Seawater carbonate chemistry and biological processes during experiments with temperate coral Cladocora caespitosa, 2010

Atmospheric CO2 partial pressure (pCO2) is expected to increase to 700 µatm or more by the end of the present century. Anthropogenic CO2 is absorbed by the oceans, leading to decreases in pH and the CaCO3 saturation state of the seawater. Elevated pCO2 was shown to drastically decrease calcification rates in tropical zooxanthellate corals. Here we show, using the Mediterranean zooxanthellate coral Cladocora caespitosa, that an increase in pCO2, in the range predicted for 2100, does not reduce its calcification rate. Therefore, the conventional belief that calcification rates will be affected by ocean acidification may not be widespread in temperate corals. Seasonal change in temperature is the predominant factor controlling photosynthesis, respiration, calcification and symbiont density. An increase in pCO2, alone or in combination with elevated temperature, had no significant effect on photosynthesis, photosynthetic efficiency and calcification. The lack of sensitivity C. caespitosa to elevated pCO2 might be due to its slow growth rates, which seem to be more dependent on temperature than on the saturation state of calcium carbonate in the range projected for the end of the century.

Seawater carbonate chemistry, length and survival of Inland silverside, Menidia beryllina, during experiments, 2012

Absorption of anthropogenic carbon dioxide by the world's oceans is causing mankind's 'other CO2 problem', ocean acidification. Although this process will challenge marine organisms that synthesize calcareous exoskeletons or shells, it is unclear how it will affect internally calcifying organisms, such as marine fish. Adult fish tolerate short-term exposures to CO2 levels that exceed those predicted for the next 300 years (~2,000 ppm), but potential effects of increased CO2 on growth and survival during the early life stages of fish remain poorly understood. Here we show that the exposure of early life stages of a common estuarine fish (Menidia beryllina) to CO2 concentrations expected in the world's oceans later this century caused severely reduced survival and growth rates. When compared with present-day CO2 levels (~400 ppm), exposure of M. beryllina embryos to ~1,000 ppm until one week post-hatch reduced average survival and length by 74% and 18%, respectively. The egg stage was significantly more vulnerable to high CO2-induced mortality than the post-hatch larval stage. These findings challenge the belief that ocean acidification will not affect fish populations, because even small changes in early life survival can generate large fluctuations in adult-fish abundance.

Australia's Foreign Economic Policy: a 'State-Society Coalition' Approach and a Historical Overview

This paper aims to explain the historical development of Australia's foreign economic policy by using an analytical framework called a 'state-society coalition' approach. This approach focuses on virtual coalitions of state and society actors that share 'belief systems' and hold similar policy ideas, goals and preferences, as basic units (policy subsystems) of policy making. Major policy changes occur when a dominant coalition is replaced by another. The paper argues that, in Australia, there have been three major state-society coalitions in the foreign economic policy issue area: 'protectionists', 'trade liberalisers' and 'optional bilateralists'. The rise and fall of these coalitions resulted in distinctive shifts of Australia's foreign economic policy in the 1980s towards unilateral and multilateral liberalisation and in the late 1990s towards bilateral trade and investment arrangements.

Production fragmentation, upstreamness, and value-added : evidence from factory Asia 1990-2005

We exploit the recent release of the 2005 Asian Input-Output Matrix to dress a picture of the geographic fragmentation of value added in Factory Asia from 1990 to 2005. We document 3 stylized facts. The first is that the average share of foreign value added embedded in production rose by about 7 percentage points between 1990 and 2005, from 9% to 16%. The second is that, contrary to popular belief, China's production embeds a smaller share of foreign value added than other Factory Asia countries'. Between 1990 and 2005 among Factory Asia countries China grew most after Japan as a source of value added to other countries' production. Third, country-industries at the upstream and downstream extremities of the supply chain embed a smaller share of foreign value added than those with intermediate levels of upstreamness.

