Associated fauna of the fan shell <em>Pinna nobilis</em> (Mollusca: Bivalvia) in the northern and eastern Tunisian coasts

Epifaunal communities associated with the fan shell Pinna nobilis Linnaeus, 1758 along the Tunisian coastline were investigated. Both univariate and multivariate analysis were done at different spatial scales within five populations located at different localities, three from northern and two from eastern Tunisia. The size of Pinna did not appear as the main factor affecting the structure of the associated biota, which seemed to be more influenced by (a) marine-lagoon and (b) biogeographic gradients. Patterns of similarity of sessile sclerobionts and motile epifauna were clearly different. The former assemblage best replied to lagoon-sea gradient and to locality, with three real clusters at 40%, whereas the latter assemblage scattered widely in a non-metrical MDS plane, with two real clusters only at 20% similarity. The spatial turnover of motile species was ten times higher than that of sessile species at a small spatial scale, being less affected by Pinna size, and three times higher though invariant at a large geographic scale. On the other hand, β-diversity of sessile species appeared to be more influenced by latitudinal (climatic) gradient at a large scale, being higher in the northern than in the eastern communities. Analysis of taxonomic (dis)similarity of the whole community detected these two sources of environmental (lagoon-sea gradient) and biogeographic (lati-longitudinal gradient) variation, although each phylum showed its peculiar pattern. In terms of Dajoz's constancy index the majority of associated communities were dominated by rare species, and within the majority of epifaunal assemblages, the most abundant sessile epizoobiont was a bivalve mollusc. The sessile epifauna was dominated by active filterers, which led to a possible existence of trophic competition between the host and the sedentary epizoites, since both basibionts and sclerobionts occupy the same trophic niche. The fan shell played an important ecological role, providing new hard substrate to colonise, increasing the spatial heterogeneity for the surrounding soft-bottom communities, and contributing to the overall increase of the local biotope complexity level

Groundwater quality for irrigation in an arid region-application of fuzzy logic techniques

Groundwater is the main source to answer the irrigation supply in several arid and semi-arid areas. In the present work, groundwater quality for irrigation purposes in the arid region of Menzel Habib (Tunisia) for thirty-six groundwater samples is assessed considering the application of different conventional water quality indicators, particularly, electrical conductivity (EC), sodium absorption ratio (SAR), soluble sodium percentage (SSP), magnesium adsorption ratio (MAR), Kelly ratio (KR), and permeability index (PI). The results obtained indicate a variability for EC: 3.06 to 14.98 mS.cm-1; SAR: 4.08 to 19.30; SSP: 35.78 to 71.53%; MAR: 34.19 to 56.01; PI: 38.47 to 72.74; and KR: 0.56 to 2.47. These results suggest that groundwater from Menzel Habib aquifer system is classified between excellent to unsuitable according to the applied water quality indices. Furthermore, the groundwater samples are also plotted in the Richards diagram classification system, based on the relation between SAR and EC, suggesting that almost groundwater samples present a harmful quality. Moreover, fuzzy logic model has been proposed and created to assess groundwater quality for irrigation. The membership functions are constructed for six significant parameters such as EC, SAR, SSP, MAR, KR, and PI and the rules are, then, fired to get a simple Fuzzy Irrigation Water Quality Index (FIWQI). The obtained groundwater quality results suggest that 3% of the samples from Menzel Habib region are considered as "good" for irrigation, 3% are classified as "good to permissible", 33% with a "permissible" quality, 36% "permissible to unsuitable", while 25% of groundwater present an "unsuitable" quality. Thus, the use of fuzzy logic techniques has more reliable and robust results by overcoming the uncertainties in the decision-making attributed to the conventional methods by the creation of new classes (excellent to good, good to permissible, and permissible to unsuitable) in addition to the classes proposed by Richards diagram classification (excellent, good, permissible, and unsuitable) to assess the groundwater quality suitability for irrigation purposes.This research was developed under the FCT–Fundação para a Ciência e a Tecnologia, I.P. program, through the project’s reference UIDB/04683/2020 and UIDP/04683/2020

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

International audienc

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.</jats:p

Neural Network-based System for Automatic Passport Stamp Classification

International audienceThe international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases