Agricultural water markets: exploring limits and opportunities in Italy and Spain

Agriculture is the main water-using sector in Southern European Countries, such as Spain and Italy. Innovative institutional solutions for reducing water use or increasing its efficiency are pursued by recent legislation concerning water, in particular by the Water Framework Directive (WFD). Even if not explicitly considered by the directive, water markets may be seen as a kind of instrument responding to the guiding principles of the upcoming water regulation. The issue of water markets is very much debated in the water economics literature and particularly in the agricultural water literature. Water markets refer to a mechanism of water allocation based on the exchange of rights on water use. Water markets are proposed and supported by economic theory on the ground that they produce an efficient allocation of water resources. Criticisms to water markets may derive both on the ground of economic efficiency itself (for example due to higher transaction costs and expenditure for wider water transport systems) and on equity considerations (for example the concentration of water on the more efficient farms that would specialise in intensive production, while the others would retain less intensive crops). The objective of this paper is to test to what extent water markets may contribute to the improvement of the efficiency of water use. The analysis is based on a linear programming model applied at basin level, comparing the situation with and without market and including transaction costs proportional to the amount of water exchanged. The model simulates the behaviour of different farm types, derived from cluster analysis on a sample of farms in each area. The model is tested in two areas in Southern Italy and Spain. The paper confirms that water markets have the possibility to improve water use efficiency. However, the exchanges are very much affected by the amount of transaction costs, even for transaction costs relatively low. In the case of Lower Ter, gross margin increase due to markets may be as high as 30% which is rather a considerable amount. Instead, the highest increase in Foggia is only about 10%, a result that may be regarded as hardly relevant. In Foggia the benefits of the water market collapse only when transaction costs are between 0,1 and 0,2 EUR/m3 (that may be regarded as a fairly high amount). On the contrary, Lower Ter is more sensitive to transaction costs and 0,075 EUR/m3 are enough to cause the market to shut down whatever the water quota. When potential improvements occur, an additional issue arises, i.e. the institutional acceptance of market criteria for water allocation purposes. The general attitude in Europe still appears against such a solution. However, the changing economic context (agricultural markets, demographic trends) tend to decrease rigidities about water exchange, particularly among farmers.Water, Irrigation, Agriculture, Water markets, Water policy, Mathematical programming

The potential impact of markets for irrigation water in Italy and Spain: a comparison of two study areas

The viability of irrigated systems in Southern Europe is closely linked to efficient institutional settings and water-allocation mechanisms. A significant, although not widely used, mechanism for water allocation is an intra-sectorial water market. The objective of this paper is to evaluate to what extent water markets may contribute to the improvement of the efficiency of water allocation and to the profitability of irrigated agriculture. The related issues of water allocation among farm types and farm specialisation are also addressed. The analysis is based on a basin-level linear programming model, comparing the situation with and without a market. It includes both fixed and variable transaction costs and estimates their combined effects on market performances. The model is applied in two areas in Southern Italy and Spain, and simulates the behaviour of different farm types, derived from cluster analysis on a sample of farms in each area. The paper confirms that water markets could potentially improve the economic efficiency of water use, in terms of higher profit per hectare, given limited water availability. The potential improvements are associated with a more intense specialisation of farms and are strongly differentiated among farmers, particularly where significant restrictions to water availability occur. This corroborates the expectations of institutional difficulties in implementing water markets. However, the exchanges, and consequently the potential effects of water markets, are heavily affected by the actual level of water availability, as well as the size and the structure (fixed vs. proportional) of transaction costs. The paper calls for a more in-depth analysis of the connections between market performances and institutional settings, as related to the issue of water-agriculture policy design and coordination.water trading, natural resource management, simulation, water management and policy, linear programming, irrigation, Resource /Energy Economics and Policy,

Event detection in location-based social networks

With the advent of social networks and the rise of mobile technologies, users have become ubiquitous sensors capable of monitoring various real-world events in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the White House. However, the deluge of user-generated data on these networks requires intelligent systems capable of identifying and characterizing such events in a comprehensive manner. The data mining community coined the term, event detection , to refer to the task of uncovering emerging patterns in data streams . Nonetheless, most data mining techniques do not reproduce the underlying data generation process, hampering to self-adapt in fast-changing scenarios. Because of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of event detection in Twitter : a data mining technique called Tweet-SCAN and a machine learning technique called Warble. We assess and compare both techniques in a dataset of tweets geo-located in the city of Barcelona during its annual festivities. Last but not least, we present the algorithmic changes and data processing frameworks to scale up the proposed techniques to big data workloads.This work is partially supported by Obra Social “la Caixa”, by the Spanish Ministry of Science and Innovation under contract (TIN2015-65316), by the Severo Ochoa Program (SEV2015-0493), by SGR programs of the Catalan Government (2014-SGR-1051, 2014-SGR-118), Collectiveware (TIN2015-66863-C2-1-R) and BSC/UPC NVIDIA GPU Center of Excellence.We would also like to thank the reviewers for their constructive feedback.Peer ReviewedPostprint (author's final draft