Nonparametric Message Passing Methods for Cooperative Localization and Tracking

The objective of this thesis is the development of cooperative localization and tracking algorithms using nonparametric message passing techniques. In contrast to the most well-known techniques, the goal is to estimate the posterior probability density function (PDF) of the position of each sensor. This problem can be solved using Bayesian approach, but it is intractable in general case. Nevertheless, the particle-based approximation (via nonparametric representation), and an appropriate factorization of the joint PDFs (using message passing methods), make Bayesian approach acceptable for inference in sensor networks. The well-known method for this problem, nonparametric belief propagation (NBP), can lead to inaccurate beliefs and possible non-convergence in loopy networks. Therefore, we propose four novel algorithms which alleviate these problems: nonparametric generalized belief propagation (NGBP) based on junction tree (NGBP-JT), NGBP based on pseudo-junction tree (NGBP-PJT), NBP based on spanning trees (NBP-ST), and uniformly-reweighted NBP (URW-NBP). We also extend NBP for cooperative localization in mobile networks. In contrast to the previous methods, we use an optional smoothing, provide a novel communication protocol, and increase the efficiency of the sampling techniques. Moreover, we propose novel algorithms for distributed tracking, in which the goal is to track the passive object which cannot locate itself. In particular, we develop distributed particle filtering (DPF) based on three asynchronous belief consensus (BC) algorithms: standard belief consensus (SBC), broadcast gossip (BG), and belief propagation (BP). Finally, the last part of this thesis includes the experimental analysis of some of the proposed algorithms, in which we found that the results based on real measurements are very similar with the results based on theoretical models.

Optimized edge appearance probability for cooperative localization based on tree-reweighted nonparametric belief propagation

Nonparametric belief propagation (NBP) is a well-known particle-based method for distributed inference in wireless networks. NBP has a large number of applications, including cooperative localization. However, in loopy networks NBP suffers from similar problems as standard BP, such as over-confident beliefs and possible nonconvergence. Tree-reweighted NBP (TRW-NBP) can mitigate these problems, but does not easily lead to a distributed implementation due to the non-local nature of the required so-called edge appearance probabilities. In this paper, we propose a variation of TRWNBP, suitable for cooperative localization in wireless networks. Our algorithm uses a fixed edge appearance probability for every edge, and can outperform standard NBP in dense wireless networks.

Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing

Belief propagation (BP) is a technique for distributed inference in wireless networks and is often used even when the underlying graphical model contains cycles. In this paper, we propose a uniformly reweighted BP scheme that reduces the impact of cycles by weighting messages by a constant ?edge appearance probability? rho ? 1. We apply this algorithm to distributed binary hypothesis testing problems (e.g., distributed detection) in wireless networks with Markov random field models. We demonstrate that in the considered setting the proposed method outperforms standard BP, while maintaining similar complexity. We then show that the optimal ? can be approximated as a simple function of the average node degree, and can hence be computed in a distributed fashion through a consensus algorithm.

Uniformly Reweighted Belief Propagation: A Factor Graph Approach

Tree-reweighted belief propagation is a message passing method that has certain advantages compared to traditional belief propagation (BP). However, it fails to outperform BP in a consistent manner, does not lend itself well to distributed implementation, and has not been applied to distributions with higher-order interactions. We propose a method called uniformly-reweighted belief propagation that mitigates these drawbacks. After having shown in previous works that this method can substantially outperform BP in distributed inference with pairwise interaction models, in this paper we extend it to higher-order interactions and apply it to LDPC decoding, leading performance gains over BP.

Effects of diet and exercise on metabolic syndrome and the underlying factors = Efectos de la dieta y el ejercicio sobre el síndrome metabólico y sus factores

