Genetic differentiation, effective population size and gene flow in marine fishes : implications for stock management

Many commercially exploited marine fish and mollusc species exhibit no or a low degree of genetic differentiation in neutral marker genes. This lack of genetic differentiation, typically attributed to high degree of gene flow in marine environments, has sometimes supported the thinking that genetically indistinguishable stocks can be managed as being one panmictic population. Recent comparative studies of neutral marker gene and quantitative trait differentiation in a wide variety of taxa - including several marine organisms - show that a high degree of genetic differentiation (as measured by Q_) in ecologically and economically important traits is a common place occurrence, even when the degree of differentiation in neutral marker genes (as measured by F_) is low or absent. In fact, among the empirical studies made so far, the outcome Q_>F_ is pervasive. This accords with the increasing evidence that natal homing and self-replenishment of local populations may be more common in marine habitats than previously anticipated. If so, the low degree of genetic differentiation in neutral genetic markers could be a simple consequence of the large effective population size (N_e) of many marine populations, effectively buffering them against differentiation due to genetic drift. However, genetic markers linked to parts of the genome under directional selection should readily diverge in allele frequencies especially when N_e is high. In fact, several recent studies have discovered that such loci provide a way to differentiate among stocks undifferentiated in neutral marker genes. Hence, the study of adaptive rather than neutral genetic differentiation among fish and shellfish populations might provide practical tools for stock identification and thereby contribute to improved fisheries policies.Special Revie

Mobile Indoor Augmented Reality. Exploring applications in hospitality environments.

Augmented reality (AR) is been increasingly used in mobile devices. Most of the available applications are set to work outdoors, mainly due to the availability of a reliable positioning system. Nevertheless, indoor (smart) spaces offer a lot of opportunities of creating new service concepts. In particular, in this paper we explore the applicability of mobile AR to hospitality environments (hotels and similar establishments). From the state-of-the-art of technologies and applications, a portfolio of services has been identified and a prototype using off-the-shelf technologies has been designed. Our objective is to identify the next technological challenges to overcome in order to have suitable underlying infrastructures and innovative services which enhance the traveller?s experience

Enhancing Interaction With Smart Objects Throught Mobile Devices

Interaction with smart objects can be accomplished with different technologies, such as tangible interfaces or touch computing, among others. Some of them require the object to be especially designed to be 'smart', and some other are limited in the variety and complexity of the possible actions. This paper describes a user-smart object interaction model and prototype based on the well known event-condition-action (ECA) reasoning, which can work, to a degree, independently of the intelligence embedded into the smart object. It has been designed for mobile devices to act as mediators between users and smart objects and provides an intuitive means for personalization of object's behavior. When the user is close to an object, this one publishes its 'event & action' capabilities to the user's device. The user may accept the object's module offering, which will enable him to configure and control that object, but also its actions with respect to other elements of the environment or the virtual world. The modular ECA interaction model facilitates the integration of different types of objects in a smart space, giving the user full control of their capabilities and facilitating creative mash-uping to build customized functionalities that combine physical and virtual action

Improving distribution system state estimation with synthetic measurements

Distribution state estimation is a desired feature of modern power systems. The availability of measurements from smart meters has opened the door to extend the application of state estimation techniques down to end customers, preferably at the secondary distribution transformer level. However, the light coupling between phases makes the estimation of certain state variables, such as voltage phase angles, a great challenge. This paper proposes the use of synthetic measurements as a means of including cross-coupled information in the available set of measurements. This possibility can be easily implemented in line supervisors located at secondary transformer stations without the need for new hardware, just by embracing a different connection of measurement devices. This work demonstrates that this costless action results in a strong reduction of the sensibility of phase angle estimation errors with respect to measurement noise, thus leading to an important improvement in the quality of the results.publishedVersio

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements

Forest and arborescent scrub habitats of special interest for SCIs in Central Spain

The habitat of the several territories in Ciudad Real (Castilla-La Mancha, Spain) are studued through the and mapping (scale 1:10.000) and vegetation analysis. The distribution and surface of the habitat presents in the Sites of Community Interest (SCIs), as well as pressures, threats, trends, and state of conservation are described. These site contributes significantly to the maintenance or restoration at a favourable conservation status of a natural habitat type or of a species of community intesess.These specially protected areas are part of the Natura 2000 network. We discuss the diversity of forest habitats characterized by species of the genus Quercus L., focusing only on the plant communities in the Habitats Directive 92/43/EEC of 1992, regarding the conservation of fauna and flora and habitats of interest owing to their endemic or rare character. Habitats and species must be studied in combination to ensure the maximum reliability of the results. We concentrate on habitats with low representation in the territory as a consequence of their rarity or endemicity. We study the following habitats of special interest: 9230—Mediterranean-Ibero-Atlantic and Galaico-Portuguese oak woods of Quercus robur and Quercus pyrenaica; 9240—Iberian oaks of Quercus faginea and Quercus canariensis; 9320—Thermomediterranean forests of Olea and Ceratonia (Iberian Peninsula, Balearic and Canary Islands); 9540—Mediterranean pine forests of endemic Pinus pinaster (Pinus pinaster subsp. acutisquama); 9560—Endemic forests with Juniperus spp.; 5210. Arborescent scrub with Juniperus spp.info:eu-repo/semantics/publishedVersio

Dynamic Analysis, Stability and Design of Grid Forming Converters With PI-Based Voltage Control in DC and 3-Phase AC Microgrids

This paper analyzes the dynamic behavior of the voltage control loop based on proportional-integral regulators, commonly used for grid-forming converters in 3-phase AC and DC Microgrids and applications that involve a DC-link voltage control. The paper proposes a simple and accurate generalized analysis useful both for the system characterization and design. Two different control schemes, based on linear (Direct Voltage Control, DVC) and quadratic voltage feedback (Quadratic Voltage Control, QVC), are analytically studied, simulated and experimentally tested, demonstrating a superior performance of the QVC under the presence of constant power loads. The operation limits, the system stability and the disturbance rejection capability are analyzed considering the effect of control and plant parameters and the effect of the different types of disturbances and the operating point, taking into account the non-linearities of the system. The analysis is mainly focused on the effect of constant power loads given their negative impact on the system performance. The study provides a generic procedure for the analysis and design of proportional-integral voltage controllers, including the selection of the system capacitance for meeting specific dynamic specifications while considering system characteristics as the load level, the stability margins and the maximum voltage deviation under disturbances

Modeling of Current Consumption in 802.15.4/ZigBee Sensor Motes

Battery consumption is a key aspect in the performance of wireless sensor networks. One of the most promising technologies for this type of networks is 802.15.4/ZigBee. This paper presents an empirical characterization of battery consumption in commercial 802.15.4/ZigBee motes. This characterization is based on the measurement of the current that is drained from the power source under different 802.15.4 communication operations. The measurements permit the definition of an analytical model to predict the maximum, minimum and mean expected battery lifetime of a sensor networking application as a function of the sensor duty cycle and the size of the sensed data

Ultra-fast quantum randomness generation by accelerated phase diffusion in a pulsed laser diode

We demonstrate a high bit-rate quantum random number generator by interferometric detection of phase diffusion in a gain-switched DFB laser diode. Gain switching at few-GHz frequencies produces a train of bright pulses with nearly equal amplitudes and random phases. An unbalanced Mach-Zehnder interferometer is used to interfere subsequent pulses and thereby generate strong random-amplitude pulses, which are detected and digitized to produce a high-rate random bit string. Using established models of semiconductor laser field dynamics, we predict a regime of high visibility interference and nearly complete vacuum-fluctuation-induced phase diffusion between pulses. These are confirmed by measurement of pulse power statistics at the output of the interferometer. Using a 5.825 GHz excitation rate and 14-bit digitization, we observe 43 Gbps quantum randomness generation.This work was supported by the ERC under project MAMBO (Proof of Concept of PER- CENT) and project AQUMET, MINECO under projects FIS2011-23520, TEC2010-14832, and Explora INTRINQRA, Galician Regional Government under projects CN2012/279 and CN2012/260 “Consolidation of research units: AtlantTIC” and FEDER under project Ref: UPVOV10-3E-492.Peer ReviewedPostprint (published version

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.This work has been jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología de la Región de Murcia) under grant 15290/PI/2010, by the Spanish MINECO and the European Commission FEDER funds under grants TIN2009-14475-C04 and TIN2012-31345, and by the Catholic University of Murcia (UCAM) under grant PMAFI/26/12. This work was partially supported by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain