Growth limiting conditions and denitrification govern extent and frequency of volume detachment of biofilms

This study aims at evaluating the mechanisms of biofilm detachment with regard of the physical properties of the biofilm. Biofilms were developed in Couette–Taylor reactor under controlled hydrodynamic conditions and under different environmental growth conditions. Five different conditions were tested and lead to the formation of two aerobic heterotrophic biofilms (aeHB1 and aeHB2), a mixed autotrophic and heterotrophic biofilm (MAHB) and two anoxic heterotrophic biofilms (anHB1 and anHB2). Biofilm detachment was evaluated by monitoring the size of the detached particles (using light-scattering) as well as the biofilm physical properties (using CCD camera and image analysis). Results indicate that volume erosion of large biofilm particles with size ranging from 50 to 500 lm dominated the biomass loss for all biofilms. Surface erosion of small particles with size lower than 20 lm dominates biofilm detachment in number. The extent of the volume detachment events was governed by the size of the biofilm surface heterogeneities (i.e., the absolute biofilm roughness) but never impacted more than 80% of the mean biofilm thickness due to the highly cohesive basal layer. Anoxic biofilms were smoother and thinner than aerobic biofilms and thus associated with the detachment of smaller particles. Our results contradict the simplifying assumption of surface detachment that is considered in many biofilm models and suggest that discrete volume events should be considered

Monitoring optimistic agents

Monitoring is typically included in economic models of crime thanks to a probability of detection, constant across individuals. We build on recent results in psychology to argue that comparative optimism deeply affects this standard relation. To this matter, we introduce an experiment involving proper incentives that allow a measurement of optimism bias. Our experiments support the relevance of so-called comparative optimism in decision under risk. In the context of illegal activities, our results provide a guide into costless devices to undermine fraud, through well-designed information campaigns.Optimism; Risk aversion; Monitoring design; Illegal activity; Experimental economics

Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Les entrepôts de données reposent sur la modélisation multidimensionnelle. A l'aide d'outils OLAP, les décideurs analysent les données à différents niveaux d'agrégation. Il est donc nécessaire de représenter les connaissances d'agrégation dans les modèles conceptuels multidimensionnels, puis de les traduire dans les modèles logiques et physiques. Cependant, les modèles conceptuels multidimensionnels actuels représentent imparfaitement les connaissances d'agrégation, qui (1) ont une structure et une dynamique complexes et (2) sont fortement contextuelles. Afin de prendre en compte les caractéristiques de ces connaissances, nous proposons de les représenter avec des objets (diagrammes de classes UML) et des règles en langage PRR (Production Rule Representation). Les connaissances d'agrégation statiques sont représentées dans les digrammes de classes, tandis que les règles représentent la dynamique (c'est-à-dire comment l'agrégation peut être effectuée en fonction du contexte). Nous présentons les diagrammes de classes, ainsi qu'une typologie et des exemples de règles associées.Agrégation ; Entrepôt de données ; Modèle conceptuel multidimensionnel ; OLAP ; Règle de production ; UML

Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in Production Rule Representation (PRR) language. Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a data warehouse project.Aggregation; Conceptual Multidimensional Model; Data Warehouse; On-line Analytical Processing (OLAP); Production Rule; UML

Short-term memory effects of an auditory biofeedback on isometric force control: Is there a differential effect as a function of transition trials?

The aim of the present study was to investigate memory effects, force accuracy, and variability during constant isometric force at different force levels, using auditory biofeedback. Two types of transition trials were used: a biofeedback-no biofeedback transition trial and a no biofeedback-biofeedback transition trial. The auditory biofeedback produced a low- or high-pitched sound when participants produced an isometric force lower or higher than required, respectively. To achieve this goal, 16 participants were asked to produce and maintain two different isometric forces (30$\pm$5% and 90N$\pm$5%) during 25s. Constant error and standard deviation of the isometric force were calculated. While accuracy and variability of the isometric force varied according to the transition trial, a drift of the force appeared in the no biofeedback condition. This result suggested that the degradation of information about force output in the no biofeedback condition was provided by a leaky memory buffer which was mainly dependent on the sense of effort. Because this drift remained constant whatever the transition used, this memory buffer seemed to be independent of short-term memory processes.Comment: Human Movement Science (2011) epub ahead of prin

Effect of removing small (<150μm) chironomids on inferring temperature in cold lakes

Sieving samples for chironomid analysis with a 150μm mesh was shown to greatly reduce sample preparation time, and use of only larger specimens did not affect chironomid-inferred salinities in African lakes. Here, we tested if this method is suitable for temperature reconstruction in colder lakes at higher latitudes. Removal of specimens 150μm) than those obtained with the full model (all specimens >100μm). General patterns of temperature change were also altered. For Lake 7 on Southampton Island, Canada, a cooling trend was reconstructed with the full Canadian model while the modified Canadian model yielded a warming trend. When only specimens >150μm were used, two to three times more wet sediment was needed to obtain a sufficient number of head capsules. These results indicate that, in cold lakes (mean July/August air temperature ≤11°C), large proportions of head capsules are <150 μm, and sieving the samples in a 150μm mesh leads to altered temperature reconstruction

Representation of Aggregation Knowledge in OLAP Systems

Decision support systems are mainly based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels, using OLAP operators such as roll-up and drill-down. Roll-up operators decrease the details of the measure, aggregating it along the dimension hierarchy. Conversely, drill-down operators increase the details of the measure. As a consequence, dimensions hierarchies play a central role in knowledge representation. More precisely, since aggregation hierarchies are widely used to support data aggregation, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects and rules. Static aggregation knowledge is represented using UML class diagrams, while rules, which represent the dynamics (i.e. how aggregation may be performed depending on context), are represented using the Production Rule Representation (PRR) language. The latter allows us to incorporate dynamic aggregation knowledge. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a decision support system project. In order to illustrate the applicability and benefits of our approach, we exemplify the production rules and present an application scenario