Introducción. El número de personas que padecen síndrome metabólico ha incrementado a nivel mundial durante las últimas dos décadas. Existen numerosos estudios que tratan de comparar prevalencias según los diferentes criterios y estimaciones del riesgo metabólico. De ellos se puede concluir que el principal hallazgo ha sido recalcar la necesidad de una definición estándar universal. A pesar de estas discrepancias no hay lugar a duda sobre el problema de salud pública que esto conlleva. Se necesitan medidas y estrategias urgentes para prevenir y controlar esta emergente epidemia global y para ello se debe prestar especial atención a los cambios en el estilo de vida, fundamentalmente dieta y ejercicio. A pesar de todo, existe a día de hoy una importante controversia sobre el tipo de ejercicio más efectivo y su combinación con la dieta para conseguir mejoras en la salud. Objetivos. Estudiar los índices de riesgo metabólico empleados en la literatura científica y las terapias basadas en dieta y ejercicio para el tratamiento de los factores del síndrome metabólico en adultos con sobrepeso. Diseño de investigación. Los datos empleados en el análisis de esta tesis son, primeramente un estudio piloto, y posteriormente parte del estudio “Programas de Nutrición y Actividad Física para el tratamiento de la obesidad” (PRONAF). El estudio PRONAF es un proyecto consistente en un estudio clínico sobre programas de nutrición y actividad física para el sobrepeso y la obesidad, desarrollado en España durante varios años de intervenciones. Fue diseñado, en parte, para tratar de comparar protocolos de entrenamiento de resistencia, cargas y combinado en igualdad de volumen e intensidad, con el objetivo de evaluar su impacto en los factores de riesgo y la prevalencia del síndrome metabólico en personas con sobrepeso y obesidad. El diseño experimental es un control aleatorio y el protocolo incluye 3 modos de ejercicio (entrenamiento de resistencia, con cargas y combinado) y restricción dietética sobre diversas variables determinantes del estado de salud. Las principales variables para la investigación que comprende esta tesis fueron: actividad física habitual, marcadores de grasa corporal, niveles de insulina, glucosa, triglicéridos, colesterol total, colesterol HDL, colesterol LDL, presión arterial y parámetros relacionados con el ejercicio. Conclusiones. A) Los índices de riesgo metabólico estudiados presentan resultados contradictorios en relación al riesgo metabólico en un individuo, dependiendo de los métodos matemáticos empleados para el cálculo y de las variables introducidas, tanto en mujeres sanas como en adultos en sobrepeso. B) El protocolo de entrenamiento combinado (de cargas y de resistencia) junto con la dieta equilibrada propuesto en este estudio fue la mejor estrategia para la mejora del riesgo de síndrome metabólico en adultos con sobrepeso. C) Los protocolos de entrenamiento supervisado de resistencia, con cargas y combinado junto con la restricción nutricional, no obtuvieron mejoras sobre el perfil lipídico, más allá de los cambios conseguidos con el protocolo de dieta y recomendaciones generales de actividad física habitual en clínica, en adultos con sobrepeso. Background. Over the past two decades, a striking increase in the number of people with the MetS worldwide has taken place. Many studies compare prevalences using different criteria and metabolic risk estimation formulas, and perhaps their main achievement is to reinforce the need for a standardized international definition. Although these discrepancies, there is no doubt it is a public health problem. There is urgent need for strategies to prevent and manage the emerging global epidemic, special consideration should be given to behavioral and lifestyle, mainly diet and exercise. However, there is still controversy about the most effective type of exercise and diet combination to achieve improvements. Objectives. To study the metabolic risk scores used in the literature and the diet and exercise therapies for the treatment of the MetS factors in overweight adults. Research design. The data used in the analysis was collected firstly in a pilot study and lately, as a part of the “Programas de Nutrición y Actividad física para el tratamiento de la obesidad” study (PRONAF). The PRONAF Study is a clinical research project in nutrition and physical activity programs for overweight and obesity, carried out in Spain (2008-2011). Was designed, in part, to attempt to match the volume and intensity of endurance, strength and combined training protocols in order to evaluate their impact on risk factors and MetS prevalence in overweight and obese people. The design and protocol included three exercise modes (endurance, strength and combined training) and diet restriction, in a randomized controlled trial concerning diverse health status variables. The main variables under investigation were habitual physical activity, markers of body fat, fasting serum levels of insulin, glucose, triglycerides, total, LDL and HDL cholesterol, blood pressure and diet and exercise parameters. Main outcomes. A) The metabolic risk scores studied presented contradictory results in relation to the metabolic risk of an individual, depending on the mathematical method used and the variables included, both in healthy women and overweight adults. B) The protocol proposed for combination of strength and endurance training combined with a balance diet was the optimal strategy for the improvement of MetS risk in overweight adults. C) The intervention program of endurance, strength or combined supervised training protocol with diet restriction did not achieved further improvements in lipid profile than a habitual clinical practice protocol including dietary advice and standard physical activity recommendations, in overweight adults.

