A SECOND LANGUAGE TASK BASED LEARNING EXPERIENCE FOR CHILDREN

The study was designed to investigate the implementation phase of a Task Based Learning approach for teaching English as a Second Language to an intact sample of 25 children, six to seven years of age and whose first language in Asuncion, Paraguay was Spanish. Most of the theory behind Task Based Learning (TBL) was accomplished with adolescents and adults. The present researcher found that many of the tasks researched were either not developmentally appropriate or likely needed significant adaptations. Therefore, tasks were developed and adapted to be likely appropriate for children 6 to7 years of age. Findings revealed that adaptations to tasks were crucial to achieving maximum success. This was true for pre-planning and for the implementation phase. More time, scenarios, games, pictures and active learning experiences were found to be needed than originally thought by the present researcher. To the surprise of two teachers and present researcher, most children enjoyed repetition and recycling of new vocabulary. The highest level of motivation emerged when children interviewed high school seniors in their school. Emerging English learners made increased progress when paired with high or moderate level learners. Tasks that were most effective were the interview and retelling of a story. Abstract tasks required increased adaptations, increased teacher intervention in the first language and time to achieve even moderate success. Children responded with curiosity, high motivation and high achievement during the implementation phase. Additional tasks, planning and adaptations must continue to occur before and during the continued implementation of TBL at Colegio del Sol School. Task Based Learning holds promise for young children; however, there needs to be additional development of ideas and materials to support TBL theoretical background

Flash flood detection through a multi-stage probabilistic warning system for heavy precipitation events

The deadly combination of short to no warning lead times and the vulnerability of urbanized areas makes flash flood events extremely dangerous for the modern society. This paper contributes to flash flood early warning by proposing a multi-stage warning system for heavy precipitation events based on threshold exceedances within a probabilistic framework. It makes use of meteorological products at different resolutions, namely, numerical weather predictions (NWP), radar-NWP blending, and radar nowcasting. The system is composed by two main modules. First, a European Precipitation Index based on a simulated Climatology (EPIC) and probabilistic weather forecasts is calculated to pinpoint catchments at risk of upcoming heavy precipitation. Then, a Probabilistic Flash Flood Guidance System (PFFGS) is activated at the regional scale and uses more accurate input data to reduce the estimation uncertainty. <br></br> The system is tested for a high flow event occurred in Catalonia (Spain) in November 2008 and results from the different meteorological input data are compared and discussed. The strength of coupling the two systems is shown in its ability to detect areas potentially at risk of severe meteorological conditions and then monitoring the evolution by providing more accurate information with higher spatial-temporal resolution as the event approaches

DEVELOPMENT OF EXPERT SYSTEMS FOR THE MITIGATION OF NITROGEN POLLUTION AT FARM AND REGIONAL SCALE

Intensive agriculture and concentration of livestock activities represent critical factors in the environment, particularly in Lombardia region where nitrate vulnerable zones constitute 62% of Utilized Agricultural Area (UAA). In addition, the aquifer Po valley aquifer is one of the largest in Europe, for which it is estimated that over two-thirds of the nitrogen that reaches the surface and the subsoil is of agricultural origin. The problem of reduction of nitrogen losses into the environment, as leaching of nitrates into groundwater and ammonia emissions into atmosphere, can be only addressed through a critical and scientific analysis of manure entire production chain. As a consequence, the opportunity to develop software tools to analyze the current situation and the effects of possible scenarios arising from different regional policies relating to the release of nitrogen from agricultural sources. A decision support system (DSS) has been developed, to run simulation both at farm and territorial scale. The farm simulator is aimed at farmers and allows to analyze the management and technological alternatives available for the entire supply chain from animal feed to the distribution in the field for maximum utilization of the livestock waste. It is a free software downloaded from the website of the Lombardia Region, which collects the data of the structure and management of farm in the regional database; the speed of execution and the interface easily understandable make it "user friendly". The territorial simulator, available to regional authorities, works on a regional scale it is completely resident on the web and allows the evaluation of the impact of any regulatory measures and incentives simulated from the agronomic, environmental and economic point of view. Below, has been analyzed the regional database and have been drawn up reference tables, were also collected, reviewed and made consistent models already existing and validated for the various phases of manure production. Were then assessed and defined the agronomic, plant and economic alternatives. The basic unit of simulation is made by the single cadastral parcel; in case of a territorial scale simulation model is applied to all the parcels of the sample selected. The DSS provides data both in detail and in the form of synthetic indicators. In the first months of activity the DSS at farm scale, introduced in November 2011, has been used by 200 users of which around 65% are professional agronomists and farmers of large companies; therefore it represents an important opportunity for the Lombardia agriculture to combine environmental protection with economic and technical sustainability

A distributed approach for parameter estimation in Systems Biology models

Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology Mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models

Understanding stochastic perturbation theory: toy models and statistical analysis

The numerical stochastic perturbation method based on Parisi-Wu quantisationis applied to a suite of simple models to test its validity at high orders.Large deviations from normal distribution for the basic estimators aresystematically found in all cases (``Pepe effect''). As a consequence oneshould be very careful in estimating statistical errors. We present someresults obtained on Weingarten's ``pathological'' model where reliable resultscan be obtained by an application of the bootstrap method. We also present someevidence that in the far less trivial application to Lattice Gauge Theory asimilar problem should not arise at moderately high loops (up toO(\alpha^{10}))

The transformed-stationary approach: A generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/ (Mentaschi et al., 2016)