Exploring the topical structure of short text through probability models : from tasks to fundamentals

Recent technological advances have radically changed the way we communicate. Today’s communication has become ubiquitous and it has fostered the need for information that is easier to create, spread and consume. As a consequence, we have experienced the shortening of text messages in mediums ranging from electronic mailing, instant messaging to microblogging. Moreover, the ubiquity and fast-paced nature of these mediums have promoted their use for unthinkable tasks. For instance, reporting real-world events was classically carried out by news reporters, but, nowadays, most interesting events are first disclosed on social networks like Twitter by eyewitness through short text messages. As a result, the exploitation of the thematic content in short text has captured the interest of both research and industry. Topic models are a type of probability models that have traditionally been used to explore this thematic content, a.k.a. topics, in regular text. Most popular topic models fall into the sub-class of LVMs (Latent Variable Models), which include several latent variables at the corpus, document and word levels to summarise the topics at each level. However, classical LVM-based topic models struggle to learn semantically meaningful topics in short text because the lack of co-occurring words within a document hampers the estimation of the local latent variables at the document level. To overcome this limitation, pooling and hierarchical Bayesian strategies that leverage on contextual information have been essential to improve the quality of topics in short text. In this thesis, we study the problem of learning semantically meaningful and predictive representations of text in two distinct phases: • In the first phase, Part I, we investigate the use of LVM-based topic models for the specific task of event detection in Twitter. In this situation, the use of contextual information to pool tweets together comes naturally. Thus, we first extend an existing clustering algorithm for event detection to use the topics learned from pooled tweets. Then, we propose a probability model that integrates topic modelling and clustering to enable the flow of information between both components. • In the second phase, Part II and Part III, we challenge the use of local latent variables in LVMs, specially when the context of short messages is not available. First of all, we study the evaluation of the generalization capabilities of LVMs like PFA (Poisson Factor Analysis) and propose unbiased estimation methods to approximate it. With the most accurate method, we compare the generalization of chordal models without latent variables to that of PFA topic models in short and regular text collections. In summary, we demonstrate that by integrating clustering and topic modelling, the performance of event detection techniques in Twitter is improved due to the interaction between both components. Moreover, we develop several unbiased likelihood estimation methods for assessing the generalization of PFA and we empirically validate their accuracy in different document collections. Finally, we show that we can learn chordal models without latent variables in text through Chordalysis, and that they can be a competitive alternative to classical topic models, specially in short text.Els avenços tecnològics han canviat radicalment la forma que ens comuniquem. Avui en dia, la comunicació és ubiqua, la qual cosa fomenta l’ús de informació fàcil de crear, difondre i consumir. Com a resultat, hem experimentat l’escurçament dels missatges de text en diferents medis de comunicació, des del correu electrònic, a la missatgeria instantània, al microblogging. A més de la ubiqüitat, la naturalesa accelerada d’aquests medis ha promogut el seu ús per tasques fins ara inimaginables. Per exemple, el relat d’esdeveniments era clàssicament dut a terme per periodistes a peu de carrer, però, en l’actualitat, el successos més interessants es publiquen directament en xarxes socials com Twitter a través de missatges curts. Conseqüentment, l’explotació de la informació temàtica del text curt ha atret l'interès tant de la recerca com de la indústria. Els models temàtics (o topic models) són un tipus de models de probabilitat que tradicionalment s’han utilitzat per explotar la informació temàtica en documents de text. Els models més populars pertanyen al subgrup de models amb variables latents, els quals incorporen varies variables a nivell de corpus, document i paraula amb la finalitat de descriure el contingut temàtic a cada nivell. Tanmateix, aquests models tenen dificultats per aprendre la semàntica en documents curts degut a la manca de coocurrència en les paraules d’un mateix document, la qual cosa impedeix una correcta estimació de les variables locals. Per tal de solucionar aquesta limitació, l’agregació de missatges segons el context i l’ús d’estratègies jeràrquiques Bayesianes són essencials per millorar la qualitat dels temes apresos. En aquesta tesi, estudiem en dos fases el problema d’aprenentatge d’estructures semàntiques i predictives en documents de text: En la primera fase, Part I, investiguem l’ús de models temàtics amb variables latents per la detecció d’esdeveniments a Twitter. En aquest escenari, l’ús del context per agregar tweets sorgeix de forma natural. Per això, primer estenem un algorisme de clustering per detectar esdeveniments a partir dels temes apresos en els tweets agregats. I seguidament, proposem un nou model de probabilitat que integra el model temàtic i el de clustering per tal que la informació flueixi entre ambdós components. En la segona fase, Part II i Part III, qüestionem l’ús de variables latents locals en models per a text curt sense context. Primer de tot, estudiem com avaluar la capacitat de generalització d’un model amb variables latents com el PFA (Poisson Factor Analysis) a través del càlcul de la likelihood. Atès que aquest càlcul és computacionalment intractable, proposem diferents mètodes d estimació. Amb el mètode més acurat, comparem la generalització de models chordals sense variables latents amb la del models PFA, tant en text curt com estàndard. En resum, demostrem que integrant clustering i models temàtics, el rendiment de les tècniques de detecció d’esdeveniments a Twitter millora degut a la interacció entre ambdós components. A més a més, desenvolupem diferents mètodes d’estimació per avaluar la capacitat generalizadora dels models PFA i validem empíricament la seva exactitud en diverses col·leccions de text. Finalment, mostrem que podem aprendre models chordals sense variables latents en text a través de Chordalysis i que aquests models poden ser una bona alternativa als models temàtics clàssics, especialment en text curt.Postprint (published version