The role of computational logic as a hinge paradigm among deduction, problem solving, programming, and parallelism

This paper presents some brief considerations on the role of Computational Logic in the construction of Artificial Intelligence systems and in programming in general. It does not address how the many problems in AI can be solved but, rather more modestly, tries to point out some advantages of Computational Logic as a tool for the AI scientist in his quest. It addresses the interaction between declarative and procedural views of programs (deduction and action), the impact of the intrinsic limitations of logic, the relationship with other apparently competing computational paradigms, and finally discusses implementation-related issues, such as the efficiency of current implementations and their capability for efficiently exploiting existing and future sequential and parallel hardware. The purpose of the discussion is in no way to present Computational Logic as the unique overall vehicle for the development of intelligent systems (in the firm belief that such a panacea is yet to be found) but rather to stress its strengths in providing reasonable solutions to several aspects of the task.

Identification of pressures and maintenance in railway tunnels

This paper describes an interactive set of tools used to determine the safety of tunnels and to provide data for the decision making of its mainteinance. Although, no doubt, there are still several drawbacks in the difficult procedures in use it is clear that the way is promising and future improvements both in experimental and analytical methods will increase our understanding of this matter.

Reducing communication overhead for cooperative localization using nonparametric belief propagation

A number of methods for cooperative localization has been proposed, but most of them provide only location estimate, without associated uncertainty. On the other hand, nonparametric belief propagation (NBP), which provides approximated posterior distributions of the location estimates, is expensive mostly because of the transmission of the particles. In this paper, we propose a novel approach to reduce communication overhead for cooperative positioning using NBP. It is based on: i) communication of the beliefs (instead of the messages), ii) approximation of the belief with Gaussian mixture of very few components, and iii) censoring. According to our simulations results, these modifications reduce significantly communication overhead while providing the estimates almost as accurate as the transmission of the particles.

B2DI a bayesian BDI agent model with causal belief updating based on MSBN

In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of these beliefs is updated based on MSBN. The classical BDI agent architecture has been extended in order to manage uncertainty using Bayesian reasoning. The resulting extended model, so-called B2DI, proposes a new control loop. The proposed B2DI model has been evaluated in a network fault diagnosis scenario. The evaluation has compared this model with two previously developed agent models. The evaluation has been carried out with a real testbed diagnosis scenario using JADEX. As a result, the proposed model exhibits significant improvements in the cost and time required to carry out a reliable diagnosis.

Cooperative localization in mobile networks using nonparametric variants of belief propagation

Of the many state-of-the-art methods for cooperative localization in wireless sensor networks (WSN), only very few adapt well to mobile networks. The main problems of the well-known algorithms, based on nonparametric belief propagation (NBP), are the high communication cost and inefficient sampling techniques. Moreover, they either do not use smoothing or just apply it o ine. Therefore, in this article, we propose more flexible and effcient variants of NBP for cooperative localization in mobile networks. In particular, we provide: i) an optional 1-lag smoothing done almost in real-time, ii) a novel low-cost communication protocol based on package approximation and censoring, iii) higher robustness of the standard mixture importance sampling (MIS) technique, and iv) a higher amount of information in the importance densities by using the population Monte Carlo (PMC) approach, or an auxiliary variable. Through extensive simulations, we confirmed that all the proposed techniques outperform the standard NBP method.

Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks

Non-parametric belief propagation (NBP) is a well-known message passing method for cooperative localization in wireless networks. However, due to the over-counting problem in the networks with loops, NBP’s convergence is not guaranteed, and its estimates are typically less accurate. One solution for this problem is non-parametric generalized belief propagation based on junction tree. However, this method is intractable in large-scale networks due to the high-complexity of the junction tree formation, and the high-dimensionality of the particles. Therefore, in this article, we propose the non-parametric generalized belief propagation based on pseudo-junction tree (NGBP-PJT). The main difference comparing with the standard method is the formation of pseudo-junction tree, which represents the approximated junction tree based on thin graph. In addition, in order to decrease the number of high-dimensional particles, we use more informative importance density function, and reduce the dimensionality of the messages. As by-product, we also propose NBP based on thin graph (NBP-TG), a cheaper variant of NBP, which runs on the same graph as NGBP-PJT. According to our simulation and experimental results, NGBP-PJT method outperforms NBP and NBP-TG in terms of accuracy, computational, and communication cost in reasonably sized networks.