On the distribution of an effective channel estimator for multi-cell massive MIMO

Accurate channel estimation is of utmost importance for massive MIMO systems to provide significant improvements in spectral and energy efficiency. In this work, we present a study on the distribution of a simple but yet effective and practical channel estimator for multi-cell massive MIMO systems suffering from pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without previous knowledge of the inter-cell large-scale channel coefficients and noise power, asymptotically approximating the performance of the linear MMSE estimator as the number of antennas increases. We prove that the distribution of the proposed channel estimator can be accurately approximated by the circularly-symmetric complex normal distribution, when the number of antennas, M, deployed at the base station is greater than 10

Assessment of fatigue resistance of additivated asphalt concrete incorporating fibers and polymers

This paper reports the evaluation of fatigue response of asphalt mixtures produced with different additives, namely cellulose and synthetic fibers, amorphous polyolefin, Ethyl Vinyl Acetate (EVA) in comparison with conventional asphalt mixtures. The additive content was also analyzed by producing asphalt mixtures with 3, 6 and 9% of additive. Fatigue testing was performed with an indirect tensile test apparatus under controlled stress mode of loading. A comparing analysis of the fatigue resistance within different methods was carried out. Fatigue life was defined using the classical approach in which the number of cycles reaches the double of initial deformation. It was also defined in terms of different methods based on dissipated energy: total dissipated energy, ratio of dissipated energy change and plateau value. This paper offers an analysis of different methods of evaluating the indirect tensile fatigue test. The testing conducted clearly shows that polymer modification may extend the fatigue life and that energetic methods can be effectively applied to data from indirect tensile fatigue tests

The Stellar Parameters and Evolutionary State of the Primary in the d'-Symbiotic System StH\alpha190

We report on a high-resolution, spectroscopic stellar parameter and abundance analysis of a d' symbiotic star: the yellow component of StH\alpha190. This star has recently been discovered, and confirmed here, to be a rapidly rotating (vsini=100 km/s) subgiant, or giant, that exhibits radial-velocity variations of probably at least 40 km/s, indicating the presence of a companion (a white dwarf star). It is found that the cool stellar component has Teff=5300K and log g=3.0. The iron and calcium abundances are close to solar, however, barium is overabundant, relative to Fe and Ca, by about +0.5 dex. The barium enhancement reflects mass-transfer of s-process enriched material when the current white dwarf was an asymptotic giant branch (AGB) star. The past and future evolution of this binary system depends critically on its current orbital period, which is not yet known. Concerted and frequent radial-velocity measurements are needed to provide crucial physical constraints to this d' symbiotic system.Comment: 9 pages, 1 table, 3 figures. In press to Astrophysical Journal Letter

Singular Features of Trypanosomatids' Phosphotransferases Involved in Cell Energy Management

Trypanosomatids are responsible for economically important veterinary affections and severe human diseases. In Africa, Trypanosoma brucei causes sleeping sickness or African trypanosomiasis, while in America, Trypanosoma cruzi is the etiological agent of Chagas disease. These parasites have complex life cycles which involve a wide variety of environments with very different compositions, physicochemical properties, and availability of metabolites. As the environment changes there is a need to maintain the nucleoside homeostasis, requiring a quick and regulated response. Most of the enzymes required for energy management are phosphotransferases. These enzymes present a nitrogenous group or a phosphate as acceptors, and the most clear examples are arginine kinase, nucleoside diphosphate kinase, and adenylate kinase. Trypanosoma and Leishmania have the largest number of phosphotransferase isoforms ever found in a single cell; some of them are absent in mammals, suggesting that these enzymes are required in many cellular compartments associated to different biological processes. The presence of such number of phosphotransferases support the hypothesis of the existence of an intracellular enzymatic phosphotransfer network that communicates the spatially separated intracellular ATP consumption and production processes. All these unique features make phosphotransferases a promising start point for rational drug design for the treatment of human trypanosomiasis

IMPROVEMENT OF RADIATION DOSE ESTIMATION DUE TO NUCLEAR ACCIDENTS USING DEEP NEURAL NETWORK AND GPU

Recently, the use of mobile devices has been proposed for dose assessment during nuclear accidents. The idea is to support field teams, providing an approximated estimation of the dose distribution map in the vicinity of the nuclear power plant (NPP), without needing to be connected to the NPP systems. In order to provide such stand-alone execution, the use of artificial neural networks (ANN) has been proposed in substitution of the complex and time consuming physical models executed by the atmospheric dispersion radionuclide (ADR) system. One limitation observed on such approach is the very time-consuming training of the ANNs. Moreover, if the number of input parameters increases the performance of standard ANNs, like Multilayer-Perceptron (MLP) with backpropagation training, is affected leading to unreasonable training time. To improve learning, allowing better dose estimations, more complex ANN architectures are required. ANNs with many layers (much more than a typical number of layers), referred to as Deep Neural Networks (DNN), for example, have demonstrating to achieve better results. On the other hand, the training of such ANNs is very much slow. In order to allow the use of such DNNs in a reasonable training time, a parallel programming solution, using Graphic Processing Units (GPU) and Computing Unified Device Architecture (CUDA) is proposed. This work focuses on the study of computational technologies for improvement of the ANNs to be used in the mobile application, as well as their training algorithms

