Shrimp farming: Where does the carbon go?

Abstract The muscle tissues of the Litopenaeus vannamei shrimp grown in ponds through organic and traditional (intensive) management show that δ13C values were similar amongst the shrimp. Shrimp grown in the traditional pond were enriched in 13C by 7‰ relative to the carbon isotope ratios of their feed. The differences in the carbon isotope ratios of shrimp and feed in the traditional pond shows that the feed is not the main carbon source for shrimp grown in the traditional intensive management. Using mass balance we calculate that feed in traditional culture contributes at most 13% of the shrimp's carbon biomass

Identificación del mezclador de gases de la nueva incubadora neonatal BAN

The objective of this work is to develop a method of Systems Identification applied to the gas mixer in the neonatal incubator BAN, with the aim of finding a mathematical model that describes its dynamics to implement later a controller. The method considered the conduct of an identification nonparametric which was to excite the entrance to the mixer with signals generated by a computer to determine basic information but valuable. Then, the parametric identification was made consisting of excite the entrance to the mixer with a pseudo-random binary signal to obtain the data matrix with which the coefficients were obtained model ARX. The validation and cross correlation conducted showed the effectiveness of the method. Based on the survey results mixer, it is concluded that the subsystem Pipeline Oxygen is the fourth order considering actuators mechanically coupled and transmitter and subsystem Pipeline Air is the fourth order considering the actuator and transmitter.El objetivo del trabajo presentado fue el desarrollo de un método de Identificación de Sistemas aplicado al mezclador de gases de la incubadora neonatal BAN, con la finalidad de encontrar un modelo matemático que describa su dinámica para implementar posteriormente un controlador. El método empleado consideró la realización de una Identificación No Paramétrica, la cual consistió en excitar la entrada del mezclador con señales generadas por una computadora para poder determinar información básica pero valiosa. Luego, se realizó la Identificación Paramétrica que consistió en excitar la entrada del mezclador con una señal binaria seudo aleatoria para obtener la matriz de datos, con ellos se obtuvieron los coeficientes del modelo ARX. Las pruebas de validación cruzada y correlación efectuada s mostraron la efectividad del método. Basado en los resultados del estudio del mezclador, se concluye que el subsistema Ducto de Oxígeno es de cuarto orden considerando los actuadores acoplados mecánicamente y el subsistema Ducto de Aire es de cuarto orden considerando el actuador y el transmisor

The use of cosmic muons in detecting heterogeneities in large volumes

The muon intensity attenuation method to detect heterogeneities in large matter volumes is analyzed. Approximate analytical expressions to estimate the collection time and the signal to noise ratio, are proposed and validated by Monte Carlo simulations. Important parameters, including point spread function and coordinate reconstruction uncertainty are also estimated using Monte Carlo simulations.Comment: 8 pages, 11 figures, submetted to NIM

Particle finite element method applied to granular material flow

A numerical model, based on a rate-dependent constitutive model, via a flow formulation, and in the framework of the particle finite element method (PFEM) is proposed. It is settled on the assumption that the powder can be modelled as a continuous medium. The model, provided with the corresponding characterization of the parameters, is able to capture the two fundamental phenomena observed during the granular material flow: 1) the irreversibility of most of the deformation experienced by the material and 2) the energy dissipation of the granular system through the inter-particle friction processes, modelled by the plastic dissipation associated with the material model. Experimental and numerical results have been compared in order to study the viability of the proposed model.Peer ReviewedPostprint (published version

Studying soil moisture at a national level through statistical analysis of NASA NLDAS data

The purpose of this research is to enable better understanding of current environmental conditions through the relations of environmental variables to the historical record. Our approach is to organize and visualize land surface model (LSM) outputs and statistics in a web application, using the latest technologies in geographic information systems (GISs), web services, and cloud computing. The North American Land Data Assimilation System (NLDAS-2) (http://ldas.gsfc.nasa.gov/nldas/; Documentation: ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/README.NLDAS2.pdf) drives four LSM (e.g., Noah) (http://ldas.gsfc.nasa.gov/nldas/NLDAS2model.php) that simulate a suite of states and fluxes for central North America. The NLDAS-2 model output is accessible via multiple methods, designed to handle the outputs as time-step arrays. To facilitate data access as time series, selected NLDAS-Noah variables have been replicated byNASA as point-location files. These time series filesor 'data rods' are accessible through web services. In this research, 35-year historical daily cumulative distribution functions (CDFs) are constructed using the data rods for the top-meter soil moisture variable. The statistical data are stored in and served from the cloud. The latest values in the Noah model are compared with the CDFs and displayed in a web application. Two case studies illustrate the utility of this approach: the 2011 Texas drought, and the 31 October 2013 flash flood in Austin, Texas

Reconocimiento de Personas en Ambiente con Emisiones de Humo Usando Sensor Laser y Redes Neuronales Convolucionales desde Nube de Puntos 3D. Parte 1.

Cuando ocurren incendios en Plantas industriales, los materiales siniestrados y el humo dificultan la identificación del personal. Los Drones con sensores laser y redes CNN posibilitan el reconocimiento de personas en tales ambientes. El objetivo del proyecto es el estudio de un sistema neuronal convolucional para el reconocimiento de personas en un ambiente con emisiones de humo (parte1) y para su implementación usa un sensor laser, una tarjeta controladora y un dron (parte2). El método empleado consideró la realización de un reconocimiento de personas usando una red CNN previamente entrenada con nube de puntos 3D. La prueba se realizó con Alexnet e imágenes de personas. Los resultados (parte1) muestran que una matriz de confusión del 97.5 % ha sido alcanzada. Basado en el estudio, se concluye que el sistema neuronal de reconocimiento de personas usando CNN en ambientes siniestrados presenta un comportamiento muy aceptable para su aplicabilidad.Palabras clave: reconocimiento de personas, redes neuronales convolucionales, programación en entorno Matlab, sensor laser y dron

Recognition of People in Environment with Smoke Emissions Using Laser Sensor and Convolutional Neural Networks from 3D Pointed Cloud.

Cuando ocurren incendios en Plantas industriales, los materiales siniestrados y el humo dificultan la identificación del personal. Los Drones con sensores laser y redes CNN posibilitan el reconocimiento de personas en tales ambientes.  El objetivo del proyecto es el estudio de un sistema neuronal convolucional para el reconocimiento de personas en un ambiente con emisiones de humo (parte1) y para su implementación usa un sensor laser, una tarjeta controladora y un dron (parte2). El método empleado consideró la realización de un reconocimiento de personas usando una red CNN previamente entrenada con nube de puntos 3D. La prueba se realizó con Alexnet e imágenes de personas. Los resultados (parte1) muestran que una matriz de confusión del 97.5 % ha sido alcanzada. Basado en el estudio, se concluye que el sistema neuronal de reconocimiento de personas usando CNN en ambientes siniestrados presenta un comportamiento muy aceptable para su aplicabilidad. Palabras clave: reconocimiento de personas, redes neuronales convolucionales, programación en entorno Matlab, sensor laser y dron

Jitter Tolerance Acceleration Using the Golden Section Optimization Technique

Post-silicon validation of high-speed input/output (HSIO) links is a critical process for product qualification schedules of computer platforms under the current time-to-market (TTM) commitments. The goal of post-silicon validation for HSIO links is to confirm design robustness of both receiver (Rx) and transmitter (Tx) circuitry in a real application environment. One of the most common ways to evaluate the performance of a HSIO link is to characterize the Rx jitter tolerance (JTOL) performance by measuring the bit error rate (BER) through the link under worst stressing conditions. However, JTOL testing is very time-consuming when executing at specification BER, and the testing time is extremely increased when considering manufacturing process, voltage, and temperature (PVT) test coverage for a qualification decision. In order to speed up this process, we propose a new approach for JTOL testing based on the golden section algorithm. The proposed method takes advantage of the fast execution of the golden section search with a high BER, while overcoming the lack of correlation between different BERs by performing a downward linear search at the actual target BER until no errors are seen. Our proposed methodology is validated by implementing it in a server HSIO link