Selectivity of diacylhydrazine insecticides to the predatory bug Orius laevigatus: In vivo and modeling/docking experiments

BACKGROUND: Knowledge of pesticide selectivity to natural enemies is necessary for a successful implementation of biological and chemical control methods in integrated pest management (IPM) programs. Diacylhydrazine (DAH)-based ecdysone agonists also known as molting-accelerating compounds (MACs) are considered a selective group of insecticides, and their compatibility with predatory Heteroptera, which are used as biological control agents, is known. However, their molecular mode of action has not been explored in beneficial insects such as Orius laevigatus (Fieber) (Hemiptera: Anthocoridae). RESULTS: In this project in vivo toxicity assays demonstrated that the DAH-based RH-5849, tebufenozide and methoxyfenozide have no toxic effect against O. laevigatus. The ligand-binding domain (LBD) of the ecdysone receptor (EcR) of O. laevigatus was sequenced and a homology protein model was constructed which confirmed a cavity structure with 12 ?-helixes, harboring the natural insect molting hormone 20-hydroxyecdysone. However, docking studies showed that a steric clash occurred for the DAH-based insecticides due to a restricted extent of the ligand-binding cavity of the EcR of O. laevigatus. CONCLUSIONS: The insect toxicity assays demonstrated that MACs are selective for O. laevigatus. The modeling/docking experiments are indications that these pesticides do not bind with the LBD-EcR of O. laevigatus and support that they show no biological effects in the predatory bug. These data help in explaining the compatible use of MACs together with predatory bugs in IPM programs. Keywords: Orius laevigatus, selectivity, diacylhydrazine insecticides, ecdysone receptor, homology modelling, docking studies.

User-centric need-driven affect modeling for spoken conversational agents: design and evaluation

It is easy to get frustrated at spoken conversational agents (SCAs), perhaps because they seem to be callous. By and large, the quality of human-computer interaction is affected due to the inability of the SCAs to recognise and adapt to user emotional state. Now with the mass appeal of artificially-mediated communication, there has been an increasing need for SCAs to be socially and emotionally intelligent, that is, to infer and adapt to their human interlocutors’ emotions on the fly, in order to ascertain an affective, empathetic and naturalistic interaction. An enhanced quality of interaction would reduce users’ frustrations and consequently increase their satisfactions. These reasons have motivated the development of SCAs towards including socio-emotional elements, turning them into affective and socially-sensitive interfaces. One barrier to the creation of such interfaces has been the lack of methods for modelling emotions in a task-independent environment. Most emotion models for spoken dialog systems are task-dependent and thus cannot be used “as-is” in different applications. This Thesis focuses on improving this, in which it concerns computational modeling of emotion, personality and their interrelationship for task-independent autonomous SCAs. The generation of emotion is driven by needs, inspired by human’s motivational systems. The work in this Thesis is organised in three stages, each one with its own contribution. The first stage involved defining, integrating and quantifying the psychological-based motivational and emotional models sourced from. Later these were transformed into a computational model by implementing them into software entities. The computational model was then incorporated and put to test with an existing SCA host, a HiFi-control agent. The second stage concerned automatic prediction of affect, which has been the main challenge towards the greater aim of infusing social intelligence into the HiFi agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. In this stage, we attempted to address part of this challenge by considering the roles of user satisfaction ratings and conversational/dialog features as the respective target and predictors in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. The final stage concerned the evaluation of the emotional model through the HiFi agent. A series of user studies with 70 subjects were conducted in a real-time environment, each in a different phase and with its own conditions. All the studies involved the comparisons between the baseline non-modified and the modified agent. The findings have gone some way towards enhancing our understanding of the utility of emotion in spoken dialog systems in several ways; first, an SCA should not express its emotions blindly, albeit positive. Rather, it should adapt its emotions to user states. Second, low performance in an SCA may be compensated by the exploitation of emotion. Third, the expression of emotion through the exploitation of prosody could better improve users’ perceptions of an SCA compared to exploiting emotions through just lexical contents. Taken together, these findings not only support the success of the emotional model, but also provide substantial evidences with respect to the benefits of adding emotion in an SCA, especially in mitigating users’ frustrations and ultimately improving their satisfactions. Resumen Es relativamente fácil experimentar cierta frustración al interaccionar con agentes conversacionales (Spoken Conversational Agents, SCA), a menudo porque parecen ser un poco insensibles. En general, la calidad de la interacción persona-agente se ve en cierto modo afectada por la incapacidad de los SCAs para identificar y adaptarse al estado emocional de sus usuarios. Actualmente, y debido al creciente atractivo e interés de dichos agentes, surge la necesidad de hacer de los SCAs unos seres cada vez más sociales y emocionalmente inteligentes, es decir, con capacidad para inferir y adaptarse a las emociones de sus interlocutores humanos sobre la marcha, de modo que la interacción resulte más afectiva, empática y, en definitiva, natural. Una interacción mejorada en este sentido permitiría reducir la posible frustración de los usuarios y, en consecuencia, mejorar el nivel de satisfacción alcanzado por los mismos. Estos argumentos justifican y motivan el desarrollo de nuevos SCAs con capacidades socio-emocionales, dotados de interfaces afectivas y socialmente sensibles. Una de las barreras para la creación de tales interfaces ha sido la falta de métodos de modelado de emociones en entornos independientes de tarea. La mayoría de los modelos emocionales empleados por los sistemas de diálogo hablado actuales son dependientes de tarea y, por tanto, no pueden utilizarse "tal cual" en diferentes dominios o aplicaciones. Esta tesis se centra precisamente en la mejora de este aspecto, la definición de modelos computacionales de las emociones, la personalidad y su interrelación para SCAs autónomos e independientes de tarea. Inspirada en los sistemas motivacionales humanos en el ámbito de la psicología, la tesis propone un modelo de generación/producción de la emoción basado en necesidades. El trabajo realizado en la presente tesis está organizado en tres etapas diferenciadas, cada una con su propia contribución. La primera etapa incluyó la definición, integración y cuantificación de los modelos motivacionales de partida y de los modelos emocionales derivados a partir de éstos. Posteriormente, dichos modelos emocionales fueron plasmados en un modelo computacional mediante su implementación software. Este modelo computacional fue incorporado y probado en un SCA anfitrión ya existente, un agente con capacidad para controlar un equipo HiFi, de alta fidelidad. La segunda etapa se orientó hacia el reconocimiento automático de la emoción, aspecto que ha constituido el principal desafío en relación al objetivo mayor de infundir inteligencia social en el agente HiFi. En los últimos años, los estudios sobre reconocimiento de emociones a partir de la voz han pasado de emplear datos actuados a usar datos reales en los que la presencia u observación de emociones se produce de una manera mucho más sutil. El reconocimiento de emociones bajo estas condiciones resulta mucho más complicado y esta dificultad se pone de manifiesto en tareas tales como el etiquetado y el aprendizaje automático. En esta etapa, se abordó el problema del reconocimiento de las emociones del usuario a partir de características o métricas derivadas del propio diálogo usuario-agente. Gracias a dichas métricas, empleadas como predictores o indicadores del grado o nivel de satisfacción alcanzado por el usuario, fue posible discriminar entre satisfacción y frustración, las dos emociones prevalentes durante la interacción usuario-agente. La etapa final corresponde fundamentalmente a la evaluación del modelo emocional por medio del agente Hifi. Con ese propósito se llevó a cabo una serie de estudios con usuarios reales, 70 sujetos, interaccionando con diferentes versiones del agente Hifi en tiempo real, cada uno en una fase diferente y con sus propias características o capacidades emocionales. En particular, todos los estudios realizados han profundizado en la comparación entre una versión de referencia del agente no dotada de ningún comportamiento o característica emocional, y una versión del agente modificada convenientemente con el modelo emocional propuesto. Los resultados obtenidos nos han permitido comprender y valorar mejor la utilidad de las emociones en los sistemas de diálogo hablado. Dicha utilidad depende de varios aspectos. En primer lugar, un SCA no debe expresar sus emociones a ciegas o arbitrariamente, incluso aunque éstas sean positivas. Más bien, debe adaptar sus emociones a los diferentes estados de los usuarios. En segundo lugar, un funcionamiento relativamente pobre por parte de un SCA podría compensarse, en cierto modo, dotando al SCA de comportamiento y capacidades emocionales. En tercer lugar, aprovechar la prosodia como vehículo para expresar las emociones, de manera complementaria al empleo de mensajes con un contenido emocional específico tanto desde el punto de vista léxico como semántico, ayuda a mejorar la percepción por parte de los usuarios de un SCA. Tomados en conjunto, los resultados alcanzados no sólo confirman el éxito del modelo emocional, sino xv que constituyen además una evidencia decisiva con respecto a los beneficios de incorporar emociones en un SCA, especialmente en cuanto a reducir el nivel de frustración de los usuarios y, en última instancia, mejorar su satisfacción.

Analysis and Quality Control from ARIMA Modeling

In this paper, we use ARIMA modelling to estimate a set of characteristics of a short-term indicator (for example, the index of industrial production), as trends, seasonal variations, cyclical oscillations, unpredictability, deterministic effects (as a strike), etc. Thus for each sector and product (more than 1000), we construct a vector of values corresponding to the above-mentioned characteristics, that can be used for data editing.

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Extended description of tunnel junctions for distributed modeling of concentrator multi-junction solar cells

One of the key components of highly efficient multi-junction concentrator solar cells is the tunnel junction interconnection. In this paper, an improved 3D distributed model is presented that considers real operation regimes in a tunnel junction. This advanced model is able to accurately simulate the operation of the solar cell at high concentraions at which the photogenerated current surpasses the peak current of the tunnel junctionl Simulations of dual-junction solar cells were carried out with the improved model to illustrate its capabilities and the results have been correlated with experimental data reported in the literature. These simulations show that under certain circumstances, the solar cells short circuit current may be slightly higher than the tunnel junction peak current without showing the characteristic dip in the J-V curve. This behavior is caused by the lateral current spreading toward dark regions, which occurs through the anode/p-barrier of the tunnel junction.

Design and modeling of the multi-agent robotic system: SMART

This article presents the design, kinematic model and communication architecture for the multi-agent robotic system called SMART. The philosophy behind this kind of system requires the communication architecture to contemplate the concurrence of the whole system. The proposed architecture combines different communication technologies (TCP/IP and Bluetooth) under one protocol designed for the cooperation among agents and other elements of the system such as IP-Cameras, image processing library, path planner, user Interface, control block and data block. The high level control is modeled by Work-Flow Petri nets and implemented in C++ and C♯♯. Experimental results show the performance of the designed architecture.

Ionoluminescence on α-quartz: mechanisms and modeling

Ionoluminescence of α - quartz exhibits two dominant emission bands peaking at 1.9 eV. (NBOHCs) and 2.7 eV (STEs. The evolution of the red emission yield does not show a correlation with the concentrations of neither the NBOHC nor with that of other color centers. The blue emission yield closely follows the amorphization kinetics independently measured by RBS/C spectrometry. A simple theoretical model has been proposed; it assumes that the formation and recombination of STEs are the primary event and both, the light emissions and the lattice structural damage are a consequence this phenomenon. The model leads to several simple mathematical equations that can be used to simulate the IL yields and provide a reasonable fit to experimental kinetic data.

Modeling the evolution of item rating networks using time-domain preferential attachment

The understanding of the structure and dynamics of the intricate network of connections among people that consumes products through Internet appears as an extremely useful asset in order to study emergent properties related to social behavior. This knowledge could be useful, for example, to improve the performance of personal recommendation algorithms. In this contribution, we analyzed five-year records of movie-rating transactions provided by Netflix, a movie rental platform where users rate movies from an online catalog. This dataset can be studied as a bipartite user-item network whose structure evolves in time. Even though several topological properties from subsets of this bipartite network have been reported with a model that combines random and preferential attachment mechanisms [Beguerisse Díaz et al., 2010], there are still many aspects worth to be explored, as they are connected to relevant phenomena underlying the evolution of the network. In this work, we test the hypothesis that bursty human behavior is essential in order to describe how a bipartite user-item network evolves in time. To that end, we propose a novel model that combines, for user nodes, a network growth prescription based on a preferential attachment mechanism acting not only in the topological domain (i.e. based on node degrees) but also in time domain. In the case of items, the model mixes degree preferential attachment and random selection. With these ingredients, the model is not only able to reproduce the asymptotic degree distribution, but also shows an excellent agreement with the Netflix data in several time-dependent topological properties.

Multilingual Variation in the context of Linked Data

In this paper we present a revisited classification of term variation in the light of the Linked Data initiative. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web with the idea of transforming it into a global graph. One of the crucial steps of this initiative is the linking step, in which datasets in one or more languages need to be linked or connected with one another. We claim that the linking process would be facilitated if datasets are enriched with lexical and terminological information. Being that the final aim, we propose a classification of lexical, terminological and semantic variants that will become part of a model of linguistic descriptions that is currently being proposed within the framework of the W3C Ontology-Lexica Community Group to enrich ontologies and Linked Data vocabularies. Examples of modeling solutions of the different types of variants are also provided.

Emission inventories and modeling requirements for the development of air quality plans. Application to Madrid (Spain)

Modeling is an essential tool for the development of atmospheric emission abatement measures and air quality plans. Most often these plans are related to urban environments with high emission density and population exposure. However, air quality modeling in urban areas is a rather challenging task. As environmental standards become more stringent (e.g. European Directive 2008/50/EC), more reliable and sophisticated modeling tools are needed to simulate measures and plans that may effectively tackle air quality exceedances, common in large urban areas across Europe, particularly for NO2. This also implies that emission inventories must satisfy a number of conditions such as consistency across the spatial scales involved in the analysis, consistency with the emission inventories used for regulatory purposes and versatility to match the requirements of different air quality and emission projection models. This study reports the modeling activities carried out in Madrid (Spain) highlighting the atmospheric emission inventory development and preparation as an illustrative example of the combination of models and data needed to develop a consistent air quality plan at urban level. These included a series of source apportionment studies to define contributions from the international, national, regional and local sources in order to understand to what extent local authorities can enforce meaningful abatement measures. Moreover, source apportionment studies were conducted in order to define contributions from different sectors and to understand the maximum feasible air quality improvement that can be achieved by reducing emissions from those sectors, thus targeting emission reduction policies to the most relevant activities. Finally, an emission scenario reflecting the effect of such policies was developed and the associated air quality was modeled.

Development of a wake model for wind farms based on an open source CFD solver. Strategies on parabolization and turbulence modeling

Wake effect represents one of the most important aspects to be analyzed at the engineering phase of every wind farm since it supposes an important power deficit and an increase of turbulence levels with the consequent decrease of the lifetime. It depends on the wind farm design, wind turbine type and the atmospheric conditions prevailing at the site. Traditionally industry has used analytical models, quick and robust, which allow carry out at the preliminary stages wind farm engineering in a flexible way. However, new models based on Computational Fluid Dynamics (CFD) are needed. These models must increase the accuracy of the output variables avoiding at the same time an increase in the computational time. Among them, the elliptic models based on the actuator disk technique have reached an extended use during the last years. These models present three important problems in case of being used by default for the solution of large wind farms: the estimation of the reference wind speed upstream of each rotor disk, turbulence modeling and computational time. In order to minimize the consequence of these problems, this PhD Thesis proposes solutions implemented under the open source CFD solver OpenFOAM and adapted for each type of site: a correction on the reference wind speed for the general elliptic models, the semi-parabollic model for large offshore wind farms and the hybrid model for wind farms in complex terrain. All the models are validated in terms of power ratios by means of experimental data derived from real operating wind farms.

Modeling impact assessment of transport policies through a multiregional input-output integrated approach

El objetivo de esta investigación es desarrollar una metodología para estimar los potenciales impactos económicos y de transporte generados por la aplicación de políticas en el sector transporte. Los departamentos de transporte y otras instituciones gubernamentales relacionadas se encuentran interesadas en estos análisis debido a que son presentados comúnmente de forma errónea por la insuficiencia de datos o por la falta de metodologías adecuadas. La presente investigación tiene por objeto llenar este vacío haciendo un análisis exhaustivo de las técnicas disponibles que coincidan con ese propósito. Se ha realizado un análisis que ha identificado las diferencias cuando son aplicados para la valoración de los beneficios para el usuario o para otros efectos como aspectos sociales. Como resultado de ello, esta investigación ofrece un enfoque integrado que incluye un modelo Input-Output de múltiples regiones basado en la utilidad aleatoria (RUBMRIO), y un modelo de red de transporte por carretera. Este modelo permite la reproducción con mayor detalle y realismo del transporte de mercancías que por medio de su estructura sectorial identifica los vínculos de las compras y ventas inter-industriales dentro de un país utilizando los servicios del transporte de mercancías. Por esta razón, el modelo integrado es aplicable a diversas políticas de transporte. En efecto, el enfoque se ha aplicado para estudiar los efectos macroeconómicos regionales de la implementación de dos políticas diferentes en el sistema de transporte de mercancías de España, tales como la tarificación basada en la distancia recorrida por vehículo-kilómetro (€/km) aplicada a los vehículos del transporte de mercancías, y para la introducción de vehículos más largos y pesados de mercancías en la red de carreteras de España. El enfoque metodológico se ha evaluado caso por caso teniendo en cuenta una selección de la red de carreteras que unen las capitales de las regiones españolas. También se ha tenido en cuenta una dimensión económica a través de una tabla Input-Output de múltiples regiones (MRIO) y la base de datos de conteo de tráfico existente para realizar la validación del modelo. El enfoque integrado reproduce las condiciones de comercio observadas entre las regiones usando el sistema de transporte de mercancías por carretera, y que permite por comparación con los escenarios de políticas, determinar las contribuciones a los cambios distributivos y generativos. Así pues, el análisis estima los impactos económicos en cualquier región considerando los cambios en el Producto Interno Bruto (PIB) y el empleo. El enfoque identifica los cambios en el sistema de transporte a través de todos los caminos de la red de transporte a través de las medidas de efectividad (MOEs). Los resultados presentados en esta investigación proporcionan evidencia sustancial de que en la evaluación de las políticas de transporte, es necesario establecer un vínculo entre la estructura económica de las regiones y de los servicios de transporte. Los análisis muestran que para la mayoría de las regiones del país, los cambios son evidentes para el PIB y el empleo, ya que el comercio se fomenta o se inhibe. El enfoque muestra cómo el tráfico se desvía en ambas políticas, y también determina detalles de las emisiones de contaminantes en los dos escenarios. Además, las políticas de fijación de precios o de regulación de los sistemas de transporte de mercancías por carretera dirigidas a los productores y consumidores en las regiones promoverán transformaciones regionales afectando todo el país, y esto conduce a conclusiones diferentes. Así mismo, este enfoque integrado podría ser útil para evaluar otras políticas y otros países en todo el mundo. The purpose of this research is to develop a methodological approach aimed at assessing the potential economic and transportation impacts of transport policies. Transportation departments and other related government parties are interested in such analysis because it is commonly misrepresented for the insufficiency of data and suitable methodologies available. This research is directed at filling this gap by making a comprehensive analysis of the available techniques that match with that purpose. The differences when they are applied for the valuation of user benefits or for other impacts as social matters have been identified. As a result, this research presents an integrated approach which includes both a random utility-based multiregional Input-Output model (RUBMRIO), and a road transport network model. This model accounts for freight transport with more detail and realism because its commodity-based structure traces the linkages of inter-industry purchases and sales that use freight services within a given country. For this reason, the integrated model is applicable to various transport policies. In fact, the approach is applied to study the regional macroeconomic effects of implementing two different policies in the freight transport system of Spain, such as a distance-based charge in vehicle-kilometer (€/km) for Heavy Goods Vehicles (HGVs), and the introduction of Longer and Heavier Vehicles (LHVs) in the road network of Spain. The methodological approach has been evaluated on a case by case basis considering a selected road network of highways linking the capitals of the Spanish regions. It has also considered an economic dimension through a Multiregional Input Output Table (MRIO) and the existing traffic count database used in the model validation. The integrated approach replicates observed conditions of trade among regions using road freight transport systems that determine contributions to distributional and generative changes by comparison with policy scenarios. Therefore, the model estimates economic impacts in any given area by considering changes in Gross Domestic Product (GDP), employment (jobs), and in the transportation system across all paths of the transport network considering Measures of effectiveness (MOEs). The results presented in this research provide substantive evidence that in the assessment of transport policies it is necessary to establish a link between the economic structure of regions and the transportation services. The analysis shows that for most regions in the country, GDP and employment changes are noticeable when trade is encouraged or discouraged. This approach shows how traffic is diverted in both policies, and also provides details of the pollutant emissions in both scenarios. Furthermore, policies, such as pricing or regulation of road freight transportation systems, directed to producers and consumers in regions will promote different regional transformations across the country, and this lead to different conclusions. In addition, this integrated approach could be useful to assess other policies and countries worldwide.

Knowledge representation tool for cognitiveprocesses modeling

In the last decades, neuropsychological theories tend to consider cognitive functions as a result of the whole brainwork and not as individual local areas of its cortex. Studies based on neuroimaging techniques have increased in the last years, promoting an exponential growth of the body of knowledge about relations between cognitive functions and brain structures [1]. However, so fast evolution make complicated to integrate them in verifiable theories and, even more, translated in to cognitive rehabilitation. The aim of this research work is to develop a cognitive process-modeling tool. The purpose of this system is, in the first term, to represent multidimensional data, from structural and functional connectivity, neuroimaging, data from lesion studies and derived data from clinical intervention [2][3]. This will allow to identify consolidated knowledge, hypothesis, experimental designs, new data from ongoing studies and emerging results from clinical interventions. In the second term, we pursuit to use Artificial Intelligence to assist in decision making allowing to advance towards evidence based and personalized treatments in cognitive rehabilitation. This work presents the knowledge base design of the knowledge representation tool. It is compound of two different taxonomies (structure and function) and a set of tags linking both taxonomies at different levels of structural and functional organization. The remainder of the abstract is organized as follows: Section 2 presents the web application used for gathering necessary information for generating the knowledge base, Section 3 describes knowledge base structure and finally Section 4 expounds reached conclusions.

Multilingual variation in the context of Linked Data

In this paper we present a revisited classification of term variation in the light of the Linked Data initiative. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web with the idea of transforming it into a global graph. One of the crucial steps of this initiative is the linking step, in which datasets in one or more languages need to be linked or connected with one another. We claim that the linking process would be facilitated if datasets are enriched with lexical and terminological information. Being that the final aim, we propose a classification of lexical, terminological and semantic variants that will become part of a model of linguistic descriptions that is currently being proposed within the framework of the W3C Ontology- Lexica Community Group to enrich ontologies and Linked Data vocabularies. Examples of modeling solutions of the different types of variants are also provided.

On the leakage-power modeling for optimal server operation

Leakage power consumption is a com- ponent of the total power consumption in data cen- ters that is not traditionally considered in the set- point temperature of the room. However, the effect of this power component, increased with temperature, can determine the savings associated with the careful management of the cooling system, as well as the re- liability of the system. The work presented in this paper detects the need of addressing leakage power in order to achieve substantial savings in the energy consumption of servers. In particular, our work shows that, by a careful detection and management of two working regions (low and high impact of thermal- dependent leakage), energy consumption of the data- center can be optimized by a reduction of the cooling budget.