GPU-BASED PARALLEL COMPUTING IN REAL-TIME MODELING OF ATMOSPHERIC TRANSPORT AND DIFFUSION OF RADIOACTIVE MATERIAL

Atmospheric radionuclide dispersion systems (ARDS) are essential mechanisms to predict the consequences of unexpected radioactive releases from nuclear power plants. Considering, that during an eventuality of an accident with a radioactive material release, an accurate forecast is vital to guide the evacuation plan of the possible affected areas. However, in order to predict the dispersion of the radioactive material and its impact on the environment, the model must process information about source term (radioactive materials released, activities and location), weather condition (wind, humidity and precipitation) and geographical characteristics (topography). Furthermore, ARDS is basically composed of 4 main modules: Source Term, Wind Field, Plume Dispersion and Doses Calculations. The Wind Field and Plume Dispersion modules are the ones that require a high computational performance to achieve accurate results within an acceptable time. Taking this into account, this work focuses on the development of a GPU-based parallel Plume Dispersion module, focusing on the radionuclide transport and diffusion calculations, which use a given wind field and a released source term as parameters. The program is being developed using the C ++ programming language, allied with CUDA libraries. In comparative case study between a parallel and sequential version of the slower function of the Plume Dispersion module, a speedup of 11.63 times could be observed

A mobile dose prediction system based on artificial neural networks for NPP emergencies with radioactive material releases

This work presents the approach of a mobile dose prediction system for NPP emergencies with nuclear material release. The objective is to provide extra support to field teams decisions when plant information systems are not available. However, predicting doses due to atmospheric dispersion of radionuclide generally requires execution of complex and computationally intensive physical models. In order to allow such predictions to be made by using limited computational resources such as mobile phones, it is proposed the use of artificial neural networks (ANN) previously trained (offline) with data generated by precise simulations using the NPP atmospheric dispersion system. Typical situations for each postulated accident and respective source terms, as well as a wide range of meteorological conditions have been considered. As a first step, several ANN architectures have been investigated in order to evaluate their ability for dose prediction in hypothetical scenarios in the vicinity of CNAAA Brazilian NPP, in Angra dos Reis, Brazil. As a result, good generalization and a correlation coefficient of 0.99 was achieved for a validation data set (untrained patterns). Then, selected ANNs have been coded in Java programming language to run as an Android application aimed to plot the spatial dose distribution into a map.In this paper, the general architecture of the proposed system is described; numerical results and comparisons between investigated ANN architectures are discussed; performance and limitations of running the Application into a commercial mobile phone are evaluated and possible improvements and future works are pointed

Neuroimmune and inflammatory signals in complex disorders of the central nervous system

An extensive microglial-astrocyte-monocyte-neuronal cross talk seems to be crucial for normal brain function, development, and recovery. However, under certain conditions neuroinflammatory interactions between brain cells and neuroimmune cells influence disease outcome and brain pathology. Microglial cells express a range of functional states with dynamically pleomorphic profiles from a surveilling status of synaptic transmission to an active player in major events of development such as synaptic elimination, regeneration, and repair. Also, inflammation mediates a series of neurotoxic roles in neuropsychiatric conditions and neurodegenerative diseases. The present review discusses data on the involvement of neuroinflammatory conditions that alter neuroimmune interactions in four different pathologies. In the first section of this review, we discuss the ability of the early developing brain to respond to a focal lesion with a rapid compensatory plasticity of intact axons and the role of microglial activation and proinflammatory cytokines in brain repair. In the second section, we present data of neuroinflammation and neurodegenerative disorders and discuss the role of reactive astrocytes in motor neuron toxicity and the progression of amyotrophic lateral sclerosis. In the third section, we discuss major depressive disorders as the consequence of dysfunctional interactions between neural and immune signals that result in increased peripheral immune responses and increase proinflammatory cytokines. In the last section, we discuss autism spectrum disorders and altered brain circuitries that emerge from abnormal long-term responses of innate inflammatory cytokines and microglial phenotypic dysfunctions.Fil: Liberman, Ana Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Trias, Emiliano. Instituto Pasteur de Montevideo; UruguayFil: Da Silva Chagas, Luana. Universidade Federal Fluminense; BrasilFil: Trindade, Pablo. No especifíca;Fil: Dos Santos Pereira, Marissol. Fundación Oswaldo Cruz; Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Refojo, Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Hedin Pereira, Cecilia. Universidade Federal do Rio de Janeiro; Brasil. Fundación Oswaldo Cruz; BrasilFil: Serfaty, Claudio A.. Universidade Federal Fluminense; Brasi

Numerical indications on the semiclassical limit of the flipped vertex

We introduce a technique for testing the semiclassical limit of a quantum gravity vertex amplitude. The technique is based on the propagation of a semiclassical wave packet. We apply this technique to the newly introduced "flipped" vertex in loop quantum gravity, in order to test the intertwiner dependence of the vertex. Under some drastic simplifications, we find very preliminary, but surprisingly good numerical evidence for the correct classical limit.Comment: 4 pages, 8 figure