Language knowledge and earnings in Catalonia

In this paper we are aimed to investigate the relationship between Catalan knowledge and individual earnings in Catalonia. Using data from 2006, we find a positive earning return to Catalan proficiency; however, when accounting for self-selection into Catalan knowledge, we find a higher language return (20% of extra earnings), suggesting that individuals who are more prone to know Catalan are also less remunerated than others (negative selection effect). Moreover, we also find important complementarities between language knowledge and completed education, which means that only more educated individuals benefit from Catalan knowledge.

Language knowledge and earnings in Catalonia

This paper investigates the economic value of Catalan knowledge for national and foreign first- and second-generation immigrants in Catalonia. Specifically, drawing on data from the “Survey on Living Conditions and Habits of the Catalan Population (2006)”, we want to quantify the expected earnings differential between individuals who are proficient in Catalan and those who are not, taking into account the potential endogeneity between knowledge of Catalan and earnings. The results indicate the existence of a positive return to knowledge of Catalan, with a 7.5% increase in earnings estimated by OLS; however, when we account for the presence of endogeneity, monthly earnings are around 18% higher for individuals who are able to speak and write Catalan. However, we also find that language and education are complementary inputs for generating earnings in Catalonia, given that knowledge of Catalan increases monthly earnings only for more educated individuals.

Recerca versus docència en una experiència d'internalització dels estudis de Geografia a la Universitat de Girona: The faces of landscape, Catalonia and North Karelia = Research versus teaching in one experience of internationalization of the Geography degree studies in the University of Girona: The faces of landscape, Catalonia and North Karelia

Un dels reptes cabdals de la Universitat és enllaçar l’experiència de recerca amb la docència, així com promoure la internacionalització dels estudis, especialment a escala europea, tenint present que ambdues poden actuar com a catalitzadores de la millora de la qualitat docent. Una de les fórmules d’internacionalització és la realització d’assignatures compartides entre universitats de diferents països, fet que suposa l’oportunitat d’implementar noves metodologies docents. En aquesta comunicació es presenta una experiència en aquesta línia desenvolupada entre la Universitat de Girona i la Universitat de Joensuu (Finlàndia) en el marc dels estudis de Geografia amb la realització de l’assignatura 'The faces of landscape: Catalonia and North Karelia'. Aquesta es desenvolupa al llarg de dues setmanes intensives, una en cadascuna de les Universitats. L’objectiu és presentar i analitzar diferents significats del concepte paisatge aportant també metodologies d’estudi tant dels aspectes físics i ecològics com culturals que s’hi poden vincular i que són les que empren els grups de recerca dels professors responsables de l’assignatura. Aquesta part teòrica es completa amb una presentació de les característiques i dinàmiques pròpies dels paisatges finlandesos i catalans i una sortida de camp. Per a la part pràctica es constitueixen grups d’estudi multinacionals que treballen a escala local algun dels aspectes en els dos països, es comparen i es realitza una presentació i defensa davant del conjunt d’estudiants i professorat. La llengua vehicular de l’assignatura és l’anglès.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Decision-theoretic analysis of forensic sampling criteria using Bayesian decision networks

Sampling issues represent a topic of ongoing interest to the forensic science community essentially because of their crucial role in laboratory planning and working protocols. For this purpose, forensic literature described thorough (Bayesian) probabilistic sampling approaches. These are now widely implemented in practice. They allow, for instance, to obtain probability statements that parameters of interest (e.g., the proportion of a seizure of items that present particular features, such as an illegal substance) satisfy particular criteria (e.g., a threshold or an otherwise limiting value). Currently, there are many approaches that allow one to derive probability statements relating to a population proportion, but questions on how a forensic decision maker - typically a client of a forensic examination or a scientist acting on behalf of a client - ought actually to decide about a proportion or a sample size, remained largely unexplored to date. The research presented here intends to address methodology from decision theory that may help to cope usefully with the wide range of sampling issues typically encountered in forensic science applications. The procedures explored in this paper enable scientists to address a variety of concepts such as the (net) value of sample information, the (expected) value of sample information or the (expected) decision loss. All of these aspects directly relate to questions that are regularly encountered in casework. Besides probability theory and Bayesian inference, the proposed approach requires some additional elements from decision theory that may increase the efforts needed for practical implementation. In view of this challenge, the present paper will emphasise the merits of graphical modelling concepts, such as decision trees and Bayesian decision networks. These can support forensic scientists in applying the methodology in practice. How this may be achieved is illustrated with several examples. The graphical devices invoked here also serve the purpose of supporting the discussion of the similarities, differences and complementary aspects of existing Bayesian probabilistic sampling criteria and the decision-theoretic approach proposed throughout this paper.

"Periodic crises": Clément Juglar between theories of crises and theories of business cycles

Business cycle theory is normally described as having evolved out of a previous tradition of writers focusing exclusively on crises. In this account, the turning point is seen as residing in Clément Juglar's contribution on commercial crises and their periodicity. It is well known that the champion of this view is Schumpeter, who propagated it on several occasions. The same author, however, pointed to a number of other writers who, before and at the same time as Juglar, stressed one or another of the aspects for which Juglar is credited primacy, including the recognition of periodicity and the identification of endogenous elements enabling the recognition of crises as a self-generating phenomenon. There is indeed a vast literature, both primary and secondary, relating to the debates on crises and fluctuations around the middle of the nineteenth century, from which it is apparent that Juglar's book Des Crises Commerciales et de leur Retour Périodique en France, en Angleterre et aux États-Unis (originally published in 1862 and very much revised and enlarged in 1889) did not come out of the blue but was one of the products of an intellectual climate inducing the thinking of crises not as unrelated events but as part of a more complex phenomenon consisting of recurring crises related to the development of the commercial world - an interpretation corroborated by the almost regular occurrence of crises at about 10-year intervals.

Predictive and distributed routing balancing (PR-DRB): high speed interconnection networks

Current parallel applications running on clusters require the use of an interconnection network to perform communications among all computing nodes available. Imbalance of communications can produce network congestion, reducing throughput and increasing latency, degrading the overall system performance. On the other hand, parallel applications running on these networks posses representative stages which allow their characterization, as well as repetitive behavior that can be identified on the basis of this characterization. This work presents the Predictive and Distributed Routing Balancing (PR-DRB), a new method developed to gradually control network congestion, based on paths expansion, traffic distribution and effective traffic load, in order to maintain low latency values. PR-DRB monitors messages latencies on intermediate routers, makes decisions about alternative paths and record communication pattern information encountered during congestion situation. Based on the concept of applications repetitiveness, best solution recorded are reapplied when saved communication pattern re-appears. Traffic congestion experiments were conducted in order to evaluate the performance of the method, and improvements were observed.

Suivi de l'adhésion thérapeutique des patients hypertendus ou dyslipi- démiques par les cercles de qualité médecins-pharmaciens [Monitoring drug adherence for hypertensive and dyslipidemic patients in networks of community-based pharmacists and physicians].

Patient adherence is often poor for hypertension and dyslipidaemia. A monitoring of drug adherence might improve these risk factors control, but little is known in ambulatory care. We conducted a randomised controlled study in networks of community-based pharmacists and physicians in the canton of Fribourg to examine whether monitoring drug adherence with an electronic monitor (MEMS) would improve risk factor control among treated, but uncontrolled hypertensive and dyslipidemic patients. The results indicate that MEMS achieve a better blood pressure control and lipid profile, although its implementation requires considerable resources. The study also shows the value of collaboration between physicians and pharmacists in the field of patient adherence to improve ambulatory care of patients with cardiovascular risk factors.