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This article seeks to shed new light on Pármeno’s dramatic downfall and his subsequent failure to assert his masculinity in a dignified manner by analysing in depth the principle relationships which he maintains or breaks with the other characters. The central argument of this article focuses on the changes in Pármeno’s dialogic style(s), which are interpreted as direct consequences of the internal conflict brought about by his decision to substitute reason with emotion, in an attempt to win the approval of his male contemporaries via the shared experience of courtship.

The genetic diversity in local Tunisian watermelon (Citrullus lanatus) was studied using RAPD markers. Eight watermelon cultivars originating from south of Tunisia and belong to four populations were analysed and compared to two commercial varieties (Giza and Sugar-baby) widely produced in this area. Five of nine RAPD primers generated a total of 86 reproducible bands, 85 of which were polymorphic (98.4%). Cluster analysis of the accessions considered in this study employing RAPD data indicated that commercial varieties are significantly different of all the local cultivars. The relationships among the local cultivars (four populations) showed that Medenine population and the most genotypes of Benguerdane population were grouped together and significantly different from Kebili population. The AMOVA showed significant differentiation between populations (27%). In addition, the data showed clusters according to some fruit characteristics such as fruit shape and fruit weight. This proved that RAPD markers are useful for germplasm discrimination as well as for investigation of patterns of variation in watermelon.

The genetic diversity in local Tunisian watermelon (Citrullus lanatus) was studied using RAPD markers. Eight watermelon cultivars originating from south of Tunisia and belong to four populations were analysed and compared to two commercial varieties (Giza and Sugar-baby) widely produced in this area. Five of nine RAPD primers generated a total of 86 reproducible bands, 85 of which were polymorphic (98.4%). Cluster analysis of the accessions considered in this study employing RAPD data indicated that commercial varieties are significantly different of all the local cultivars. The relationships among the local cultivars (four populations) showed that Medenine population and the most genotypes of Benguerdane population were grouped together and significantly different from Kebili population. The AMOVA showed significant differentiation between populations (27%). In addition, the data showed clusters according to some fruit characteristics such as fruit shape and fruit weight. This proved that RAPD markers are useful for germplasm discrimination as well as for investigation of patterns of variation in watermelon.

It has long been recognised that extreme coastal flooding can arise from the joint occurrence of extreme waves, winds and sea levels. The standard simplified joint probability approach used in England and Wales can result in an underestimation of flood risk unless correction factors are applied. This paper describes the application of a state-of-the-art multivariate extreme value model to offshore winds, waves and sea levels around the coast of England. The methodology overcomes the limitations of the traditional method. The output of the new statistical analysis is a Monte-Carlo (MC) simulation comprising many thousands of offshore extreme events and it is necessary to translate all of these events into overtopping rates for use as input to flood risk assessments. It is computationally impractical to transform all of these MC events from the offshore to the nearshore. Computationally efficient statistical emulators of the SWAN wave transformation model have therefore been constructed. The emulators translate the thousands of MC events offshore. Whilst the methodology has been applied for national flood risk assessment, it has the potential to be implemented for wider use, including climate change impact assessment, nearshore wave climates for detailed local assessments and coastal flood forecasting.

Flood risk analysis often involves the integration of multivariate probability distributions over a domain defined by a consequence function. Often, solutions of this risk integral involves Monte-Carlo sampling techniques, whereby 1000’s of potential flood events are generated. It is necessary to evaluate the consequence of flooding for each sampled event. A significant computational time is required in running flood related physical process models, making it computationally impractical to evaluate flood risk using this approach. To overcome the computational challenges, this paper focusses on the Gaussian Process Emulator (GPE) meta-modelling approach. Traditionally, a “look-up table” method is used when a large number of simulations from a numerical model are required. This approach typically involves simulating conditions defined across a regular matrix, and then linearly interpolating intermediate conditions. In this paper we compare a traditional “look-up table” approach to the GPE and analyse their performance in approximating SWAN wave transformation model. In both cases, selecting an appropriate training design set is important and is taken into consideration in the analysis. The analysis shows that the GPE approach requires significantly fewer SWAN runs to obtain similar (or better) accuracies, enabling a substantial reduction in computation time, hence aiding the practicality of Monte-Carlo sampling techniques in advanced flood risk modelling.