Accepted for publication in Astrophysical Journa

A Novel Two-Component System Involved in the Transition to Secondary Metabolism in Streptomyces coelicolor

BACKGROUND: Bacterial two-component signal transduction regulatory systems are the major set of signalling proteins frequently mediating responses to changes in the environment. They typically consist of a sensor, a membrane-associated histidine kinase and a cytoplasmic response regulator. The membrane-associated sensor detects the environmental signal or stress, whereas the cytoplasmic regulatory protein controls the cellular response usually by gene transcription modulation. METHODOLOGY/PRINCIPALFINDINGS: The Streptomyces coelicolor two genes operon SCO5784-SCO5785 encodes a two-component system, where SCO5784 encodes a histidine-kinase sensor and SCO5785 encodes a response regulator protein. When the expression level of the regulator gene decreases, the antibiotic synthesis and sporulation is delayed temporarily in addition to some ribosomal genes became up regulated, whereas the propagation of the regulatory gene in high copy number results in the earlier synthesis of antibiotics and sporulation, as well as the down regulation of some ribosomal genes and, moreover, in the overproduction of several extracellular proteins. Therefore, this two-component system in S. coelicolor seems to influence various processes characterised by the transition from primary to secondary metabolism, as determined by proteomic and transcriptomic analyses. CONCLUSIONS/SIGNIFICANCE: Propagation of SCO5785 in multicopy enhances the production of antibiotics as well as secretory proteins. In particular, the increase in the expression level of secretory protein encoding genes, either as an artefactual or real effect of the regulator, could be of potential usefulness when using Streptomyces strains as hosts for homologous or heterologous extracellular protein production

A system for airport weather forecasting based on circular regression trees

This paper describes a suite of tools and a model for improving the accuracy of airport weather forecasts produced by numerical weather prediction (NWP) products, by learning from the relationships between previously modelled and observed data. This is based on a new machine learning methodology that allows circular variables to be naturally incorporated into regression trees, producing more accurate results than linear and previous circular regression tree methodologies. The software has been made publicly available as a Python package, which contains all the necessary tools to extract historical NWP and observed weather data and to generate forecasts for different weather variables for any airport in the world. Several examples are presented where the results of the proposed model significantly improve those produced by NWP and also by previous regression tree models.TIN2016-78365-R, IT609-1

Tension and stiffness of the hard sphere crystal-fluid interface

A combination of fundamental measure density functional theory and Monte Carlo computer simulation is used to determine the orientation-resolved interfacial tension and stiffness for the equilibrium hard-sphere crystal-fluid interface. Microscopic density functional theory is in quantitative agreement with simulations and predicts a tension of 0.66 kT/\sigma^2 with a small anisotropy of about 0.025 kT and stiffnesses with e.g. 0.53 kT/\sigma^2 for the (001) orientation and 1.03 kT/\sigma^2 for the (111) orientation. Here kT is denoting the thermal energy and \sigma the hard sphere diameter. We compare our results with existing experimental findings

DnaSP v5: A software for comprehensive analysis of DNA polymorphism data

Podeu consultar el programari a: http://hdl.handle.net/2445/53451DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser