Carlos Angel Iglesias

Horizontal dynamics of zooplankton in subtropical Lake Blanca (Uruguay) hosting multiple zooplankton predators and aquatic plant refuges

In the subtropics, the effects of macrophytes on trophic interactions are more complex than in temperate lakes. Fish, particularly the smallest species and individuals, aggregate in high numbers in the vegetation, and a strong predation pressure on Zooplankton by shrimps and invertebrates, such as Chaoborus, can occur in these systems. We studied seasonal and diel changes in Zooplankton and their potential predators (both fish and invertebrates) and physical and chemical characteristics among open water and vegetated habitats (emergent and submerged plants (SP)), in the subtropical Lake Bianca (34°54’ S; 54°50’ W), a shallow system with an extensive and complex littoral area and high abundance of vertebrate and invertebrate predators on Zooplankton. We found differential horizontal distribution of some zooplankton species under the scenario of high abundance of small omnivorous-planktivorous fish and Chaoborus, especially in the seasons with intermediate catch per unit effort of fish. We found indications of a diel horizontal migration (DHM) opposite than described for temperate systems, as the two main cladocerans Bosmina longirostris and Diaphanosoma birgei were found in higher densities in the submerged plant beds during night, in spring and autumn respectively. Although we need experiments to prove DHM, Chaoborus seemed to be the main trigger of the apparent DHM, followed by small omnivorous fish. During summer no spatial differences were found likely because of high densities of fish in all habitats. In absence of piscivorous fish, the distribution of fish Jenynsia multidentata seemed to be conditioned by food availability and by predation risk of waterfowl. The refuge capacity of aquatic plants for Zooplankton in subtropical systems seems weak and with consequent weak or no cascading effects on water transparency, as under very high fish and invertebrate densities (summer) the refuge for Zooplankton was lost.

Environmental Warming in Shallow Lakes: A Review of Potential Changes in Community Structure as Evidenced from Space-for-Time Substitution Approaches

Abstract Shallow lakes, one of the most widespread water bodies in the world landscape, are very sensitive to climate change. Several theories predict changes in community traits, relevant for ecosystem functioning, with higher temperature. The space-for-time substitution approach (SFTS) provides one of the most plausible empirical evaluations for these theories, helping to elucidate the long-term consequences of changes in climate. Here, we reviewed the changes at the community level for the main freshwater taxa and assemblages (i.e. fishes, macroinvertebrates, zooplankton, macrophytes, phytoplankton, periphyton and bacterioplankton), under different climates. We analyzed data obtained from latitudinal and altitudinal gradients and cross-comparison (i.e. SFTS) studies, supplemented by an analysis of published geographically dispersed data for those communities or traits not covered in the SFTS literature. We found only partial empirical evidence supporting the theoretical predictions. The prediction of higher richness at warmer locations was supported for fishes, phytoplankton and periphyton, while the opposite was true for macroinvertebrates and zooplankton. With decreasing latitude, the biomass of cladoceran zooplankton and periphyton and the density of zooplankton and macroinvertebrates declined (opposite for fishes for both biomass and density variables). Fishes and cladoceran zooplankton showed the expected reduction in body size with higher temperature. Life history changes in fish and zooplankton and stronger trophic interactions at intermediate positions in the food web (fish predation on zooplankton and macroinvertebrates) were evident, but also a weaker grazing pressure of zooplankton on phytoplankton occurred with increasing temperatures. The potential impacts of lake productivity, fish predation and other factors, such as salinity, were often stronger than those of temperature itself. Additionally, shallow lakes may shift between alternative states, complicating theoretical predictions of warming effects. SFTS and meta-analyses approaches have their shortcomings, but in combination with experimental and model studies that help reveal mechanisms, the “field situation” is indispensable to understand the potential effects of warming.

Multiagent-based control systems: A hybrid approach to distributed process control☆

Abstract This paper presents a general architecture and a platform developed to implement distributed applications as a set of cooperating intelligent agents. It also shows how this architecture has been used to implement a distributed control system for a complex process: the economic control of a fossil-fuel fired power plant. Agents in this application encapsulate different distributed hardware/ software entities: neural and fuzzy controllers, a data-acquisition system, presentation manager, etc. These agents are defined in ADL (Agent Description Language), a high-level specification language, and interchange data/knowledge through service requests using a common knowledge-representation language.

Classifying and comparing community innovation in Idea Management Systems

The Idea Management Systems are a tool for collecting ideas for innovation from large communities. One of the problems of those systems is the difficulty to accurately depict the distinctive features of ideas in a rapid manner and use them for judgement of proposed innovations. Our research aims to solve this problem by introducing annotation of ideas with a domain independent taxonomy that describes various characteristics of ideas. The findings of our study show that such annotations can be successfully transformed into new metrics that allow the comparison of ideas with similar successfulness as the metrics already used in Idea Management Systems but in greater detail. The presented results are based on experiments with over 50,000 ideas gathered from case studies of four different organisations: Dell, Starbucks, Cisco and Canonical.