L'antiga Farmàcia Vilardell de Barcelona. Notes històriques

En aquest treball s’analitza els orígens de la Farmàcia Vilardell de Barcelona, un dels establiments farmacèutics modernistes més notables de la Ciutat Comtal. Fundada pel farmacèutic francès Juli Trenard, fou adquirida per la família Vilardell l’any 1928, iniciant una nissaga de farmacèutics que l’han regentat fins els nostres dies. Es fa esment de les diverses generacions de professionals que hi van exercir fins el moment del seu tancament definitiu l’any 2004, després de més d’una centúria al servei dels barcelonins

Work engagement, job satisfaction, general health, and mental health among faculty members at an Argentinian public university

La docencia universitaria se ha convertido en una de las ocupaciones más estresantes, lo que incrementa la relevancia de examinar las condiciones psicológicas y organizacionales que favorecen la conservación de la salud física y mental de los académicos. Este artículo se propone, por un lado, examinar el work engagement, la satisfacción laboral, la salud física y la salud mental en una muestra de académicos argentinos y, por el otro, analizar la influencia de un conjunto de variables sociodemográficas (i.e. edad, género y nivel educativo), otras relativas a las condiciones de empleo (i.e. jerarquía del cargo, dedicación horaria y estabilidad contractual), el work engagement y la satisfacción laboral de los participantes sobre sus niveles reportados de salud física y mental. Para ello, se administra un cuestionario virtual a una muestra no probabilística constituida por 86 académicos de una facultad de una universidad pública argentina. Los resultados revelan que más de un tercio de los participantes reportan haberse sentido estresados, cansados y/o agotados mentalmente, o haber padecido dolores de espalda, problemas de sueño, dolores de cabeza, tensión ocular y/o fatiga durante un período de treinta días. De esta investigación también surge que aquellos docentes más satisfechos con sus trabajos y/o más engaged tienden a gozar de una mejor salud física y mental. Se concluye que las universidades argentinas deberían mejorar las condiciones de trabajo que ofrecen a sus académicos con el propósito de incrementar su work engagement y su satisfacción laboral, dado que ambos factores son relevantes para la conservación de su salud física y mental.Being an academic faculty member has become a highly stressful occupation, making it necessary to examine the relevance of psychological and organizational conditions associated with higher levels of physical and mental health in the university setting. We aimed, on the one hand, to measure the levels of work engagement, job satisfaction, physical health, and mental health in a sample of Argentinian scholars and, on the other hand, to analyze the influence of a set of demographics (i.e. age, gender, and level of education), terms of employment (i.e. hierarchy, hours worked per week, and tenure), work engagement, and job satisfaction on reported levels of physical and mental health. We administered an online survey that included six self-report scales to a sample of 86 academics from an Argentinian university. Over one third of participants reported feeling stressed out, tired, emotionally exhausted, experienced backaches, having sleep difficulties, headaches, eyestrain, and/or fatigue in the previous 30-day period. Results also revealed that those academics who were highly engaged or felt highly satisfied with their jobs tended to report better physical and mental health. Argentinian universities should improve their working conditions in order to boost their faculty members’work engagement and job satisfaction, as both phenomena appear to be vital for maintaining adequate levels of physical and mental health in the workplace.Fil: Pujol Cols, Lucas Joan. Universidad Nacional de Mar del Plata. Facultad de Ciencias Económicas y Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentin

Scaling DBSCAN-like algorithms for event detection systems in Twitter

The increasing use of mobile social networks has lately transformed news media. Real-world events are nowadays reported in social networks much faster than in traditional channels. As a result, the autonomous detection of events from networks like Twitter has gained lot of interest in both research and media groups. DBSCAN-like algorithms constitute a well-known clustering approach to retrospective event detection. However, scaling such algorithms to geographically large regions and temporarily long periods present two major shortcomings. First, detecting real-world events from the vast amount of tweets cannot be performed anymore in a single machine. Second, the tweeting activity varies a lot within these broad space-time regions limiting the use of global parameters. Against this background, we propose to scale DBSCAN-like event detection techniques by parallelizing and distributing them through a novel density-aware MapReduce scheme. The proposed scheme partitions tweet data as per its spatial and temporal features and tailors local DBSCAN parameters to local tweet densities. We implement the scheme in Apache Spark and evaluate its performance in a dataset composed of geo-located tweets in the Iberian peninsula during the course of several football matches. The results pointed out to the benefits of our proposal against other state-of-the-art techniques in terms of speed-up and detection accuracy.Peer ReviewedPostprint (author's final draft