Modelling microbial exchanges between forms of soil nitrogen in contrasting ecosystems

Although nitrogen (N) is often combined with carbon (C) in organic molecules, C passes from the air to the soil through plant photosynthesis, whereas N passes from the soil to plants through a chain of microbial conversions. However, dynamic models do not fully consider the microorganisms at the centre of exchange processes between organic and mineral forms of N. This study monitored the transfer of ¹⁴C and ¹⁵N between plant materials, microorganisms, humified compartments, and inorganic forms in six very different ecosystems along an altitudinal transect. The microbial conversions of the ¹⁵N forms appear to be strongly linked to the previously modelled C cycle, and the same equations and parameters can be used to model both C and N cycles. The only difference is in the modelling of the flows between microbial and inorganic forms. The processes of mineralization and immobilization of N appear to be regulated by a two-way microbial exchange depending on the C : N ratios of microorganisms and available substrates. The MOMOS (Modelling of Organic Matter of Soils) model has already been validated for the C cycle and also appears to be valid for the prediction of microbial transformations of N forms. This study shows that the hypothesis of microbial homeostasis can give robust predictions at global scale. However, the microbial populations did not appear to always be independent of the external constraints. At some altitudes their C : N ratio could be better modelled as decreasing during incubation and increasing with increasing C storage in cold conditions. The ratio of potentially mineralizable-¹⁵N/inorganic-¹⁵N and the ¹⁵N stock in the plant debris and the microorganisms was modelled as increasing with altitude, whereas the ¹⁵N storage in stable humus was modelled as decreasing with altitude. This predicts that there is a risk that mineralization of organic reserves in cold areas may increase global warming

The Antiferromagnetic Band Structure of La2CuO4 Revisited

Using the Becke-3-LYP functional, we have performed band structure calculations on the high temperature superconductor parent compound, La2CuO4. Under the restricted spin formalism (rho(alpha) equal to rho(beta)), the R-B3LYP band structure agrees well with the standard LDA band structure. It is metallic with a single Cu x2-y2/O p(sigma) band crossing the Fermi level. Under the unrestricted spin formalism (rho(alpha) not equal to rho(beta)), the UB3LYP band structure has a spin polarized antiferromagnetic solution with a band gap of 2.0 eV, agreeing well with experiment. This state is 1.0 eV (per formula unit) lower than that calculated from the R-B3LYP. The apparent high energy of the spin restricted state is attributed to an overestimate of on-site Coulomb repulsion which is corrected in the unrestricted spin calculations. The stabilization of the total energy with spin polarization arises primarily from the stabilization of the x2-y2 band, such that the character of the eigenstates at the top of the valence band in the antiferromagnetic state becomes a strong mixture of Cu x2-y2/O p(sigma) and Cu z2/O' p(z). Since the Hohenberg-Kohn theorem requires the spin restricted and spin unrestricted calculations give exactly the same ground state energy and total density for the exact functionals, this large disparity in energy reflects the inadequacy of current functionals for describing the cuprates. This calls into question the use of band structures based on current restricted spin density functionals (including LDA) as a basis for single band theories of superconductivity in these materials.Comment: 13 pages, 8 figures, to appear in Phys. Rev. B, for more information see http://www.firstprinciples.co

A face recognition system for assistive robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga