Ex situ sulphate reduction rates of sediment at the rim of a vesicomyd clam colony in the Japan Deep Sea Trench (dive 955)

Sediment samples were collected from the rim of a large vesicomyid clam colony in the Japan Deep Sea Trench. Immediately after sample recovery onboard, the sediment core was sub-sampled for ex situ rate measurements. Sulfate reduction were measured ex situ by the whole core injection method with three replicates. We incubated the samples at in situ temperature (1.5°C) for 48 hours with carrier-free 35SO4 (dissolved in water, 50 kBq). Sediment was fixed 20 ml ZnAc solution (20%, w/v) for AOM or SR. Turnover rates were measured as previously described (Kallmeyer et al., 2004).

Ex situ sulphate reduction rates of sediment at the rim of a vesicomyd clam colony in the Japan Deep Sea Trench (dive 957)

Sediment samples were collected from the rim of a large vesicomyid clam colony in the Japan Deep Sea Trench. Immediately after sample recovery onboard, the sediment core was sub-sampled for ex situ rate measurements. Sulfate reduction were measured ex situ by the whole core injection method with three replicates. We incubated the samples at in situ temperature (1.5°C) for 48 hours with carrier-free 35SO4 (dissolved in water, 50 kBq). Sediment was fixed 20 ml ZnAc solution (20%, w/v) for AOM or SR. Turnover rates were measured as previously described (Kallmeyer et al., 2004).

Self-organizing Routing Algorithm fo Wireless Sensors Networks (WSN) using Ant Colony Optimization (ACO) with Tinyos.

This paper describes the basic tools to work with wireless sensors. TinyOShas a componentbased architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyOS's component library includes network protocols, distributed services, sensor drivers, and data acquisition tools ? all of which can be used asia or be further refined for a custom application. TinyOS was originally developed as a research project at the University of California Berkeley, but has since grown to have an international community of developers and users. Some algorithms concerning packet routing are shown. Incar entertainment systems can be based on wireless sensors in order to obtain information from Internet, but routing protocols must be implemented in order to avoid bottleneck problems. Ant Colony algorithms are really useful in such cases, therefore they can be embedded into the sensors to perform such routing task.

Freeform Fresnel RXI-RR Köhler design with spectrum-splitting for photovoltaics

The development of a novel optical design for the high concentration photovoltaics (HPCV) nonimaging concentrator (>500x) that utilizes a built-in spectrum splitting concept is presented. The primary optical element (POE) is a flat Fresnel lens and the secondary optical element (SOE) is a free-form RXI-type concentrator with a band-pass filter embedded in it. The POE and SOE perform Köhler integration to produce light homogenization on the receiver. The system uses a combination of a commercial concentration GaInP/GaInAs/Ge 3J cell and a concentration Back-PointContact (BPC) silicon cell for efficient spectral utilization, and an external confinement technique for recovering the 3J cell’s reflection. A design target of an “equivalent” cell efficiency ~46% is predicted using commercial 39% 3J and 26% Si cells. A projected CPV module efficiency of greater than 38% is achievable at a concentration level greater than 500X with a wide acceptance angle of ±1º. A first proof-of concept receiver prototype has been manufactured using a simpler optical architecture (with a lower concentration, ~100x and lower simulated added efficiency), and experimental measurements have shown up to 39.8% 4J receiver efficiency using a 3J cell with a peak efficiency of 36.9%

Free-form Fresnel RXI-RR Köhler design for high-concentration photovoltaics with spectrum-splitting

Development of a novel HCPV nonimaging concentrator with high concentration (>500x) and built-in spectrum splitting concept is presented. It uses the combination of a commercial concentration GaInP/GaInAs/Ge 3J cell and a concentration Back-Point-Contact (BPC) silicon cell for efficient spectral utilization, and external confinement techniques for recovering the 3J cell's reflection. The primary optical element (POE) is a flat Fresnel lens and the secondary optical element (SOE) is a free-form RXI-type concentrator with a band-pass filter embedded in it - Both the POE and SOE performing Köhler integration to produce light homogenization on the receiver. The band-pass filter transmits the IR photons in the 900-1200 nm band to the silicon cell. A design target of an "equivalent" cell efficiency ~46% is predicted using commercial 39% 3J and 26% Si cells. A projected CPV module efficiency of greater than 38% is achievable at a concentration level larger than 500X with a wide acceptance angle of ±1°. A first proof-of concept receiver prototype has been manufactured using a simpler optical architecture (with a lower concentration, ~100x and lower simulated added efficiency), and experimental measurements have shown up to 39.8% 4J receiver efficiency using a 3J cell with a peak efficiency of 36.9%.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

In Llanas and Lantarón, J. Sci. Comput. 46, 485–518 (2011) we proposed an algorithm (EDAS-d) to approximate the jump discontinuity set of functions defined on subsets of ℝ d . This procedure is based on adaptive splitting of the domain of the function guided by the value of an average integral. The above study was limited to the 1D and 2D versions of the algorithm. In this paper we address the three-dimensional problem. We prove an integral inequality (in the case d=3) which constitutes the basis of EDAS-3. We have performed detailed computational experiments demonstrating effective edge detection in 3D function models with different interface topologies. EDAS-1 and EDAS-2 appealing properties are extensible to the 3D case

Approximations on minimum weight pseudo-triangulation problem using ant colony optimization

In this work, we consider the Minimum Weight Pseudo-Triangulation (MWPT) problem of a given set of n points in the plane. Globally optimal pseudo-triangulations with respect to the weight, as optimization criteria, are difficult to be found by deterministic methods, since no polynomial algorithm is known. We show how the Ant Colony Optimization (ACO) metaheuristic can be used to find high quality pseudo-triangulations of minimum weight. We present the experimental and statistical study based on our own set of instances since no reference to benchmarks for these problems were found in the literature. Throughout the experimental evaluation, we appraise the ACO metaheuristic performance for MWPT problem.

Cohesive-frictional modelling of bond and splitting action of prestressing wire

This work shows a numerical procedure for bond between indented wires and concrete, and the coupled splitting of the concrete. The bond model is an interface, non-associative, plasticity model. It is coupled with a cohesive fracture model for concrete to take into account the splitting of such concrete. The radial component of the prestressing force, increased by Poisson’s effect, may split the surrounding concrete, decreasing the wire confinement and diminishing the bonding. The combined action of the bond and the splitting is studied with the proposed model. The results of the numerical model are compared with the results of a series of tests, such as those which showed splitting induced by the bond between wire and concrete. Tests with different steel indentation depths were performed. The numerical procedure accurately reproduces the experimental records and improves knowledge of this complex process.

Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

The application of inverse-dispersion and gradient methods to estimate ammonia emissions from a penguin colony.

Penguin colonies represent some of the most concentrated sources of ammonia emissions to the atmosphere in the world. The ammonia emitted into the atmosphere can have a large influence on the nitrogen cycling of ecosystems near the colonies. However, despite the ecological importance of the emissions, no measurements of ammonia emissions from penguin colonies have been made. The objective of this work was to determine the ammonia emission rate of a penguin colony using inverse-dispersion modelling and gradient methods. We measured meteorological variables and mean atmospheric concentrations of ammonia at seven locations near a colony of Adélie penguins in Antarctica to provide input data for inverse-dispersion modelling. Three different atmospheric dispersion models (ADMS, LADD and a Lagrangian stochastic model) were used to provide a robust emission estimate. The Lagrangian stochastic model was applied both in ‘forwards’ and ‘backwards’ mode to compare the difference between the two approaches. In addition, the aerodynamic gradient method was applied using vertical profiles of mean ammonia concentrations measured near the centre of the colony. The emission estimates derived from the simulations of the three dispersion models and the aerodynamic gradient method agreed quite well, giving a mean emission of 1.1 g ammonia per breeding pair per day (95% confidence interval: 0.4–2.5 g ammonia per breeding pair per day). This emission rate represents a volatilisation of 1.9% of the estimated nitrogen excretion of the penguins, which agrees well with that estimated from a temperature-dependent bioenergetics model. We found that, in this study, the Lagrangian stochastic model seemed to give more reliable emission estimates in ‘forwards’ mode than in ‘backwards’ mode due to the assumptions made.

Automatic design of Ant Colony Optimization for permutation problems

In this study, we present a framework based on ant colony optimization (ACO) for tackling combinatorial problems. ACO algorithms have been applied to many diferent problems, focusing on algorithmic variants that obtain high-quality solutions. Usually, the implementations are re-done for various problem even if they maintain the same details of the ACO algorithm. However, our goal is to generate a sustainable framework for applications on permutation problems. We concentrate on understanding the behavior of pheromone trails and specific methods that can be combined. Eventually, we will propose an automatic offline configuration tool to build an efective algorithm. ---RESUMEN---En este trabajo vamos a presentar un framework basado en la familia de algoritmos ant colony optimization (ACO), los cuales están dise~nados para enfrentarse a problemas combinacionales. Los algoritmos ACO han sido aplicados a diversos problemas, centrándose los investigadores en diversas variantes que obtienen buenas soluciones. Normalmente, las implementaciones se tienen que rehacer, inclusos si se mantienen los mismos detalles para los algoritmos ACO. Sin embargo, nuestro objetivo es generar un framework sostenible para aplicaciones sobre problemas de permutaciones. Nos centraremos en comprender el comportamiento de la sendas de feromonas y ciertos métodos con los que pueden ser combinados. Finalmente, propondremos una herramienta para la configuraron automática offline para construir algoritmos eficientes.