The road from community ideas to organisational innovation: a life cycle survey of idea management systems

This paper introduces a new emerging software component, the idea management system, which helps to gather, organise, select and manage the innovative ideas provided by the communities gathered around organisations or enterprises. We define the notion of the idea life cycle, which provides a framework for characterising tools and techniques that drive the evolution of community submitted data inside idea management systems. Furthermore, we show the dependencies between the community-created information and the enterprise processes that are a result of using idea management systems and point out the possible benefits.

MIX: a general purpose multiagent architecture

The MIX multiagent architecture has been conceived as a general purpose distributed framework for the cooperation of multiple heterogeneous agents. This architecture, starting from previous work in our group on multiagent systems, has been redesigned and implemented within a research project investigating a particular class of hybrid systems: those integrated by connectionist and symbolic components. This paper describes in some detail the principal concepts of the architecture: the network model and the agent model. Around these models, a set of languages and tools have been developed. In particular, an Agent Description Language (MIX-ADL) has been designed to specify agents declaratively in a hierarchy of classes.

Enhancing deep learning sentiment analysis with ensemble techniques in social applications

A taxonomy that classifies ensemble models in the literature is presented.Surface and deep features integration is explored to improve classification.Several ensembles of classifiers and features are proposed and evaluated.Performance of the proposed models is evaluated on several sentiment datasets. Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods. These long-established approaches can yield strong baselines, and their predictive capabilities can be used in conjunction with the arising deep learning methods. In this paper we seek to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features. The contributions of this paper are sixfold. First, we develop a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm. This classifier serves as a baseline to compare to subsequent results. Second, we propose two ensemble techniques which aggregate our baseline classifier with other surface classifiers widely used in Sentiment Analysis. Third, we also propose two models for combining both surface and deep features to merge information from several sources. Fourth, we introduce a taxonomy for classifying the different models found in the literature, as well as the ones we propose. Fifth, we conduct several experiments to compare the performance of these models with the deep learning baseline. For this, we use seven public datasets that were extracted from the microblogging and movie reviews domain. Finally, as a result, a statistical study confirms that the performance of these proposed models surpasses that of our original baseline on F1-Score.

Disasters2.0: application of web 2.0 technologies in emergency situations

This article presents a social approach for disaster management based on a social portal called Disasters2.0, which provides facilities for integrating and sharing user-generated information about disasters. The architecture of Disasters2.0 is designed following Representational State Transfer (REST) principles and integrates external mashups such as Google Maps. This architecture has been consumed with different clients, including a mobile client, a multiagent system for assisting the decentralised management of disasters and an expert system for automatically assigning resources to disasters. As a result, the platform allows seamless collaboration of humans and intelligent agents, and provides a novel web 2.0 approach for multiagent and disaster management research.

Seasonal and diel changes in fish activity and potential cascading effects in subtropical shallow lakes with different water transparency

Fish play a key role in the functioning of shallow lakes. Simultaneously, fish are affected by physical in-lake factors, such as temperature and water transparency, with potential changes in their cascading effects on other communities. Here, we analysed the fish community structure and fish activity in four subtropical shallow lakes, varying in trophic state and water transparency, to assess changes promoted by temperature (i.e. summer and winter) and the light regime (i.e. day and night). We used a passive method (gillnets) during the day- and at night-time to detect changes in fish activity, but also sampled the littoral zone (during night) by point sample electrofishing to obtain a better description of the fish assemblage and habitat use. We observed different fish assemblages in the two seasons in all lakes. We captured more fish species and also obtained higher numbers (CPUE with nets) in summer. Contrary to our expectations, the visually oriented Characiformes were the most captured fish regardless of water transparency, at both day-time and night-time. We also found higher fish CPUE at night-time in all lakes. However, the differences between night and day decreased with decreasing transparency, being lower in the least clear lake, Lake Cisne. The nocturnal increase in fish CPUE (including visually oriented species) suggests that darkness serves as a good refuge for fish in shallow subtropical lakes, even at the likely cost of a lower feeding efficiency during the night. The importance of darkness seems to decrease with decreasing water transparency. We also argue that cascading effects of changes in the activity of piscivorous fish (seasonal changes in piscivores CPUE), when omni-planktivorous fish are indeed affected, may eventually reach the zooplankton, but may not be strong enough to reach the phytoplankton, regardless of water transparency.

Onyx: A Linked Data approach to emotion representation

Abstract Extracting opinions and emotions from text is becoming increasingly important, especially since the advent of micro-blogging and social networking. Opinion mining is particularly popular and now gathers many public services, datasets and lexical resources. Unfortunately, there are few available lexical and semantic resources for emotion recognition that could foster the development of new emotion aware services and applications. The diversity of theories of emotion and the absence of a common vocabulary are two of the main barriers to the development of such resources. This situation motivated the creation of Onyx, a semantic vocabulary of emotions with a focus on lexical resources and emotion analysis services. It follows a linguistic Linked Data approach, it is aligned with the Provenance Ontology, and it has been integrated with the Lexicon Model for Ontologies (lemon), a popular RDF model for representing lexical entries. This approach also means a new and interesting way to work with different theories of emotion. As part of this work, Onyx has been aligned with EmotionML and WordNet-Affect.